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1
Peng, Ye, Houpu Li, Wenwen Zhang, Junhui Zhu, Lei Liu, and Guojun Zhai. "Underwater Sonar Image Classification with Image Disentanglement Reconstruction and Zero-Shot Learning." Remote Sensing 17, no. 1 (2025): 134. https://doi.org/10.3390/rs17010134.
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Sonar is a valuable tool for ocean exploration since it can obtain a wealth of data. With the development of intelligent technology, deep learning has brought new vitality to underwater sonar image classification. However, due to the difficulty and high cost of acquiring underwater sonar images, we have to consider the extreme case when there are no available sonar data of a specific category, and how to improve the prediction ability of intelligent classification models for unseen sonar data. In this work, we design an underwater sonar image classification method based on Image Disentanglement Reconstruction and Zero-Shot Learning (IDR-ZSL). Initially, an image disentanglement reconstruction (IDR) network is proposed for generating pseudo-sonar samples. The IDR consists of two encoders, a decoder, and three discriminators. The first encoder is responsible for extracting the structure vectors of the optical images and the texture vectors of the sonar images; the decoder is in charge of combining the above vectors to generate the pseudo-sonar images; and the second encoder is in charge of disentangling the pseudo-sonar images. Furthermore, three discriminators are incorporated to determine the realness and texture quality of the reconstructed image and feedback to the decoder. Subsequently, the underwater sonar image classification model performs zero-shot learning based on the generated pseudo-sonar images. Experimental results show that IDR-ZSL can generate high-quality pseudo-sonar images, and improve the prediction accuracy of the zero-shot classifier on unseen classes of sonar images.
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Kim, Hong-Gi, Jungmin Seo, and Soo Mee Kim. "Underwater Optical-Sonar Image Fusion Systems." Sensors 22, no. 21 (2022): 8445. http://dx.doi.org/10.3390/s22218445.
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Unmanned underwater operations using remotely operated vehicles or unmanned surface vehicles are increasing in recent times, and this guarantees human safety and work efficiency. Optical cameras and multi-beam sonars are generally used as imaging sensors in underwater environments. However, the obtained underwater images are difficult to understand intuitively, owing to noise and distortion. In this study, we developed an optical and sonar image fusion system that integrates the color and distance information from two different images. The enhanced optical and sonar images were fused using calibrated transformation matrices, and the underwater image quality measure (UIQM) and underwater color image quality evaluation (UCIQE) were used as metrics to evaluate the performance of the proposed system. Compared with the original underwater image, image fusion increased the mean UIQM and UCIQE by 94% and 27%, respectively. The contrast-to-noise ratio was increased six times after applying the median filter and gamma correction. The fused image in sonar image coordinates showed qualitatively good spatial agreement and the average IoU was 75% between the optical and sonar pixels in the fused images. The optical-sonar fusion system will help to visualize and understand well underwater situations with color and distance information for unmanned works.
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Ye, Xiufen, Haibo Yang, Chuanlong Li, Yunpeng Jia, and Peng Li. "A Gray Scale Correction Method for Side-Scan Sonar Images Based on Retinex." Remote Sensing 11, no. 11 (2019): 1281. http://dx.doi.org/10.3390/rs11111281.
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When side-scan sonars collect data, sonar energy attenuation, the residual of time varying gain, beam patterns, angular responses, and sonar altitude variations occur, which lead to an uneven gray level in side-scan sonar images. Therefore, gray scale correction is needed before further processing of side-scan sonar images. In this paper, we introduce the causes of gray distortion in side-scan sonar images and the commonly used optical and side-scan sonar gray scale correction methods. As existing methods cannot effectively correct distortion, we propose a simple, yet effective gray scale correction method for side-scan sonar images based on Retinex given the characteristics of side-scan sonar images. Firstly, we smooth the original image and add a constant as an illumination map. Then, we divide the original image by the illumination map to produce the reflection map. Finally, we perform element-wise multiplication between the reflection map and a constant coefficient to produce the final enhanced image. Two different schemes are used to implement our algorithm. For gray scale correction of side-scan sonar images, the proposed method is more effective than the latest similar methods based on the Retinex theory, and the proposed method is faster. Experiments prove the validity of the proposed method.
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Xi, Jier, Xiufen Ye, and Chuanlong Li. "Sonar Image Target Detection Based on Style Transfer Learning and Random Shape of Noise under Zero Shot Target." Remote Sensing 14, no. 24 (2022): 6260. http://dx.doi.org/10.3390/rs14246260.
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With the development of sonar technology, sonar images have been widely used to detect targets. However, there are many challenges for sonar images in terms of object detection. For example, the detectable targets in the sonar data are more sparse than those in optical images, the real underwater scanning experiment is complicated, and the sonar image styles produced by different types of sonar equipment due to their different characteristics are inconsistent, which makes it difficult to use them for sonar object detection and recognition algorithms. In order to solve these problems, we propose a novel sonar image object-detection method based on style learning and random noise with various shapes. Sonar style target sample images are generated through style transfer, which enhances insufficient sonar objects image. By introducing various noise shapes, which included points, lines, and rectangles, the problems of mud and sand obstruction and a mutilated target in the real environment are solved, and the single poses of the sonar image target is improved by fusing multiple poses of optical image target. In the meantime, a method of feature enhancement is proposed to solve the issue of missing key features when using style transfer on optical images directly. The experimental results show that our method achieves better precision.
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Peng, Lei. "Adaptive De-Noising Approach for Underwater Side Scan Sonar Image." Applied Mechanics and Materials 373-375 (August 2013): 509–12. http://dx.doi.org/10.4028/www.scientific.net/amm.373-375.509.
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It is difficult to detect the edges of objects in side scan sonar images due to the complex background, bad contrast and deteriorate edges. Therefore, it is important to remove noise from side scan sonar images. The traditional de-noising methods for optical images may not work well on the sonar image. In this paper, an adaptive de-noising approach is used. The side scan sonar image is first filtered using mean filter to remove the rough noise, then a weighted function is generated using spatial distance filter and intensity distance filter. The parameters are adaptive according to the sonar image. The experimental results indicate that it is an effective de-noising method for underwater sonar image.
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Jiao, Shengxi, Chunyu Zhao, and Ye Xin. "Research on Convolutional Neural Network Model for Sonar IMAGE Segmentation." MATEC Web of Conferences 220 (2018): 10004. http://dx.doi.org/10.1051/matecconf/201822010004.
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The speckle noise of sonar images affects the human interpretation and automatic recognition of images seriously. It is important and difficult to realize the precision segmentation of sonar image with speckle noise in the field of image processing. Full convolution neural network (FCN) has the advantage of accepting arbitrary size image and preserving spatial information of original input image. In this paper, the image features are obtained by autonomic learning of convolutional neural network, the original learning rules based on the mean square error loss function is improved. Taking the pixel as the processing unit, the segmentation method based on FCN model with relative loss function(FCN-RLF) for small submarine sonar image is proposed, sonar image pixel-level segmentation is achievied. Experimental results show that the improved algorithm can improve the segmentation accuracy and keep the edge and detail of sonar image better. The proposed model has better ability to reject sonar image speckle noise.
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Xi, Jier, and Xiufen Ye. "Sonar Image Target Detection Based on Simulated Stain-like Noise and Shadow Enhancement in Optical Images under Zero-Shot Learning." Journal of Marine Science and Engineering 12, no. 2 (2024): 352. http://dx.doi.org/10.3390/jmse12020352.
