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Journal articles on the topic 'Underwater object detection'

Author: Grafiati

Published: 10 December 2022

Last updated: 19 July 2025

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1

Shen, Jie, Zhenxin Xu, Zhe Chen, Huibin Wang, and Xiaotao Shi. "Optical Prior-Based Underwater Object Detection with Active Imaging." Complexity 2021 (April 27, 2021): 1–12. http://dx.doi.org/10.1155/2021/6656166.

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Underwater object detection plays an important role in research and practice, as it provides condensed and informative content that represents underwater objects. However, detecting objects from underwater images is challenging because underwater environments significantly degenerate image quality and distort the contrast between the object and background. To address this problem, this paper proposes an optical prior-based underwater object detection approach that takes advantage of optical principles to identify optical collimation over underwater images, providing valuable guidance for extra

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V, Karthikeyan. "Underwater Object Detection." International Journal for Research in Applied Science and Engineering Technology 8, no. 5 (2020): 2091–95. http://dx.doi.org/10.22214/ijraset.2020.5344.

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Wang, Zhuo, Haojie Chen, Hongde Qin, and Qin Chen. "Self-Supervised Pre-Training Joint Framework: Assisting Lightweight Detection Network for Underwater Object Detection." Journal of Marine Science and Engineering 11, no. 3 (2023): 604. http://dx.doi.org/10.3390/jmse11030604.

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In the computer vision field, underwater object detection has been a challenging task. Due to the attenuation of light in a medium and the scattering of light by suspended particles in water, underwater optical images often face the problems of color distortion and target feature blurring, which greatly affect the detection accuracy of underwater object detection. Although deep learning-based algorithms have achieved state-of-the-art results in the field of object detection, most of them cannot be applied to practice because of the limited computing capacity of a low-power processor embedded i

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Trinadh, Mr R., M. Chaitanya Deepika, M. Manojna, K. Sindhu Lavanya, H. Pranay Deep, and K. Ramya Sri. "Object Detection in Underwater Using Deep Learning Techniques." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 2756–60. http://dx.doi.org/10.22214/ijraset.2023.50919.

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Abstract: Based on the architecture of convolutional neural networks, a model is suggested. The model was created using underwater photography. YOLO is used in this method to locate items underwater. An autonomous underwater item-detecting system is necessary to reduce the cost of underwater inspection. An autonomous underwater item detection system is necessary to reduce the cost of underwater inspection. The main goal of this project is to create a model that can identify and recognize objects. This can be done using deep learning techniques. Object detection has two parts. One is object cla

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ZHANG, Yan, Xingshan LI, Yemei SUN, and Shudong LIU. "Underwater object detection algorithm based on channel attention and feature fusion." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 40, no. 2 (2022): 433–41. http://dx.doi.org/10.1051/jnwpu/20224020433.

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Due to the color deviation, low contrast and fuzzy object in underwater optical images, there are some problems in underwater object detection, such as missed detection and false detection. In order to solve the above-mentioned problems, an underwater object detection algorithm is proposed based on the channel attention and feature fusion for underwater optical images. The excitation residual module is designed based on the channel attention, and the forward propagation feature information is adaptively allocated weights to highlight the salience of different channel feature maps, which improv

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Zhang, Yangmei. "Application of Smart Sensor in Underwater Weak Object Detection and Positioning." Wireless Communications and Mobile Computing 2021 (December 23, 2021): 1–16. http://dx.doi.org/10.1155/2021/5791567.

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This paper is aimed at studying underwater object detection and positioning. Objects are detected and positioned through an underwater scene segmentation-based weak object detection algorithm and underwater positioning technology based on the three-dimensional (3D) omnidirectional magnetic induction smart sensor. The proposed weak object detection involves a predesigned U-shaped network- (U-Net-) architectured image segmentation network, which has been improved before application. The key factor of underwater positioning technology based on 3D omnidirectional magnetic induction is the magnetic

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Maccabee, Bruce S. "Underwater object detection system." Journal of the Acoustical Society of America 91, no. 5 (1992): 3081. http://dx.doi.org/10.1121/1.402901.

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Wang, Shihao, Xiaoyu Liu, Siquan Yu, Xinghua Zhu, Bingbing Chen, and Xiaoyu Sun. "Design and Implementation of SSS-Based AUV Autonomous Online Object Detection System." Electronics 13, no. 6 (2024): 1064. http://dx.doi.org/10.3390/electronics13061064.