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There are many challenges in using side-scan sonar (SSS) images to detect objects. The challenge of object detection and recognition in sonar data is greater than in optical images due to the sparsity of detectable targets. The complexity of real-world underwater scanning presents additional difficulties, as different angles produce sonar images of varying characteristics. This heterogeneity makes it difficult for algorithms to accurately identify and detect sonar objects. To solve these problems, this paper presents a novel method for sonar image target detection based on a transformer and YOLOv7. Thus, two data augmentation techniques are introduced to improve the performance of the detection system. The first technique applies stain-like noise to the training optical image data to simulate the real sonar image environment. The second technique adds multiple shadows to the optical image and 3D data targets to represent the direction of the target in the sonar image. The proposed method is evaluated on a public sonar image dataset, and the experimental results demonstrate that the proposed method outperforms the state-of-the-art methods in terms of accuracy and speed. The experimental results show that our method achieves better precision.
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Nguyen, Huu-Thu, Eon-Ho Lee, and Sejin Lee. "Study on the Classification Performance of Underwater Sonar Image Classification Based on Convolutional Neural Networks for Detecting a Submerged Human Body." Sensors 20, no. 1 (2019): 94. http://dx.doi.org/10.3390/s20010094.
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Auto-detecting a submerged human body underwater is very challenging with the absolute necessity to a diver or a submersible. For the vision sensor, the water turbidity and limited light condition make it difficult to take clear images. For this reason, sonar sensors are mainly utilized in water. However, even though a sonar sensor can give a plausible underwater image within this limitation, the sonar image’s quality varies greatly depending on the background of the target. The readability of the sonar image is very different according to the target distance from the underwater floor or the incidence angle of the sonar sensor to the floor. The target background must be very considerable because it causes scattered and polarization noise in the sonar image. To successfully classify the sonar image with these noises, we adopted a Convolutional Neural Network (CNN) such as AlexNet and GoogleNet. In preparing the training data for this model, the data augmentation on scattering and polarization were implemented to improve the classification accuracy from the original sonar image. It could be practical to classify sonar images undersea even by training sonar images only from the simple testbed experiments. Experimental validation was performed using three different datasets of underwater sonar images from a submerged body of a dummy, resulting in a final average classification accuracy of 91.6% using GoogleNet.
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Tan, Xinyu, Chengjie Wang, Tianshun Chen, and Liran Shen. "Forward looking sonar image segmentation based on empirical mode decomposition." Journal of Physics: Conference Series 2303, no. 1 (2022): 012063. http://dx.doi.org/10.1088/1742-6596/2303/1/012063.
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Abstract Due to the low imaging quality and serious noise pollution of forward-looking sonar images, traditional image segmentation methods based on edge information or statistical information are difficult to obtain high precision and robust segmentation results. Therefore, it is very complicated to segment forward-looking sonar images. Based on the analysis of the characteristics of forward-looking sonar, a new image segmentation method for forward-looking sonar is proposed.Firstly, 2d maximum entropy segmentation principle combined with chicken flock optimization algorithm is used to remove the background of the forward-looking sonar image.Then, based on 2d empirical mode decomposition, appropriate eigenmode functions are selected to denoise and enhance the forward-looking sonar image.Finally, the reconstructed forward-looking sonar image is segmented by enhanced fuzzy C-means clustering, and the segmented forward-looking sonar image result is obtained. This method can not only suppress noise interference effectively, but also protect the details of image edge for segmentation.
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Dong, M., H. Qiu, H. Wang, P. Zhi, and Z. Xu. "SONAR IMAGE RECOGNITION BASED ON MACHINE LEARNING FRAMEWORK." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVI-3/W1-2022 (April 22, 2022): 45–51. http://dx.doi.org/10.5194/isprs-archives-xlvi-3-w1-2022-45-2022.
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Abstract. In order to improve the robustness and generalization ability of model recognition, sonar images are enhanced by preprocessing such as conversion coordinates, interpolation, denoising and enhancement, and the transfer learning method under the Caffe framework of MATLAB as an interface is used respectively (mainly composed of 8 layers of network structure, including 5 convolutional layers and 3 full chain layers) And the transfer learning method under the Python deep learning framework Inception-Resnet-v2 model for sonar image training and recognition. First of all, part of the sonar image dataset (derived from the 2021 National Robot Underwater Competition online competition data), using MATLAB as the interface Caffe framework, the sonar image is trained to obtain a training model, and then through parameter adjustment, the convolutional neural network model of sonar image automatic recognition is obtained, and the transfer learning method can use less sonar image data to solve the problem of insufficient sonar image data, and then make the training achieve a higher recognition rate in a shorter time. When the training data is randomly sampled for testing, the sonar data recognition model based on the Caffe framework is quickly and fully recognized, and the recognition rate can reach 92% when the test sample does not participate in the training of sonar image data; The transfer learning method under the Inception-Resnet-v2 model of python deep learning framework is used to train recognition on sonar images, and the recognition rate reaches about 97%. Using the two models in this paper, it is feasible to identify sonar images with high recognition rate, which is much higher than traditional recognition methods such as SVM classifiers, and the two sonar image data recognition models based on deep learning have better recognition ability and generalization ability.
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Jin, Leilei, Hong LIANG, and Changsheng Yang. "Sonar image recognition of underwater target based on convolutional neural network." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 39, no. 2 (2021): 285–91. http://dx.doi.org/10.1051/jnwpu/20213920285.
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Underwater target recognition is one core technology of underwater unmanned detection. To improve the accuracy of underwater automatic target recognition, a sonar image recognition method based on convolutional neural network was proposed and the underwater target recognition model was established according to the characteristics of sonar images. Firstly, the sonar image was segmented and clipped with a saliency detection method to reduce the dimension of input data, and to reduce the interference of image background to the feature extraction process. Secondly, by using stacked convolutional layers and pooling layers, the high-level semantic information of the target was automatically learned from the input sonar image, to avoid damaging the effective information caused by extracting image features manually. Finally, the spatial pyramid pooling method was used to extract the multi-scale information from the sonar feature maps, which was to make up for the lack of detailed information of sonar images and solve the problem caused by the inconsistent size of input images. On the collected sonar image dataset, the experimental results show that the target recognition accuracy of the present method can recognize underwater targets more accurately and efficiently than the conventional convolutional neural networks.
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Sun, Chao, Li Wang, Nan Wang, and Shaohua Jin. "Image Recognition Technology in Texture Identification of Marine Sediment Sonar Image." Complexity 2021 (March 13, 2021): 1–8. http://dx.doi.org/10.1155/2021/6646187.
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Through the recognition of ocean sediment sonar images, the texture in the image can be classified, which provides an important basis for the classification of ocean sediment. Aiming at the problems of low efficiency, waste of human resources, and low accuracy in the traditional manual side-scan sonar image discrimination, this paper studies the application of image recognition technology in sonar image substrate texture discrimination, which is popular in many fields. At the same time, considering the scale complexity, diversity, multisources, and small sample characteristics of the marine sediment sonar image texture, the transfer learning is introduced into the image recognition, and the K-means clustering algorithm is used to reset the prior frame parameters to improve the speed and accuracy of image recognition. Through the experimental comparison between the original model and the new model based on transfer learning, the AP (average precision) value of the yolov3 model based on transfer learning can reach 84.39%, which is 0.97% higher than that of the original model, with considerable accuracy and room for improvement; it takes less than 0.2 seconds. This shows the applicability and development of image recognition technology in texture discrimination of bottom sonar images.
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Zhou, Xin, Kun Tian, Zihan Zhou, Bo Ning, and Yanhao Wang. "SID-TGAN: A Transformer-Based Generative Adversarial Network for Sonar Image Despeckling." Remote Sensing 15, no. 20 (2023): 5072. http://dx.doi.org/10.3390/rs15205072.
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Sonar images are inherently affected by speckle noise, which degrades image quality and hinders image exploitation. Despeckling is an important pre-processing task that aims to remove such noise so as to improve the accuracy of analysis tasks on sonar images. In this paper, we propose a novel transformer-based generative adversarial network named SID-TGAN for sonar image despeckling. In the SID-TGAN framework, transformer and convolutional blocks are used to extract global and local features, which are further integrated into the generator and discriminator networks for feature fusion and enhancement. By leveraging adversarial training, SID-TGAN learns more comprehensive representations of sonar images and shows outstanding performance in speckle denoising. Meanwhile, SID-TGAN introduces a new adversarial loss function that combines image content, local texture style, and global similarity to reduce image distortion and information loss during training. Finally, we compare SID-TGAN with state-of-the-art despeckling methods on one image dataset with synthetic optical noise and four real sonar image datasets. The results show that it achieves significantly better despeckling performance than existing methods on all five datasets.
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Li, Chuanlong, Xiufen Ye, Jier Xi, and Yunpeng Jia. "A Texture Feature Removal Network for Sonar Image Classification and Detection." Remote Sensing 15, no. 3 (2023): 616. http://dx.doi.org/10.3390/rs15030616.
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Deep neural network (DNN) was applied in sonar image target recognition tasks, but it is very difficult to obtain enough sonar images that contain a target; as a result, the direct use of a small amount of data to train a DNN will cause overfitting and other problems. Transfer learning is the most effective way to address such scenarios. However, there is a large domain gap between optical images and sonar images, and common transfer learning methods may not be able to effectively handle it. In this paper, we propose a transfer learning method for sonar image classification and object detection called the texture feature removal network. We regard the texture features of an image as domain-specific features, and we narrow the domain gap by discarding the domain-specific features, and hence, make it easier to complete knowledge transfer. Our method can be easily embedded into other transfer learning methods, which makes it easier to apply to different application scenarios. Experimental results show that our method is effective in side-scan sonar image classification tasks and forward-looking sonar image detection tasks. For side-scan sonar image classification tasks, the classification accuracy of our method is enhanced by 4.5% in a supervised learning experiment, and for forward-looking sonar detection tasks, the average precision (AP) is also significantly improved.
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Zhao, Xinyang, Shaohua Jin, Gang Bian, Yang Cui, Junsen Wang, and Bo Zhou. "A Curvelet-Transform-Based Image Fusion Method Incorporating Side-Scan Sonar Image Features." Journal of Marine Science and Engineering 11, no. 7 (2023): 1291. http://dx.doi.org/10.3390/jmse11071291.
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Current methods of fusing side-scan sonar images fail to tackle the issues of shadow removal, preservation of information from adjacent strip images, and maintenance of image clarity and contrast. To address these deficiencies, a novel curvelet-transform-based approach that integrates the complementary attribute of details from side-scan sonar strip images is proposed. By capitalizing on the multiple scales and orientations of the curvelet transform and its intricate hierarchical nature, myriad fusion rules were applied at the corresponding frequency levels, enabling a more-tailored image fusion technique for side-scan sonar imagery. The experimental results validated the effectiveness of this method in preserving valuable information from side-scan sonar images, reducing the presence of shadows and ensuring both clarity and contrast in the fused images. By meeting the aforementioned challenges encountered in existing methodologies, this approach demonstrated great practical significance.
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Tamsett, Duncan, Jason McIlvenny, James Baxter, Paulo Gois, and Benjamin Williamson. "On the Information Advantage of Sidescan Sonar Three-Frequency Colour over Greyscale Imagery." Journal of Marine Science and Engineering 7, no. 8 (2019): 276. http://dx.doi.org/10.3390/jmse7080276.
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A prototype three-frequency (114, 256, and 410 kHz) colour sidescan sonar system, built by Kongsberg Underwater Mapping Ltd. (Great Yarmouth, UK), was previously described, and preliminary results presented, in Tamsett, McIlvenny, and Watts. The prototype system has subsequently been modified, and in 2017, new data were acquired in a resurvey of the Inner Sound of the Pentland Firth, North Scotland. An image texture characterisation and image classification exercise demonstrates considerably greater discrimination between different seabed classes in a three-frequency colour sonar image of the seabed, than in a multi-frequency colour image reduced to greyscale display, or in a single-frequency greyscale image, with readily twice the number of classes of seabed discriminated between, in the colour image. The information advantage of colour acoustic imagery over greyscale acoustic imagery is analogous to the information advantage of colour television images over black-and-white television images. A three-frequency colour sonar image contains a theoretical maximum of a factor of 3 times the information in a corresponding greyscale image, for independent seabed responses at the three frequencies. Estimates of the average information per pixel (information entropy) in the colour image, and in corresponding greyscale images, reveal an actual information advantage of colour sonar imagery over greyscale, to be in practice approximately a factor of 2.5, empirically confirming the greater information based utility of three-frequency colour sonar over greyscale sonar. Reference: Tamsett, D.; McIlvenny, J.; Watts, A. J. Mar. Sci. Eng. 2016, 4(26).
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Yang, Dianyu, Jingfeng Yu, Can Wang, et al. "Side-Scan Sonar Image Matching Method Based on Topology Representation." Journal of Marine Science and Engineering 12, no. 5 (2024): 782. http://dx.doi.org/10.3390/jmse12050782.
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In the realm of underwater environment detection, achieving information matching stands as a pivotal step, forming an indispensable component for collaborative detection and research in areas such as distributed mapping. Nevertheless, the progress in studying the matching of underwater side-scan sonar images has been hindered by challenges including low image quality, intricate features, and susceptibility to distortion in commonly used side-scan sonar images. This article presents a comprehensive overview of the advancements in underwater sonar image processing. Building upon the novel SchemaNet image topological structure extraction model, we introduce a feature matching model grounded in side-scan sonar images. The proposed approach employs a semantic segmentation network as a teacher model to distill the DeiT model during training, extracting the attention matrix of intermediate layer outputs. This emulates SchemaNet’s transformation method, enabling the acquisition of high-dimensional topological structure features from the image. Subsequently, utilizing a real side-scan sonar dataset and augmenting data, we formulate a matching dataset and train the model using a graph neural network. The resulting model demonstrates effective performance in side-scan sonar image matching tasks. These research findings bear significance for underwater detection and target recognition and can offer valuable insights and references for image processing in diverse domains.
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Ge, Qiang, Fengxue Ruan, Baojun Qiao, Qian Zhang, Xianyu Zuo, and Lanxue Dang. "Side-Scan Sonar Image Classification Based on Style Transfer and Pre-Trained Convolutional Neural Networks." Electronics 10, no. 15 (2021): 1823. http://dx.doi.org/10.3390/electronics10151823.
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Side-scan sonar is widely used in underwater rescue and the detection of undersea targets, such as shipwrecks, aircraft crashes, etc. Automatic object classification plays an important role in the rescue process to reduce the workload of staff and subjective errors caused by visual fatigue. However, the application of automatic object classification in side-scan sonar images is still lacking, which is due to a lack of datasets and the small number of image samples containing specific target objects. Secondly, the real data of side-scan sonar images are unbalanced. Therefore, a side-scan sonar image classification method based on synthetic data and transfer learning is proposed in this paper. In this method, optical images are used as inputs and the style transfer network is employed to simulate the side-scan sonar image to generate “simulated side-scan sonar images”; meanwhile, a convolutional neural network pre-trained on ImageNet is introduced for classification. In this paper, we experimentally demonstrate that the maximum accuracy of target classification is up to 97.32% by fine-tuning the pre-trained convolutional neural network using a training set incorporating “simulated side-scan sonar images”. The results show that the classification accuracy can be effectively improved by combining a pre-trained convolutional neural network and “similar side-scan sonar images”.
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Dai, Zezhou, Hong Liang, and Tong Duan. "Small-Sample Sonar Image Classification Based on Deep Learning." Journal of Marine Science and Engineering 10, no. 12 (2022): 1820. http://dx.doi.org/10.3390/jmse10121820.