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Underwater object detection is an important task in marine exploration. The existing autonomous underwater vehicle (AUV) designs typically lack an integrated object detection module and are constrained by communication limitations in underwater environments. This results in a situation where AUV, when tasked with object detection missions, require real-time transmission of underwater sensing data to shore-based stations but are unable to do so. Consequently, the task is divided into two discontinuous phases: AUV acquisition of underwater data and shore-based object detection, leading to limite

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Shen, Jie, Tanghuai Fan, Min Tang, Qian Zhang, Zhen Sun, and Fengchen Huang. "A Biological Hierarchical Model Based Underwater Moving Object Detection." Computational and Mathematical Methods in Medicine 2014 (2014): 1–8. http://dx.doi.org/10.1155/2014/609801.

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Underwater moving object detection is the key for many underwater computer vision tasks, such as object recognizing, locating, and tracking. Considering the super ability in visual sensing of the underwater habitats, the visual mechanism of aquatic animals is generally regarded as the cue for establishing bionic models which are more adaptive to the underwater environments. However, the low accuracy rate and the absence of the prior knowledge learning limit their adaptation in underwater applications. Aiming to solve the problems originated from the inhomogeneous lumination and the unstable ba

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R, Rohan, Vishnu Prakash R, Shibin K T, Akshay K, and Akhila E. "Underwater Image Restoration and Object Detection." Journal of Innovative Image Processing 6, no. 1 (2024): 74–83. http://dx.doi.org/10.36548/jiip.2024.1.007.

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Underwater environments present unique challenges for imaging due to factors such as light attenuation, scattering, and colour distortion. This research combines advanced CNN models like CBAM(convolutional Block Attention Mod-ule) and VGG16 with state-of-the-art object detection methods of CNN like YOLO or RCNN to enhance the visual quality of underwater images and to detect the objects based on an accuracy rate. Leveraging the various capabilities of the VGG16 model, pretrained on extensive datasets, the system efficiently restores degraded underwater images by capturing and learning intricat

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Wang, Jinkang, Xiaohui He, Faming Shao, et al. "A Novel Attention-Based Lightweight Network for Multiscale Object Detection in Underwater Images." Journal of Sensors 2022 (September 7, 2022): 1–14. http://dx.doi.org/10.1155/2022/2582687.

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Underwater images have low quality, and underwater targets have different sizes. The mainstream target detection networks cannot achieve good results in detecting objects from underwater images. In this study, a lightweight underwater multiscale target detection model with an attention mechanism is designed to solve the above problems. In this model, MobileNetv3 is used as the backbone network for preliminary feature extraction. The lightweight feature extraction module (LFEM) pays attention to the feature map at the channel and space levels. The features with large weights are promoted, while

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Suryowinoto, Andy, Teguh Herlambang, Muhammad Sawal Baital, and Berny Pebo Tomasouw. "UNDERWATER OBJECT SHAPE DETECTION BASED ON TONAL DISTRIBUTION AND EDGE DETECTION USING DIGITAL IMAGE PROCESSING." BAREKENG: Jurnal Ilmu Matematika dan Terapan 18, no. 1 (2024): 0395–402. http://dx.doi.org/10.30598/barekengvol18iss1pp0395-0402.

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Underwater exploration activities always have their own charm, many exotic objects that exist in underwater ecosystems have not been mapped properly, due to the lack of related databases of the shapes and names of these underwater objects. Another factor that affects the visibility of objects related to the quantity of light intensity that enters under water, also not as much above the surface of the abundant water, especially during the day. This also hinders the process of documenting underwater objects. The main purpose of this study was to obtain the shape of underwater objects for several

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Wulandari, Nurcahyani, Igi Ardiyanto, and Hanung Adi Nugroho. "A Comparison of Deep Learning Approach for Underwater Object Detection." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 6, no. 2 (2022): 252–58. http://dx.doi.org/10.29207/resti.v6i2.3931.

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In recent year, marine ecosystems and fisheries becomes potential resources, therefore, monitoring of these objects will be important to ensure their existence. One of computer vision techniques, it is object detection, utilized to recognize and localize objects in underwater scenery. Many studies have been conducted to investigate various deep learning methods implemented in underwater object detection; however, only a few investigations have been performed to compare mainstream object detection algorithms in these circumstances. This article examines various state-of-the-art deep learning me

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Mahavarkar, Avinash, Ritika Kadwadkar, Sneha Maurya, and Smitha Raveendran. "Underwater Object Detection using Tensorflow." ITM Web of Conferences 32 (2020): 03037. http://dx.doi.org/10.1051/itmconf/20203203037.