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Deep learning is a core technology for sonar image classification. However, owing to the cost of sampling, a lack of data for sonar image classification impedes the training and deployment of classifiers. Classic deep learning models such as AlexNet, VGG, GoogleNet, and ResNet suffer from low recognition rates and overfitting. This paper proposes a novel network (ResNet-ACW) based on a residual network and a combined few-shot strategy, which is derived from generative adversarial networks (GAN) and transfer learning (TL). We establish a sonar image dataset of six-category targets, which are formed by sidescan sonar, forward-looking sonar, and three-dimensional imaging sonar. The training process of ResNet-ACW on the sonar image dataset is more stable and the classification accuracy is also improved through an asymmetric convolution and a designed network structure. We design a novel GAN (LN-PGAN) that can generate images more efficiently to enhance our dataset and fine-tune ResNet-ACW pretrained on mini-ImageNet. Our method achieves 95.93% accuracy and a 14.19% increase in the six-category target sonar image classification tasks.
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CHEN, Yule, Bo LI, Hong LIANG, and Changsheng YANG. "Research on sonar image few-shot classification based on deep learning." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 40, no. 4 (2022): 739–45. http://dx.doi.org/10.1051/jnwpu/20224040739.
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The underwater environment is complex and diverse, which makes it difficult to evolve traditional methods such as manually extracting features from blurred images. What's more, sonar images are so hard to be obtained that their number is far less than optical images, this case usually is called as few-shot, which leads to over fitting and low recognition accuracy of networks for sonar image classification. Based on the established sonar image data set after image preprocessing, a sonar image few-shot classification method with multi strategy optimization fusion is proposed in this paper. It is an improved label smooth regularization method with category preferences that can optimize the labels of training data and reduce the self-confidence of the network. And then based on the fine-tuning method in migration learning, some parameters of pre-learned models from optical images domain are utilized to help improve the performance in the sonar images domain. Finally, all the above three optimization strategies are combined. The simulation experiments in this study conclude that the optimal recognition accuracy can increase to 96.94%, which proves the multi strategy fusion can effectively suppresses the overfitting phenomenon and accurately classifies sonar images in the case of few-shot.
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Sun, Yushan, Haotian Zheng, Guocheng Zhang, Jingfei Ren, Hao Xu, and Chao Xu. "DP-ViT: A Dual-Path Vision Transformer for Real-Time Sonar Target Detection." Remote Sensing 14, no. 22 (2022): 5807. http://dx.doi.org/10.3390/rs14225807.
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Sonar image is the main way for underwater vehicles to obtain environmental information. The task of target detection in sonar images can distinguish multi-class targets in real time and accurately locate them, providing perception information for the decision-making system of underwater vehicles. However, there are many challenges in sonar image target detection, such as many kinds of sonar, complex and serious noise interference in images, and less datasets. This paper proposes a sonar image target detection method based on Dual Path Vision Transformer Network (DP-VIT) to accurately detect targets in forward-look sonar and side-scan sonar. DP-ViT increases receptive field by adding multi-scale to patch embedding enhances learning ability of model feature extraction by using Dual Path Transformer Block, then introduces Conv-Attention to reduce model training parameters, and finally uses Generalized Focal Loss to solve the problem of imbalance between positive and negative samples. The experimental results show that the performance of this sonar target detection method is superior to other mainstream methods on both forward-look sonar dataset and side-scan sonar dataset, and it can also maintain good performance in the case of adding noise.
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Zhao, Kun, Jisheng Ding, YanFei Sun, and ZhiYuan Hu. "Side-scan Sonar Image De-noising Based on Bidimensional Empirical Mode Decomposition and Non-local Means." E3S Web of Conferences 206 (2020): 03019. http://dx.doi.org/10.1051/e3sconf/202020603019.
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In order to suppress the multiplicative specular noise in side-scan sonar images, a denoising method combining bidimensional empirical mode decomposition and non-local means algorithm is proposed. First, the sonar image is decomposed into intrinsic mode functions(IMF) and residual component, then the high frequency IMF is denoised by non-local mean filtering method, and finally the processed intrinsic mode functions and residual component are reconstructed to obtain the de-noised side-scan sonar image. The paper’s method is compared with the conventional filtering algorithm for experimental quantitative analysis. The results show that this method can suppress the sonar image noise and retain the detailed information of the image, which is beneficial to the later image processing.
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Kokoshkin, Alexander V., Evgeny P. Novichikhin, and Ilia V. Smolyaninov. "Application of spectral and spatial processing methods to sonar images." Radioelectronics. Nanosystems. Information Technologies. 13, no. 3 (2021): 377–82. http://dx.doi.org/10.17725/rensit.2021.13.377.
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The paper proposes the use of the method of renormalization with limitation (MRL) for suppressing the speckle noise of images obtained using sonar. The method is tested on real images obtained by the interferometric side-view sonar. The principal possibility of a significant reduction in the speckle noise level is found due to the fact that the MRL renormalizes the spectrum of the sonar image to the universal reference spectrum (URS) model, which is a model of the spectrum of a "good" quality grayscale image. To increase the overall sharpness of the image, after applying the MRL, it is proposed to use spatial brightness transformations. The study allows us to conclude that the application of MRL to sonar images can significantly reduce speckle noise.
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Zhang, Zhi Gang, Hong Yu Bian, Hui Xu, and Zi Qi Song. "Method of Image Matching for Seabed Sonar Images Based on Regions of Interest." Applied Mechanics and Materials 532 (February 2014): 126–29. http://dx.doi.org/10.4028/www.scientific.net/amm.532.126.
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One of the most effective instruments for target detection in turbid waters is imaging sonar. However, the aspect angle of imaging sonar is usually small and that is a sacrifice for high detection precision. To make imaging sonar practical in large scale target detection with wide aspect angle, investigating image matching methods for continuous sonar frames is of great importance. A novel image matching method using local features of SIFT is described in this paper, which mainly focuses on the problem of weak echo signals and the following sonar images mismatch. The correspondence between objects and cast shadow regions is employed to extract regions of interest. Besides, status parameters of underwater vehicle are used to approximate the image transformation. Image segmentation methods are involved to decrease the size of the feature extracting regions and reduce the impact of non-target seabed areas, which improves the stability of this sonar image matching method significantly.
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Wang, Jian, Haisen Li, Guanying Huo, Chao Li, and Yuhang Wei. "Multi-Modal Multi-Stage Underwater Side-Scan Sonar Target Recognition Based on Synthetic Images." Remote Sensing 15, no. 5 (2023): 1303. http://dx.doi.org/10.3390/rs15051303.
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Due to the small sample size of underwater acoustic data and the strong noise interference caused by seabed reverberation, recognizing underwater targets in Side-Scan Sonar (SSS) images is challenging. Using a transfer-learning-based recognition method to train the backbone network on a large optical dataset (ImageNet) and fine-tuning the head network with a small SSS image dataset can improve the classification of sonar images. However, optical and sonar images have different statistical characteristics, directly affecting transfer-learning-based target recognition. In order to improve the accuracy of underwater sonar image classification, a style transformation method between optical and SSS images is proposed in this study. In the proposed method, objects with the SSS style were synthesized through content image feature extraction and image style transfer to reduce the variability of different data sources. A staged optimization strategy using multi-modal data effectively captures the anti-noise features of sonar images, providing a new learning method for transfer learning. The results of the classification experiment showed that the approach is more stable when using synthetic data and other multi-modal datasets, with an overall accuracy of 100%.
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Zhang, Zhi Gang, Hong Yu Bian, Zi Qi Song, and Hui Xu. "A Multi-View Sonar Image Fusion Method Based on Nonsubsampled Contourlet Transform and Morphological Modification." Applied Mechanics and Materials 530-531 (February 2014): 567–70. http://dx.doi.org/10.4028/www.scientific.net/amm.530-531.567.
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In underwater detection, a single image from the image sonar doesnt have the capacity to fully describe the surface of the interested target. In practice, a target is usually detected from various views, and similar contours and textures in a series of multi-view images can be employed. This work offers a novel technique for the fusion of a series of sonar images in multi-view detection of the same target, to improve the quality of images and repair the regions of target. The method takes advantage of Nonsubsampled Contourlet Transform (NSCT) to acquire coefficient matrixes of different resolutions and directions. Besides, a framework for sonar image fusion is set up based on morphology modification. The coefficient matrixes in NSCT domain are fused in the multi-scale framework and revised in decision-making. Experimental results show that target regions in the fused sonar images are effectively repaired and image quality also get improved evidently.