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Object Detection is a popular technology that detects instances within an image. In order to eliminate the barriers in Computer Vision technology due to the dissolution of the BGR(Blue-Green-Red) constituents with the increase in depth, it has been a necessity that the accuracy and efficiency of detecting any object underwater is optimum. In this article, we conduct Underwater Object Detection using Machine Learning through Tensorflow and Image Processing along with Faster R-CNN (Regions with Convolution Neural Network) as an algorithm for implementation. A suitable environment will be created

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Liu, Kun, Lei Peng, and Shanran Tang. "Underwater Object Detection Using TC-YOLO with Attention Mechanisms." Sensors 23, no. 5 (2023): 2567. http://dx.doi.org/10.3390/s23052567.

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Underwater object detection is a key technology in the development of intelligent underwater vehicles. Object detection faces unique challenges in underwater applications: blurry underwater images; small and dense targets; and limited computational capacity available on the deployed platforms. To improve the performance of underwater object detection, we proposed a new object detection approach that combines a new detection neural network called TC-YOLO, an image enhancement technique using an adaptive histogram equalization algorithm, and the optimal transport scheme for label assignment. The

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Zheng, Shijian, Rujing Wang, and Liusan Wang. "Underwater Fish Object Detection with Degraded Prior Knowledge." Electronics 13, no. 12 (2024): 2346. http://dx.doi.org/10.3390/electronics13122346.

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Understanding fish distribution, behavior, and abundance is crucial for marine ecological research, fishery management, and environmental monitoring. However, the distinctive features of the underwater environment, including low visibility, light attenuation, water turbidity, and strong currents, significantly impact the quality of data gathered by underwater imaging systems, posing considerable challenges in accurately detecting fish objects. To address this challenge, our study proposes an innovative fish detection network based on prior knowledge of image degradation. In our research proces

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Zhao, Xiaoyang, Zhuo Wang, Zhongchao Deng, and Hongde Qin. "G-Net: An Efficient Convolutional Network for Underwater Object Detection." Journal of Marine Science and Engineering 12, no. 1 (2024): 116. http://dx.doi.org/10.3390/jmse12010116.

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Visual perception technology is of great significance for underwater robots to carry out seabed investigation and mariculture activities. Due to the complex underwater environment, it is often necessary to enhance the underwater image when detecting underwater targets by optical sensors. Most of the traditional methods involve image enhancement and then target detection. However, this method greatly increases the timeliness in practical application. To solve this problem, we propose a feature-enhanced target detection network, Global-Net (G-Net), which combines underwater image enhancement wit

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Liu, Guoqin, Vyacheslav Aranchuk, Likun Zhang, and Craig J. Hickey. "Laser-acoustic detection of objects buried underwater." Journal of the Acoustical Society of America 153, no. 3_supplement (2023): A53. http://dx.doi.org/10.1121/10.0018138.

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An object buried underwater such as landmines can potentially be excited to vibrate by a sound source in air. The vibration then radiates a secondary wave in the water to excite the water surface vibration which can be detected by a laser sensor. This idea of laser-acoustic detection of buried objects is effective in detecting objects buried under ground where the object is mechanically excited and the ground surface vibration is scanned by the laser sensor. When applying this approach to detect objects buried underwater, the addition of the water layer has an impact on the flexibility of the

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Karimanzira, Divas, Helge Renkewitz, David Shea, and Jan Albiez. "Object Detection in Sonar Images." Electronics 9, no. 7 (2020): 1180. http://dx.doi.org/10.3390/electronics9071180.

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The scope of the project described in this paper is the development of a generalized underwater object detection solution based on Automated Machine Learning (AutoML) principles. Multiple scales, dual priorities, speed, limited data, and class imbalance make object detection a very challenging task. In underwater object detection, further complications come in to play due to acoustic image problems such as non-homogeneous resolution, non-uniform intensity, speckle noise, acoustic shadowing, acoustic reverberation, and multipath problems. Therefore, we focus on finding solutions to the problems

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Gadzhiev, А. А., R. А. Eminov, and Kh G. Asadov. "Solution to the problem of minimum distance detection of various objects in shallow water depth." Transactions of the Krylov State Research Centre 2, no. 400 (2022): 147–52. http://dx.doi.org/10.24937/2542-2324-2022-2-400-147-152.