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Wan, Gang, Qi He, Qianqian Zhang, et al. "A Novel Lightweight Algorithm for Sonar Image Recognition." Sensors 25, no. 11 (2025): 3329. https://doi.org/10.3390/s25113329.
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Sonar images possess characteristics such as low resolution, high noise, and blurred edges. Utilizing CNNs would lead to problems such as inadequate target recognition accuracy. Moreover, due to their larger sizes and higher computational requirements, existing CNNs face deployment issues in embedded devices. Therefore, we propose a sonar image recognition algorithm optimized for the lightweight algorithm, MobileViT, by analyzing the features of sonar images. Firstly, the MobileViT block is modified by adding and redesigning the jump connection layer to capture more important features of sonar images. Secondly, the original 1 × 1 convolution is replaced with the redesigned multi-scale convolution Res2Net in the MV2 module to enhance the ability of the algorithm to learn global and local features. Finally, the IB loss is applied to address the imbalance of sample categories in the sonar dataset, assigning different weights to the samples to improve the performance of the network. The experimental results show that several proposed improvements have improved the accuracy of sonar image recognition to varying degrees. At the same time, the proposed algorithm is lightweight and can be deploy on embedded devices.
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Wen, Fengdan, Shaohua Jin, Gang Bian, and Chengyang Peng. "Small-target and diversity oriented underwater sonar image augmentation." Journal of Physics: Conference Series 3007, no. 1 (2025): 012056. https://doi.org/10.1088/1742-6596/3007/1/012056.
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Abstract Underwater sonar images are crucial in areas like oceanographic research for mapping the seabed and detecting resources, and in marine biology for understanding habitats. They are also important for naval and military uses such as navigation and surveillance. However, due to equipment and environmental limitations, the number of image samples is restricted, impeding further data-driven AI research. Although some works have explored data augmentation of underwater sonar images, they still face the following two problems: 1) inability to generate small-target images; 2) limited diversity of generated images. Toward this end, in this paper we propose a small-target and diversity oriented underwater sonar image augmentation method. Specifically, for small-target images, we propose to first detect and extract the target objects in the seabed sonar images, then perform scale scaling, and fuse them onto the background image using the Poisson fusion algorithm; for diverse images, we ingeniously combine mainstream image generation methods, including GAN, VAE, and Diffusion Models, using the diversity of the generative models to ensure the diversity of the generated images. Meanwhile, we design a Mixture-of-Experts (MoE) enhanced discriminator in GAN to screen the images generated by the three generative models to ensure the quality of the final augmented images. Experimental results prove that our method can effectively increase the proportion of small-target images and ensure the diversity of the augmented images, which further boost related researches based on underwater sonar images.
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Lee, Eon-ho, Byungjae Park, Myung-Hwan Jeon, Hyesu Jang, Ayoung Kim, and Sejin Lee. "Data augmentation using image translation for underwater sonar image segmentation." PLOS ONE 17, no. 8 (2022): e0272602. http://dx.doi.org/10.1371/journal.pone.0272602.
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In underwater environment, the study of object recognition is an important basis for implementing an underwater unmanned vessel. For this purpose, abundant experimental data to train deep learning model is required. However, it is very difficult to obtain these data because the underwater experiment itself is very limited in terms of preparation time and resources. In this study, the image transformation model, Pix2Pix is utilized to generate data similar to experimental one obtained by our ROV named SPARUS between the pool and reservoir. These generated data are applied to train the other deep learning model, FCN for a pixel segmentation of images. The original sonar image and its mask image have to be prepared for all training data to train the image segmentation model and it takes a lot of effort to do it what if all training data are supposed to be real sonar images. Fortunately, this burden can be released here, for the pairs of mask image and synthesized sonar image are already consisted in the image transformation step. The validity of the proposed procedures is verified from the performance of the image segmentation result. In this study, when only real sonar images are used for training, the mean accuracy is 0.7525 and the mean IoU is 0.7275. When the both synthetic and real data is used for training, the mean accuracy is 0.81 and the mean IoU is 0.7225. Comparing the results, the performance of mean accuracy increase to 6%, performance of the mean IoU is similar value.
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Dong, Zhipeng, Yanxiong Liu, Long Yang, Yikai Feng, Jisheng Ding, and Fengbiao Jiang. "Artificial Reef Detection Method for Multibeam Sonar Imagery Based on Convolutional Neural Networks." Remote Sensing 14, no. 18 (2022): 4610. http://dx.doi.org/10.3390/rs14184610.
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Artificial reef detection in multibeam sonar images is an important measure for the monitoring and assessment of biological resources in marine ranching. With respect to how to accurately detect artificial reefs in multibeam sonar images, this paper proposes an artificial reef detection framework for multibeam sonar images based on convolutional neural networks (CNN). First, a large-scale multibeam sonar image artificial reef detection dataset, FIO-AR, was established and made public to promote the development of artificial multibeam sonar image artificial reef detection. Then, an artificial reef detection framework based on CNN was designed to detect the various artificial reefs in multibeam sonar images. Using the FIO-AR dataset, the proposed method is compared with some state-of-the-art artificial reef detection methods. The experimental results show that the proposed method can achieve an 86.86% F1-score and a 76.74% intersection-over-union (IOU) and outperform some state-of-the-art artificial reef detection methods.
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Zhou, Ping, Jifa Chen, Pu Tang, Jianjun Gan, and Hongmei Zhang. "A Multi-Scale Fusion Strategy for Side Scan Sonar Image Correction to Improve Low Contrast and Noise Interference." Remote Sensing 16, no. 10 (2024): 1752. http://dx.doi.org/10.3390/rs16101752.
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Side scan sonar images have great application prospects in underwater surveys, target detection, and engineering activities. However, the acquired sonar images exhibit low illumination, scattered noise, distorted outlines, and unclear edge textures due to the complicated undersea environment and intrinsic device flaws. Hence, this paper proposes a multi-scale fusion strategy for side scan sonar (SSS) image correction to improve the low contrast and noise interference. Initially, an SSS image was decomposed into low and high frequency sub-bands via the non-subsampled shearlet transform (NSST). Then, modified multi-scale retinex (MMSR) was employed to enhance the contrast of the low frequency sub-band. Next, sparse dictionary learning (SDL) was utilized to eliminate high frequency noise. Finally, the process of NSST reconstruction was completed by fusing the emerging low and high frequency sub-band images to generate a new sonar image. The experimental results demonstrate that the target features, underwater terrain, and edge contours could be clearly displayed in the image corrected by the multi-scale fusion strategy when compared to eight correction techniques: BPDHE, MSRCR, NPE, ALTM, LIME, FE, WT, and TVRLRA. Effective control was achieved over the speckle noise of the sonar image. Furthermore, the AG, STD, and E values illustrated the delicacy and contrast of the corrected images processed by the proposed strategy. The PSNR value revealed that the proposed strategy outperformed the advanced TVRLRA technology in terms of filtering performance by at least 8.8%. It can provide sonar imagery that is appropriate for various circumstances.
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Conti, Luis Américo, and Murilo Baptista. "SYNTHETIC APERTURE SONAR IMAGES SEGMENTATION USING DYNAMICAL MODELING ANALYSIS." Revista Brasileira de Geofísica 31, no. 3 (2013): 455. http://dx.doi.org/10.22564/rbgf.v31i3.315.