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Object and purpose of research. The object of research is arrangement of various items on the bottom of water bodies. The purpose of research is achieving maximum invisibility for such items. Anticipated search or accidental detection of bottom objects can be carried out by the bathymetric method, i.e. assessment of water column over such objects. Materials and methods. It is expected that low flying UAVs (unmanned aerial vehicles) equipped with bathymetric laser emitter are used for detection of underwater objects. With consideration of some simplifications, optimization is carried out for th

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Zhang, Fubin, Weiye Cao, Jian Gao, et al. "Underwater Object Detection Algorithm Based on an Improved YOLOv8." Journal of Marine Science and Engineering 12, no. 11 (2024): 1991. http://dx.doi.org/10.3390/jmse12111991.

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Due to the complexity and diversity of underwater environments, traditional object detection algorithms face challenges in maintaining robustness and detection accuracy when applied underwater. This paper proposes an underwater object detection algorithm based on an improved YOLOv8 model. First, the introduction of CIB building blocks into the backbone network, along with the optimization of the C2f structure and the incorporation of large-kernel depthwise convolutions, effectively enhances the model’s receptive field. This improvement increases the capability of detecting multi-scale objects

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Yuxin Long. "Underwater Object Detection Based on Spatial Pyramid and Channel Attention." International Water and Irrigation 44, no. 1 (2024): 265–77. https://doi.org/10.52783/iwi.v44i1.138.

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In order to solve the problems of low accuracy and high detection delay of conventional object detector in underwater environment, underwater object detection network model based on single-stage target detection is introduced. The specific work is as follows: In order to solve the problem that conventional detectors have low detection accuracy in detecting blurry small targets in underwater scenes due to water quality, a channel spatial attention mechanism is designed, which enables the model to focus more on the feature learning of target objects. This improves the extraction of information w

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Er, Meng Joo, Jie Chen, Yani Zhang, and Wenxiao Gao. "Research Challenges, Recent Advances, and Popular Datasets in Deep Learning-Based Underwater Marine Object Detection: A Review." Sensors 23, no. 4 (2023): 1990. http://dx.doi.org/10.3390/s23041990.

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Underwater marine object detection, as one of the most fundamental techniques in the community of marine science and engineering, has been shown to exhibit tremendous potential for exploring the oceans in recent years. It has been widely applied in practical applications, such as monitoring of underwater ecosystems, exploration of natural resources, management of commercial fisheries, etc. However, due to complexity of the underwater environment, characteristics of marine objects, and limitations imposed by exploration equipment, detection performance in terms of speed, accuracy, and robustnes

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Zhang, Feihu, Wei Zhang, Chensheng Cheng, Xujia Hou, and Chun Cao. "Detection of Small Objects in Side-Scan Sonar Images Using an Enhanced YOLOv7-Based Approach." Journal of Marine Science and Engineering 11, no. 11 (2023): 2155. http://dx.doi.org/10.3390/jmse11112155.

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Deep learning-based object detection methods have demonstrated remarkable effectiveness across various domains. Recently, there has been growing interest in applying these techniques to underwater environments. Conventional optical imaging methods face severe limitations when operating in underwater conditions, restricting their ability to identify objects with good visibility and at close distances. Consequently, side-scan sonar (SSS) has emerged as a common equipment choice for underwater detection due to its compatibility with the characteristics of sound waves in water. This paper introduc

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Li, Zhenming, Bing Zheng, Dong Chao, et al. "Underwater-Yolo: Underwater Object Detection Network with Dilated Deformable Convolutions and Dual-Branch Occlusion Attention Mechanism." Journal of Marine Science and Engineering 12, no. 12 (2024): 2291. https://doi.org/10.3390/jmse12122291.

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Underwater object detection is critical for marine ecological monitoring and biodiversity research, yet existing algorithms struggle in detecting densely packed objects of varying sizes, particularly in occluded and complex underwater environments. This study introduces Underwater-Yolo, a novel detection network that enhances performance in these challenging scenarios by integrating a dual-branch occlusion-handling attention mechanism (GLOAM) and a Cross-Stage Partial Dilated Deformable Convolution (CSP-DDC) backbone. The dilated deformable convolutions (DDCs) in the backbone and neck expand t

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Pachaiyappan, Prabhavathy, Gopinath Chidambaram, Abu Jahid, and Mohammed H. Alsharif. "Enhancing Underwater Object Detection and Classification Using Advanced Imaging Techniques: A Novel Approach with Diffusion Models." Sustainability 16, no. 17 (2024): 7488. http://dx.doi.org/10.3390/su16177488.