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ABSTRACT. Symbolic Models applied to Synthetic Aperture Sonar images are proposed in order to assess the validity and reliability of use of such models and evaluate how effective they can be in terms of image classification and segmentation. We developed an approach for the description of sonar images where the pixels distribution can be transformed into points in the symbolic space in a similar way as symbolic space can encode a trajectory of a dynamical system. One of the main characteristic of approach is that points in the symbolic space are mapped respecting dynamical rules and, as a consequence, it can possible to calculate quantities thatcharacterize the dynamical system, such as Fractal Dimension (D), Shannon Entropy (H) and the amount of information of the image. It also showed potential to classify image sub-patterns based on the textural characteristics of the seabed. The proposed method reached a reasonable degree of success with results compatible with the classical techniques described in literature.Keywords: Synthetic Aperture Sonar, image processing, dynamical models, fractal, seabed segmentation. RESUMO. Este estudo apresenta uma proposta de metodologia para segmentação e classificação de imagens de sonar de Abertura Sintética a partir de modelos de Dinâmica Simbólica. Foram desenvolvidas, em um primeiro momento, técnicas de descrição de registros de sonar, com base na transformação da distribuição dos pixels da imagem em pontos em um espaço simbólico, codificado a partir de uma função de interação, de modo que as imagens podem ser interpretadas como sistemas dinâmicos em que trajetórias do sistema podem ser estabelecidas. Uma das características marcantes deste método é que, ao descrever uma imagem como um sistema dinâmico, é possível calcular grandezas como dimensão fractal (D) e entropia de Shannon (H) além da quantidade de informação inerente a imagem. Foi possível classificar, posteriormente, características texturais das imagens com base nas propriedades dinâmicas do espaço simbólico, o que permitiu a segmentação automática de padrões de “backscatter” indicando variações da geologia/geomorfologia do substrato marinho. O método proposto atingiu um razoável grau de sucesso em relação à acurácia de segmentação, com sucesso compatível com métodos alternativos descritos em literatura.Palavras-chave: sonar de abertura sintética, processamento de imagens, modelos dinâmicos, fractal, segmentação.
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Baek, Hyuk, Bong-Huan Jun, and Myounggyu D. Noh. "The Application of Sector-Scanning Sonar: Strategy for Efficient and Precise Sector-Scanning Using Freedom of Underwater Walking Robot in Shallow Water." Sensors 20, no. 13 (2020): 3654. http://dx.doi.org/10.3390/s20133654.
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In this paper, we discuss underwater walking robot technology to improve the quality of raw data in sector-scanning sonar images. We propose a strategy for an efficient and precise sector-scanning sonar image acquisition method for use in shallow, strong tidal water with a curved and sloped seabed environment. We verified the strategy by analyzing images acquired through a sea trial using the sector-scanning sonar installed on the CRABSTER (CR200). Before creating this strategy, an experiment was conducted to acquire the seabed image near a pier using a tripod and vertical pole. To overcome the problems and limitations revealed through image analysis, we established two technical strategies. In conclusion, we were able to achieve those technical strategies by using the CR200, which is resistant to strong current, and its six legs provide freedom of movement, allowing for a good sonar attitude.
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Yang, Zhiwei, Jianhu Zhao, Hongmei Zhang, Yongcan Yu, and Chao Huang. "A Side-Scan Sonar Image Synthesis Method Based on a Diffusion Model." Journal of Marine Science and Engineering 11, no. 6 (2023): 1103. http://dx.doi.org/10.3390/jmse11061103.
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The limited number and under-representation of side-scan sonar samples hinders the training of high-performance underwater object detection models. To address this issue, in this paper, we propose a diffusion model-based method to augment side-scan sonar image samples. First, the side-scan sonar image is transformed into Gaussian distributed random noise based on its a priori discriminant. Then, the Gaussian noise is modified step by step in the inverse process to reconstruct a new sample with the same distribution as the a priori data. To improve the sample generation speed, an accelerated encoder is introduced to reduce the model sampling time. Experiments show that our method can generate a large number of representative side-scan sonar images. The generated side-scan sonar shipwreck images are used to train an underwater shipwreck object detection model, which achieves a detection accuracy of 91.5% on a real side-scan sonar dataset. This exceeds the detection accuracy of real side-scan sonar data and validates the feasibility of the proposed method.
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Tian, Yuanyuan, Luyu Lan, and Haitao Guo. "A review on the wavelet methods for sonar image segmentation." International Journal of Advanced Robotic Systems 17, no. 4 (2020): 172988142093609. http://dx.doi.org/10.1177/1729881420936091.
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The sonar image segmentation is needed such as in underwater object orientation and recognition, in collision prevention and navigation of underwater robots, in underwater investigation and rescue, in seafloor object seeking, in seafloor salvage, and in marine military affairs like torpedo detection. The wavelet-based methods have the ability of multiscale and multiresolution, and they are apt at edge detection and feature extraction of images. The applications of these methods to the sonar image segmentation are increasingly raised. The contents of the article are to classify the sonar image segmentation methods with wavelets and to describe main ideas, advantages, disadvantages, and conditions of use of every method. In the methods for sonar image region (or texture) segmentation, the thought of multiscale (or multiresolution) analysis of the wavelet transform is usually combined with other theories or methods such as the clustering algorithms, the Markov random field, co-occurrence matrix, Bayesian theory, and support vector machine. In the methods for sonar image edge detection, the space–frequency local characteristics of the wavelet transform are usually utilized. The wavelet packet-based and beyond wavelet-based methods can usually reach more precise segmentation. The article also gives 12 directions (or development trends predicted) of the sonar image segmentation methods with wavelets which should be researched deeply in the future. The aim of writing this review is to make the researchers engaged in sonar image segmentation learn about the research works in the field in a short time. Up to now, the similar reviews in this field have not been found.
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Xie, Baolin, Hongmei Zhang, and Weihan Wang. "Side-Scan Sonar Image Classification Based on Joint Image Deblurring–Denoising and Pre-Trained Feature Fusion Attention Network." Electronics 14, no. 7 (2025): 1287. https://doi.org/10.3390/electronics14071287.
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Side-Scan Sonar (SSS) is widely used in underwater rescue operations and the detection of seabed targets, such as shipwrecks, drowning victims, and aircraft. However, the quality of sonar images is often degraded by noise sources like reverberation and speckle noise, which complicate the extraction of effective features. Additionally, challenges such as limited sample sizes and class imbalances are prevalent in side-scan sonar image data. These issues directly impact the accuracy of deep learning-based target classification models for SSS images. To address these challenges, we propose a side-scan sonar image classification model based on joint image deblurring–denoising and a pre-trained feature fusion attention network. Firstly, by employing transform domain filtering in conjunction with upsampling and downsampling techniques, the joint image deblurring–denoising approach effectively reduces image noise while preserving and enhancing edge and texture features. Secondly, a feature fusion attention network based on transfer learning is employed for image classification. Through the transfer learning approach, a feature extractor based on depthwise separable convolutions and densely connected networks is trained to effectively address the challenge of limited training samples. Subsequently, a dual-path feature fusion strategy is utilized to leverage the complementary strengths of different feature extraction networks. Furthermore, by incorporating channel attention and spatial attention mechanisms, key feature channels and regions are adaptively emphasized, thereby enhancing the accuracy and robustness of image classification. Finally, the Gradient-weighted Class Activation Mapping (Grad-CAM) technique is integrated into the proposed model to ensure interpretability and transparency. Experimental results show that our model achieves a classification accuracy of 96.80% on a side-scan sonar image dataset, confirming the effectiveness of this method for SSS image classification.
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Peng, Chengyang, Shaohua Jin, Gang Bian, Yang Cui, and Meina Wang. "Sample Augmentation Method for Side-Scan Sonar Underwater Target Images Based on CBL-sinGAN." Journal of Marine Science and Engineering 12, no. 3 (2024): 467. http://dx.doi.org/10.3390/jmse12030467.