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Underwater object detection and classification pose significant challenges due to environmental factors such as water turbidity and variable lighting conditions. This research proposes a novel approach that integrates advanced imaging techniques with diffusion models to address these challenges effectively, aligning with Sustainable Development Goal (SDG) 14: Life Below Water. The methodology leverages the Convolutional Block Attention Module (CBAM), Modified Swin Transformer Block (MSTB), and Diffusion model to enhance the quality of underwater images, thereby improving the accuracy of object

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Feng, Guanbo, Zhixin Xiong, Hongshuai Pang, et al. "RTL-YOLOv8n: A Lightweight Model for Efficient and Accurate Underwater Target Detection." Fishes 9, no. 8 (2024): 294. http://dx.doi.org/10.3390/fishes9080294.

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Underwater object detection is essential for the advancement of automated aquaculture operations. Addressing the challenges of low detection accuracy and insufficient generalization capabilities for underwater targets, this paper focuses on the development of a novel detection method tailored to such environments. We introduce the RTL-YOLOv8n model, specifically designed to enhance the precision and efficiency of detecting objects underwater. This model incorporates advanced feature-extraction mechanisms—RetBlock and triplet attention—that significantly improve its ability to discern fine deta

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Zavyalov, A., and Yu Patrakov. "Universal assessment method for laser detection probability of sunken engineering structures." Transactions of the Krylov State Research Centre 1, no. 399 (2022): 176–88. http://dx.doi.org/10.24937/2542-2324-2022-1-399-176-188.

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Object and purpose of research. Laser diagnostics, analysis of reflected laser signal from fixed underwater objects, improvement of laser optical methods, technologies and tools for underwater object studies, development of laser detection systems, determination of laser indication probability for fixed underwater objects. Materials and methods. Laser detection systems, analytical and computational methods, software programs, analytical tools for measurement data processing, laser diagnostics of reflected laser signal from underwater objects taking into account dissipation and absorption in at

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Zhao, Hua, Chao Xu, Jiaxing Chen, Zhexian Zhang, and Xiang Wang. "BGLE-YOLO: A Lightweight Model for Underwater Bio-Detection." Sensors 25, no. 5 (2025): 1595. https://doi.org/10.3390/s25051595.

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Due to low contrast, chromatic aberration, and generally small objects in underwater environments, a new underwater fish detection model, BGLE-YOLO, is proposed to investigate automated methods dedicated to accurately detecting underwater objects in images. The model has small parameters and low computational effort and is suitable for edge devices. First, an efficient multi-scale convolutional EMC module is introduced to enhance the backbone network and capture the dynamic changes in targets in the underwater environment. Secondly, a global and local feature fusion module for small targets (B

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Baig, Muhammad Daniyal, and Hafiz Burhan Ul Haq. "Marine Object Detection using YOLOv4 Adapted Convolutional Neural Network." Decision Making Advances 2, no. 1 (2024): 83–91. http://dx.doi.org/10.31181/dma21202428.

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This research presents an innovative application of the YOLOv4 object detection model for the identification and classification of marine objects within a dataset encompassing seven distinct classes. The study focuses on enhancing the robustness and accuracy of object detection in challenging marine environments, leveraging the unique capabilities of YOLOv4. Pre-processing steps involve resizing raw images, applying data augmentations, and normalizing pixel values to ensure optimal model training. Specifically tailored for underwater scenarios, additional color space transformations address va

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Wang, Jingyang, Yujia Li, Junkai Wang, and Ying Li. "An Underwater Dense Small Object Detection Model Based on YOLOv5-CFDSDSE." Electronics 12, no. 15 (2023): 3231. http://dx.doi.org/10.3390/electronics12153231.

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Underwater target detection is a key technology in the process of exploring and developing the ocean. Because underwater targets are often very dense, mutually occluded, and affected by light, the detection objects are often unclear, and so, underwater target detection technology faces unique challenges. In order to improve the performance of underwater target detection, this paper proposed a new target detection model YOLOv5-FCDSDSE based on YOLOv5s. In this model, the CFnet (efficient fusion of C3 and FasterNet structure) structure was used to optimize the network structure of the YOLOv5, wh

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Li, Qiming, and Hongwei Shi. "YOLO-GE: An Attention Fusion Enhanced Underwater Object Detection Algorithm." Journal of Marine Science and Engineering 12, no. 10 (2024): 1885. http://dx.doi.org/10.3390/jmse12101885.

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Underwater object detection is a challenging task with profound implications for fields such as aquaculture, marine ecological protection, and maritime rescue operations. The presence of numerous small aquatic organisms in the underwater environment often leads to issues of missed detections and false positives. Additionally, factors such as the water quality result in weak target features, which adversely affect the extraction of target feature information. Furthermore, the lack of illumination underwater causes image blur and low contrast, thereby increasing the difficulty of the detection t

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Cao, Ruicheng, Ruiteng Zhang, Xinyue Yan, and Jian Zhang. "BG-YOLO: A Bidirectional-Guided Method for Underwater Object Detection." Sensors 24, no. 22 (2024): 7411. http://dx.doi.org/10.3390/s24227411.

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Degraded underwater images decrease the accuracy of underwater object detection. Existing research uses image enhancement methods to improve the visual quality of images, which may not be beneficial in underwater image detection and lead to serious degradation in detector performance. To alleviate this problem, we proposed a bidirectional guided method for underwater object detection, referred to as BG-YOLO. In the proposed method, a network is organized by constructing an image enhancement branch and an object detection branch in a parallel manner. The image enhancement branch consists of a c

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Yang, Ke, Xiao Wang, Wei Wang, Xin Yuan, and Xin Xu. "SEANet: Semantic Enhancement and Amplification for Underwater Object Detection in Complex Visual Scenarios." Sensors 25, no. 10 (2025): 3078. https://doi.org/10.3390/s25103078.

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Detecting underwater objects is a complex task due to the inherent challenges of low contrast and intricate backgrounds. The wide range of object scales further complicates detection accuracy. To address these issues, we propose a Semantic Enhancement and Amplification Network (SEANet), a framework designed to enhance underwater object detection in complex visual scenarios. SEANet integrates three core components: the Multi-Scale Detail Amplification Module (MDAM), the Semantic Enhancement Feature Pyramid (SE-FPN), and the Contrast Enhancement Module (CEM). MDAM expands the receptive field acr

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Yang, Yutuo, Wei Liang, Daoxian Zhou, Yinlong Zhang, and Gaofei Xu. "Object Detection for Underwater Cultural Artifacts Based on Deep Aggregation Network with Deformation Convolution." Journal of Marine Science and Engineering 11, no. 12 (2023): 2228. http://dx.doi.org/10.3390/jmse11122228.

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Cultural artifacts found underwater are located in complex environments with poor imaging conditions. In addition, the artifacts themselves present challenges for automated object detection owing to variations in their shape and texture caused by breakage, stacking, and burial. To solve these problems, this paper proposes an underwater cultural object detection algorithm based on the deformable deep aggregation network model for autonomous underwater vehicle (AUV) exploration. To fully extract the object feature information of underwater objects in complex environments, this paper designs a mu

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Sheela, K. Selva, S. Vinoth Kumar, Saman M. Almufti, and R. Lakshmana Kumar. "Shifted split-merge segmentation and fuzzy-guided generative adversarial network underwater object detection." Intelligent Data Analysis: An International Journal 29, no. 4 (2024): 1037–61. https://doi.org/10.1177/1088467x241290657.

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Marine object localization from UAVs is required for different areas, including marine research, environment monitoring and topographic surveys. Herein, we introduce a new undersea object detection method; synthesizing shifted split-merge segmentation and fuzzy-oriented generative adversarial networks. Split-merge segmentation is the region-based model, where this process is achieved via splitting and merging regions based on specified similarity measures. The previous split-merge segmentation algorithm was modified to employ a shifted window approach that is better at detecting undersea objec

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Nersesov, B. A. "METHODS OF PROBABILISTIC PERFORMANCE EVALUATION MARINE MAGNETOMETRY." Journal of Oceanological Research 47, no. 4 (2019): 152–60. http://dx.doi.org/10.29006/1564-2291.jor-2019.47(4).10.

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Marine magnetometers are promising means of monitoring the water area with an estimated presence of potentially dangerous underwater objects. They are successfully used when searching for underwater objects in conditions of the ineffectiveness of sonar tools: in shallow water, in any media (water, soil) and, especially, at the boundaries of these media. As a rule, the search for an underwater object using magnetometric means is carried out along the “orthogonal tacks” trajectory, the main characteristics of which are the search bandwidth (depending on the magnetic characteristics of the underw

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Pan, Haixia, Jiahua Lan, Hongqiang Wang, et al. "UWV-Yolox: A Deep Learning Model for Underwater Video Object Detection." Sensors 23, no. 10 (2023): 4859. http://dx.doi.org/10.3390/s23104859.

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Underwater video object detection is a challenging task due to the poor quality of underwater videos, including blurriness and low contrast. In recent years, Yolo series models have been widely applied to underwater video object detection. However, these models perform poorly for blurry and low-contrast underwater videos. Additionally, they fail to account for the contextual relationships between the frame-level results. To address these challenges, we propose a video object detection model named UWV-Yolox. First, the Contrast Limited Adaptive Histogram Equalization method is used to augment t

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Nissar, Mehvish, Amit Kumar Mishra, and Badri Narayan Subudhi. "Dual Stream Encoder–Decoder Architecture with Feature Fusion Model for Underwater Object Detection." Mathematics 12, no. 20 (2024): 3227. http://dx.doi.org/10.3390/math12203227.