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The scarcity and difficulty in acquiring Side-scan sonar target images limit the application of deep learning algorithms in Side-scan sonar target detection. At present, there are few amplification methods for Side-scan sonar images, and the amplification image quality is not ideal, which is not suitable for the characteristics of Side-scan sonar images. Addressing the current shortage of sample augmentation methods for Side-scan sonar, this paper proposes a method for augmenting single underwater target images using the CBL-sinGAN network. Firstly, considering the low resolution and monochromatic nature of Side-scan sonar images while balancing training efficiency and image diversity, a sinGAN network is introduced and designed as an eight-layer pyramid structure. Secondly, the Convolutional Block Attention Module (CBAM) is integrated into the network generator to enhance target learning in images while reducing information diffusion. Finally, an L1 loss function is introduced in the network discriminator to ensure training stability and improve the realism of generated images. Experimental results show that the accuracy of shipwreck target detection increased by 4.9% after training with the Side-scan sonar sample dataset augmented by the proposed network. This method effectively retains the style of the images while achieving diversity augmentation of small-sample underwater target images, providing a new approach to improving the construction of underwater target detection models.
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Han, Jingqi, Yue Fan, Zheng He, Zhenhang You, Peng Zhang, and Zhengliang Hu. "Self-Training Can Reduce Detection False Alarm Rate of High-Resolution Imaging Sonar." Applied Sciences 15, no. 3 (2025): 1189. https://doi.org/10.3390/app15031189.
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Imaging sonar is a primary means of underwater detection, but it faces challenges of high false alarm rates in sonar image target detection due to factors such as reverberation, noise, and resolution. This paper proposes a method to improve the false alarm rate by self-training a deep learning detector on sonar images. Self-training automatically generates proxy classification tasks based on the sonar image target detection dataset, and pre-trains the deep learning detector through these proxy classification tasks to enhance its learning effectiveness of target and background features. This, in turn, improves the detector’s ability to distinguish between targets and backgrounds, thereby reducing the false alarm rate. For the first time, this paper conducts target detection experiments based on deep learning using high-resolution synthetic aperture sonar images at two frequencies. The results show that, under the conditions of equal or higher recall rates, this method can reduce the false alarm rate by 3.91% and 18.50% on 240 kHz and 450 kHz sonar images, respectively, compared to traditional transfer learning methods.
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Yanchen, Wu. "Sonar Image Target Detection and Recognition Based on Convolution Neural Network." Mobile Information Systems 2021 (March 22, 2021): 1–8. http://dx.doi.org/10.1155/2021/5589154.
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Recent advancements in deep learning offer an effective approach for the study in machine vision using optical images. In this paper, a convolution neural network is used to deal with the target task of sonar detection, and the performance of each neural network model in the sonar image detection and recognition task of underwater box and tire is compared. The simulation results show that the neural network method proposed in this paper is better than the traditional machine learning methods and SSD network models. The average accuracy of the proposed method for sonar image target recognition is 93%, and the detection time of a single image is only 0.3 seconds.
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Aleksi, Ivan, Tomislav Matić, Benjamin Lehmann, and Dieter Kraus. "Robust A*-Search Image Segmentation Algorithm for Mine-like Objects Segmentation in SONAR Images." International journal of electrical and computer engineering systems 11, no. 2 (2020): 53–66. http://dx.doi.org/10.32985/ijeces.11.2.1.
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This paper addresses a sonar image segmentation method employing a Robust A*-Search Image Segmentation (RASIS) algorithm. RASIS is applied on Mine-Like Objects (MLO) in sonar images, where an object is defined by highlight and shadow regions, i.e. regions of high and low pixel intensities in a side-scan sonar image. RASIS uses a modified A*-Search method, which is usually used in mobile robotics for finding the shortest path where the environment map is predefined, and the start/goal locations are known. RASIS algorithm represents the image segmentation problem as a path-finding problem. Main modification concerning the original A*-Search is in the cost function that takes pixel intensities and contour curvature in order to navigate the 2D segmentation contour. The proposed method is implemented in Matlab and tested on real MLO images. MLO image dataset consist of 70 MLO images with manta mine present, and 70 MLO images with cylinder mine present. Segmentation success rate is obtained by comparing the ground truth data given by the human technician who is detecting MLOs. Measured overall success rate (highlight and shadow regions) is 91% for manta mines and 81% for cylinder mines.
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Wang, Yue, Kefa Zhou, Wei Tian, Zhe Chen, and Dewei Yang. "Underwater Sonar Image Segmentation by a Novel Joint Level Set Model." Journal of Physics: Conference Series 2173, no. 1 (2022): 012040. http://dx.doi.org/10.1088/1742-6596/2173/1/012040.
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Abstract This paper proposes a novel joint level set model for underwater sonar image segmentation. Combining features in points and regions in our novel joint level set (LS), it can achieve excellent performance for underwater sonar image segmentation. Regional information guides the model to locate the object of interest, whereas the point information accurately delineates contours. In addition, the unified Markov random field (UMRF) is taken to measure the neighboring relation between points and regions, which can overcome the problems of the high speckle noise, strong bias and low resolution of underwater sonar images. Our novel model can segment underwater sonar images into three partitions, such as the objects of interest, shadow and backgrounds. In contrast to current segmentation methods, outstanding results are demonstrated by our model. Moreover, another advantage of our model lies in its high efficiency.
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Zhou, Xiaoteng, Changli Yu, Xin Yuan, and Citong Luo. "Matching Underwater Sonar Images by the Learned Descriptor Based on Style Transfer Method." Journal of Physics: Conference Series 2029, no. 1 (2021): 012118. http://dx.doi.org/10.1088/1742-6596/2029/1/012118.
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Abstract This paper proposes a method that combines the style transfer technique and the learned descriptor to enhance the matching performances of underwater sonar images. In the field of underwater vision, sonar is currently the most effective long-distance detection sensor, it has excellent performances in map building and target search tasks. However, the traditional image matching algorithms are all developed based on optical images. In order to solve this contradiction, the style transfer method is used to convert the sonar images into optical styles, and at the same time, the learned descriptor with excellent expressiveness for sonar images matching is introduced. Experiments show that this method significantly enhances the matching quality of sonar images. In addition, it also provides new ideas for the preprocessing of underwater sonar images by using the style transfer approach.
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Zhang, Ning, Shaohua Jin, Gang Bian, Yang Cui, and Liang Chi. "A Mosaic Method for Side-Scan Sonar Strip Images Based on Curvelet Transform and Resolution Constraints." Sensors 21, no. 18 (2021): 6044. http://dx.doi.org/10.3390/s21186044.
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Due to the complex marine environment, side-scan sonar signals are unstable, resulting in random non-rigid distortion in side-scan sonar strip images. To reduce the influence of resolution difference of common areas on strip image mosaicking, we proposed a mosaic method for side-scan sonar strip images based on curvelet transform and resolution constraints. First, image registration was carried out to eliminate dislocation and distortion of the strip images. Then, the resolution vector of the common area in two strip images were calculated, and a resolution model was created. Curvelet transform was then performed for the images, the resolution fusion rules were used for Coarse layer coefficients, and the maximum coefficient integration was applied to the Detail layer and Fine layer to calculate the fusion coefficients. Last, inverse Curvelet transform was carried out on the fusion coefficients to obtain images in the fusion area. The fusion images in multiple areas were then combined in the registered images to obtain the final image. The experiment results showed that the proposed method had better mosaicking performance than some conventional fusion algorithms.
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Wu, Jun Peng, and Hai Tao Guo. "Sonar Image Segmentation Based on an Improved Selection of Initial Contour of Active Contour Model." Applied Mechanics and Materials 709 (December 2014): 447–50. http://dx.doi.org/10.4028/www.scientific.net/amm.709.447.