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Underwater surveillance is an imminent and fascinating exploratory domain, particularly in monitoring aquatic ecosystems. This field offers valuable insights into underwater behavior and activities, which have broad applications across various domains. Specifically, underwater surveillance involves detecting and tracking moving objects within aquatic environments. However, the complex properties of water make object detection a challenging task. Background subtraction is a commonly employed technique for detecting local changes in video scenes by segmenting images into the background and foreg

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Wei, Yifan, Jun Tao, Wenjun Wu, Donghua Yuan, and Shunzhi Hou. "RHS-YOLOv8: A Lightweight Underwater Small Object Detection Algorithm Based on Improved YOLOv8." Applied Sciences 15, no. 7 (2025): 3778. https://doi.org/10.3390/app15073778.

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To address the challenge posed by the abundance of small objects with weak object features and little information in the images of underwater biomonitoring scenarios, and the added difficulty of recognizing these objects due to light absorption and scattering in the underwater environment, this study proposes an improved RHS-YOLOv8 (Ref-Dilated-HBFPN-SOB-YOLOv8). Firstly, a combination of hybrid inflated convolution and RefConv is used to redesign the lightweight Ref-Dilated convolution block, which reduces the model computation. Second, a new feature pyramid network fusion module, the Hybrid

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Sineglazov, Victor, and Mykhailo Savchenko. "Comprehensive Framework for Underwater Object Detection Based on Improved YOLOv8." Electronics and Control Systems 1, no. 79 (2024): 9–15. http://dx.doi.org/10.18372/1990-5548.79.18429.

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Underwater object detection poses unique challenges due to issues such as poor visibility, small densely packed objects, and target occlusion. In this paper, we propose a comprehensive framework for underwater object detection based on improved YOLOv8, addressing these challenges and achieving superior performance. Our framework integrates several key enhancements including Contrast Limited Adaptive Histogram Equalization for image preprocessing, a lightweight GhostNetV2 backbone, Coordinate Attention mechanism, and Deformable ConvNets v4 for improved feature representation. Through experiment

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Han, Fenglei, Jingzheng Yao, Haitao Zhu, and Chunhui Wang. "Underwater Image Processing and Object Detection Based on Deep CNN Method." Journal of Sensors 2020 (May 22, 2020): 1–20. http://dx.doi.org/10.1155/2020/6707328.

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Due to the importance of underwater exploration in the development and utilization of deep-sea resources, underwater autonomous operation is more and more important to avoid the dangerous high-pressure deep-sea environment. For underwater autonomous operation, the intelligent computer vision is the most important technology. In an underwater environment, weak illumination and low-quality image enhancement, as a preprocessing procedure, is necessary for underwater vision. In this paper, a combination of max-RGB method and shades of gray method is applied to achieve the enhancement of underwater

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Zhou, Shize, Long Wang, Zhuoqun Chen, Hao Zheng, Zhihui Lin, and Li He. "An Improved YOLOv9s Algorithm for Underwater Object Detection." Journal of Marine Science and Engineering 13, no. 2 (2025): 230. https://doi.org/10.3390/jmse13020230.

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Monitoring marine life through underwater object detection technology serves as a primary means of understanding biodiversity and ecosystem health. However, the complex marine environment, poor resolution, color distortion in underwater optical imaging, and limited computational resources all affect the accuracy and efficiency of underwater object detection. To solve these problems, the YOLOv9s-SD underwater target detection algorithm is proposed to improve the detection performance in underwater environments. We combine the inverted residual structure of MobileNetV2 with Simple Attention Modu

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Ayush Aditya, Om Prakash, Praveen, Yash Rathi, and Prof. Ramya K. "Implementation of Image Recognition for Human detection in Underwater Images." International Research Journal on Advanced Engineering Hub (IRJAEH) 2, no. 01 (2024): 1–5. http://dx.doi.org/10.47392/irjaeh.2024.0001.

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Recent advances in deep learning have resolved the challenges of detection of objects underwater. Specialized methods have been developed as a result of the particular characteristics of small, fuzzy objects and heterogeneous noise. The Sample-Weighted Network (SWIPE Net) for small object recognition is one of them, as are frameworks with feature enhancement and anchor refining. Additionally, upgraded versions of the attention processes and YOLOv7 have been released. These advancements help with tracking the effects of clean energy technologies, developing accurate and reliable underwater obje

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Zhu, Yong, Qingyi He, Qiang Fu, Chao Dong, Jianhua Liu, and Jin Duan. "Detection of Underwater Targets Using Polarization Laser Assisted Echo Detection Technique." Applied Sciences 13, no. 5 (2023): 3222. http://dx.doi.org/10.3390/app13053222.

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At present, polarization imaging detection experiments of underwater objects mainly focus on the degree of polarization but lack of Stokes vector imaging effect of each element. Based on the principle that the polarization characteristics of different materials are different, the experiment of underwater target detection by laser pulse polarization is carried out in this paper, and the influence of different depths of an underwater object and material factors on polarization imaging detection is studied. The results show that in air, the average degree of polarization of iron sheet is 0.56, th

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Kolyuchkin, V. Y., N. M. Kostylev, and Y. S. Gulina. "Performance evaluation of underwater vision systems." Computer Optics 47, no. 5 (2023): 761–69. http://dx.doi.org/10.18287/2412-6179-co-1262.

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The article describes a methodology for performance evaluation of vision systems for remotely operated underwater vehicles. The methodology is based on a system approach and uses mathematical models of the aqueous medium where an optical signal propagates, the underwater object image registration system, and the mathematical model of the human visual system. The detection and recognition probabilities of underwater object image at a given registration range are used as performance evaluation indicators of underwater vision systems. The mathematical model of the aqueous medium developed by the

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Kang, Seongwang, Han Kyu Lim, and Hyun seung Son. "An Automatic Labeling Method for Overlapping Paralichthys olivaceus Object Detection based on Image Processing Technique." Korean Institute of Smart Media 13, no. 11 (2024): 59–70. https://doi.org/10.30693/smj.2024.13.11.59.

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Detecting objects in underwater environments is challenging due to factors such as object overlap, noise, and lighting variations. In particular, labeling underwater organisms like Paralichthys olivaceus requires significant time and effort when done manually. To address these challenges, this paper proposes a method to automate the labeling of Paralichthys olivaceus using image processing techniques and to increase the size of the dataset for training. The proposed method applies morphological operations and contour detection to remove noise and accurately extract object boundaries. Additiona

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Wang, Zhenhua, Guangshi Zhang, Kuifeng Luan, Congqin Yi, and Mingjie Li. "Image-Fused-Guided Underwater Object Detection Model Based on Improved YOLOv7." Electronics 12, no. 19 (2023): 4064. http://dx.doi.org/10.3390/electronics12194064.

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Underwater object detection, as the principal means of underwater environmental sensing, plays a significant part in the marine economic, military, and ecological fields. Due to the degradation problems of underwater images caused by color cast, blurring, and low contrast, we proposed a model for underwater object detection based on YOLO v7. In the presented detection model, an enhanced image branch was constructed to expand the feature extraction branch of YOLOv7, which could mitigate the feature degradation issues existing in the original underwater images. The contextual transfer block was

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Qian, Jiahui, and Ming Chen. "WDS-YOLO: A Marine Benthos Detection Model Fusing Wavelet Convolution and Deformable Attention." Applied Sciences 15, no. 7 (2025): 3537. https://doi.org/10.3390/app15073537.

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Accurate marine benthos detection is a technical prerequisite for underwater robots to achieve automated fishing. Considering the challenges of poor underwater imaging conditions during the actual fishing process, where small objects are easily occluded or missed, we propose WDS-YOLO, an advanced model designed for marine benthos detection, built upon the YOLOv8n architecture. Firstly, the convolutional module incorporated with wavelet transform was used to enhance the backbone network, thereby expanding the receptive field of the model and enhancing its feature extraction ability for marine b

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Zu, Yunqin, Lixun Zhang, Siqi Li, Yuhe Fan, and Qijia Liu. "EF-UODA: Underwater Object Detection Based on Enhanced Feature." Journal of Marine Science and Engineering 12, no. 5 (2024): 729. http://dx.doi.org/10.3390/jmse12050729.

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The ability to detect underwater objects accurately is important in marine environmental engineering. Although many kinds of underwater object detection algorithms with relatively high accuracy have been proposed, they involve a large number of parameters and floating point operations (FLOPs), and often fail to yield satisfactory results in complex underwater environments. In light of the demand for an algorithm with the capability to extract high-quality features in complex underwater environments, we proposed a one-stage object detection algorithm called the enhanced feature-based underwater

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