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The correct sonar image segmentation is an important foundation for underwater target recognition. Because the contour convergence of the active contour model depends on the selection of initial position, the active contour model is applied in sonar image segmentation. This paper proposed a selection method based on local standard deviation of image as the outline of initial contour. Due to the disturbance of noise, sonar image is usually affected in resolution and contrast. Firstly, sonar image is enhanced by top-hat and bottom-hat transformation in image morphology. Then after image enhancement, a suitable threshold value is chose for rough binarization and the standard deviation of target areas to calculate the local image. According to the size of standard deviation of different regions to determine the scope of the initial contour, sonar image segmentation is achieved by active contour algorithm.
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Bülow, Heiko, and Andreas Birk. "Synthetic Aperture Sonar (SAS) without Navigation: Scan Registration as Basis for Near Field Synthetic Imaging in 2D." Sensors 20, no. 16 (2020): 4440. http://dx.doi.org/10.3390/s20164440.
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Sonars are essential for underwater sensing as they can operate over extended ranges and in poor visibility conditions. The use of a synthetic aperture is a popular approach to increase the resolution of sonars, i.e., the sonar with its N transducers is positioned at k places to generate a virtual sensor with kN transducers. The state of the art for synthetic aperture sonar (SAS) is strongly coupled to constraints, especially with respect to the trajectory of the placements and the need for good navigation data. In this article, we introduce an approach to SAS using registration of scans from single arrays, i.e., at individual poses of arbitrary trajectories, hence avoiding the need for navigation data of conventional SAS systems. The approach is introduced here for the near field using the coherent phase information of sonar scans. A Delay and Sum (D&S) beamformer (BF) is used, which directly operates on pixel/voxel form on a Cartesian grid supporting the registration. It is shown that this pixel/voxel-based registration and the coherent processing of several scans forming a synthetic aperture yields substantial improvements of the image resolution. The experimental evaluation is done with an advanced simulation tool generating realistic 2D sonar array data, i.e., with simulations of a linear 1D antenna reconstructing 2D images. For the image registration of the raw sonar scans, a robust implementation of a spectral method is presented. Furthermore, analyses with respect to the trajectories of the sensor locations are provided to remedy possible grating lobes due to the gaping positions of the transmitter devices.
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Zieja, Mariusz, Wojciech Wawrzyński, Justyna Tomaszewska, and Norbert Sigiel. "A Method for the Interpretation of Sonar Data Recorded during Autonomous Underwater Vehicle Missions." Polish Maritime Research 29, no. 3 (2022): 176–86. http://dx.doi.org/10.2478/pomr-2022-0038.
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Abstract Image acquisition from autonomous underwater vehicles (AUVs) is useful for mapping objects on the seabed. However, there are few studies on the interpretation of data collected with side-scan sonar during autonomous underwater vehicle missions. By recording the seabed with 3D multibeam sonar, a large number of survey points can be obtained. The collected data are processed using applications based on remote sensing image processing. The data collected during AUV missions (or other sonar carriers) needs to be pre-processed to reach the proper effectiveness level. This process includes corrections of signal amplification (Time Varying Gain, or TVG) and geometric distortions of sonar images (Slant Range Corrections). It should be mentioned that, when carrying out the interpretation process for structures on the sea floor, sonar users need to understand the process of visualising seabed projections and depressions, as well as the resolution limitations of the sonar sensors.
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Chen, Xinzhe, and Hong Liang. "An Optimized Class Incremental Learning Network with Dynamic Backbone Based on Sonar Images." Journal of Marine Science and Engineering 11, no. 9 (2023): 1781. http://dx.doi.org/10.3390/jmse11091781.
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Class incremental learning with sonar images introduces a new dimension to underwater target recognition. Directly applying networks designed for optical images to our constructed sonar image dataset (SonarImage20) results in significant catastrophic forgetting. To address this problem, our study carefully selects the Dynamically Expandable Representation (DER)—recognized for its superior performance—as the baseline. We combine the intrinsic properties of sonar images with deep learning theories and optimize both the backbone and the class incremental training strategies of DER. The culmination of this optimization is the introduction of DER-Sonar, a class incremental learning network tailored for sonar images. Evaluations on SonarImage20 underscore the power of DER-Sonar. It outperforms competing class incremental learning networks with an impressive average recognition accuracy of 96.30%, a significant improvement of 7.43% over the baseline.
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Arriafdi, N., O. Zainon, U. Din, et al. "HULU SUNGAI PERAK BED SEDIMENT MAPPING USING UNDERWATER ACOUSTIC SONAR." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W1 (September 30, 2016): 339–43. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w1-339-2016.
Full textAbstract:
Development in acoustic survey techniques in particular side scan sonar have revolutionized the way we are able to image, map and understand the riverbed environment. It is now cost effective to image large areas of the riverbed using these techniques and the backscatter image created from surveys provides base line data from which thematic maps of the riverbed environment including maps of morphological geology, can be derived when interpreted in conjunction with in situ sampling data. This article focuses on investigation characteristics of sediments and correlation of side scan backscatter image with signal strength. The interpretation of acoustic backscatter rely on experienced interpretation by eye of grey scale images produced from the data. A 990F Starfish Side Scan Sonar was used to collect and develop a series of sonar images along 6 km of Hulu Sungai Perak. Background sediments could be delineated accurately and the image textures could be linked to the actual river floor appearance through grab sampling. A major difference was found in the acoustic returns from the two research area studies: the upstream area shows much rougher textures. This is due to an actual differences in riverbed roughness, caused by a difference in bottom currents and sediment dynamics in the two areas. The highest backscatter correlates with coarsest and roughness sediment. Result suggest that image based backscatter classification shows considerable promise for interpretation of side scan sonar data for the production of geological maps.
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Lubis, Muhammad Zainuddin, Husnul Kausarian, and Wenang Anurogo. "Seabed Detection Using Application Of Image Side Scan Sonar Instrument (Acoustic Signal)." Journal of Geoscience, Engineering, Environment, and Technology 2, no. 3 (2017): 230. http://dx.doi.org/10.24273/jgeet.2017.2.3.560.
Full textAbstract:
The importance of knowing the method for seabed detection using side-scan sonar images with sonar instrument is a much-needed requirement right now. This kind of threat also requires frequent sonar surveys in such areas. These survey operations need specific procedures and special equipment to ensure survey correctness. In this paper describes the method of observation and retrieval of marine imagery data using an acoustic signal method, to determine a target based on the sea. Side scan sonar is an instrument consisting of single beam transducer on both sides. Side scan sonar (SSS) is a sonar development that is able to show in two-dimensional images of the seabed surface with seawater conditions and target targets simultaneously. The side scan sonar data processing is performed through geometric correction to establish the actual position of the image pixel, which consists of bottom tracking, slant-range correction, layback correction and radiometric correction performed for the backscatter intensity of the digital number assigned to each pixel including the Beam Angle Correction (BAC), Automatic Gain Control (AGC), Time Varied Gain (TVG), and Empirical Gain Normalization (EGN).
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Lubis, Muhammad Zainuddin, Rasyid Alkhoir Lubis, and Ramadhan Ulil Albab Lubis. "The Two-Dimensional Wavelet Transform De-noising and Combining with Side Scan Sonar Image." Journal of Applied Geospatial Information 1, no. 01 (2017): 1–4. http://dx.doi.org/10.30871/jagi.v1i01.307.
Full textAbstract:
This paper puts forward an image de-noising method based on 2D wavelet transform with the application of the method in seabed identification data collection system. Two-dimensional haar wavelets in image processing presents a unified framework for wavelet image compression and combining with side scan sonar image. Seabed identification target have 7 target detection in side scan sonar imagery result. The vibration signals were analyzed to perform fault diagnosis. The obtained signal was time-domain signal. The experiment result shows that the application of 2D wavelet transform image de-noising algorithm can achieve good subjective and objective image quality and help to collect high quality data and analyze the images for the data center with optimum effects, the features from time-domain signal were extracted. 3 vectors were formed which are v1, v2, v3. In Haar wavelet retained energy is 93.8 %, so from the results, it has been concluded that Haar wavelet transform shows the best results in terms of Energy from De-noised Image processing with side scan sonar imagery.