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Journal articles on the topic 'Convolution dilatée'

Author: Grafiati

Published: 24 August 2024

Last updated: 31 July 2025

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1

Wang, Wei, Yiyang Hu, Ting Zou, Hongmei Liu, Jin Wang, and Xin Wang. "A New Image Classification Approach via Improved MobileNet Models with Local Receptive Field Expansion in Shallow Layers." Computational Intelligence and Neuroscience 2020 (August 1, 2020): 1–10. http://dx.doi.org/10.1155/2020/8817849.

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Because deep neural networks (DNNs) are both memory-intensive and computation-intensive, they are difficult to apply to embedded systems with limited hardware resources. Therefore, DNN models need to be compressed and accelerated. By applying depthwise separable convolutions, MobileNet can decrease the number of parameters and computational complexity with less loss of classification precision. Based on MobileNet, 3 improved MobileNet models with local receptive field expansion in shallow layers, also called Dilated-MobileNet (Dilated Convolution MobileNet) models, are proposed, in which dilat

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Peng, Wenli, Shenglai Zhen, Xin Chen, Qianjing Xiong, and Benli Yu. "Study on convolutional recurrent neural networks for speech enhancement in fiber-optic microphones." Journal of Physics: Conference Series 2246, no. 1 (2022): 012084. http://dx.doi.org/10.1088/1742-6596/2246/1/012084.

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Abstract In this paper, several improved convolutional recurrent networks (CRN) are proposed, which can enhance the speech with non-additive distortion captured by fiber-optic microphones. Our preliminary study shows that the original CRN structure based on amplitude spectrum estimation is seriously distorted due to the loss of phase information. Therefore, we transform the network to run in time domain and gain 0.42 improvement on PESQ and 0.03 improvement on STOI. In addition, we integrate dilated convolution into CRN architecture, and adopt three different types of bottleneck modules, namel

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Chim, Seyha, Jin-Gu Lee, and Ho-Hyun Park. "Dilated Skip Convolution for Facial Landmark Detection." Sensors 19, no. 24 (2019): 5350. http://dx.doi.org/10.3390/s19245350.

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Facial landmark detection has gained enormous interest for face-related applications due to its success in facial analysis tasks such as facial recognition, cartoon generation, face tracking and facial expression analysis. Many studies have been proposed and implemented to deal with the challenging problems of localizing facial landmarks from given images, including large appearance variations and partial occlusion. Studies have differed in the way they use the facial appearances and shape information of input images. In our work, we consider facial information within both global and local con

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Zhao, Feng, Junjie Zhang, Zhe Meng, and Hanqiang Liu. "Densely Connected Pyramidal Dilated Convolutional Network for Hyperspectral Image Classification." Remote Sensing 13, no. 17 (2021): 3396. http://dx.doi.org/10.3390/rs13173396.

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Recently, with the extensive application of deep learning techniques in the hyperspectral image (HSI) field, particularly convolutional neural network (CNN), the research of HSI classification has stepped into a new stage. To avoid the problem that the receptive field of naive convolution is small, the dilated convolution is introduced into the field of HSI classification. However, the dilated convolution usually generates blind spots in the receptive field, resulting in discontinuous spatial information obtained. In order to solve the above problem, a densely connected pyramidal dilated convo

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Ülkü, İrem. "Effect of dilation rate on Nested U-Net model performance in remote sensing." Communications Faculty of Sciences University of Ankara Series A2-A3 Physical Sciences and Engineering 67, no. 1 (2024): 27–42. https://doi.org/10.33769/aupse.1498035.

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High spatial resolution remote sensing images contain substantial detailed multi-scale objects. Convolutional neural networks (CNNs) are not efficient enough for detecting these objects of varying sizes. Among the multitude of CNN approaches, the Nested U-Net (UNet++) model shows great potential to capture more complex details by progressively enriching highresolution feature maps. However, there is more room for improving the Nested U-Net architecture by increasing its ability to detect multi-scale objects. The nested blocks used in this architecture rely on standard convolutional layers, whi

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Tang, Jingfan, Meijia Zhou, Pengfei Li, Min Zhang, and Ming Jiang. "Crowd Counting Based on Multiresolution Density Map and Parallel Dilated Convolution." Scientific Programming 2021 (January 20, 2021): 1–10. http://dx.doi.org/10.1155/2021/8831458.

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The current crowd counting tasks rely on a fully convolutional network to generate a density map that can achieve good performance. However, due to the crowd occlusion and perspective distortion in the image, the directly generated density map usually neglects the scale information and spatial contact information. To solve it, we proposed MDPDNet (Multiresolution Density maps and Parallel Dilated convolutions’ Network) to reduce the influence of occlusion and distortion on crowd estimation. This network is composed of two modules: (1) the parallel dilated convolution module (PDM) that combines

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Ma, Tian, Xinlei Zhou, Jiayi Yang, et al. "Dental Lesion Segmentation Using an Improved ICNet Network with Attention." Micromachines 13, no. 11 (2022): 1920. http://dx.doi.org/10.3390/mi13111920.

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Precise segmentation of tooth lesions is critical to creation of an intelligent tooth lesion detection system. As a solution to the problem that tooth lesions are similar to normal tooth tissues and difficult to segment, an improved segmentation method of the image cascade network (ICNet) network is proposed to segment various lesion types, such as calculus, gingivitis, and tartar. First, the ICNet network model is used to achieve real-time segmentation of lesions. Second, the Convolutional Block Attention Module (CBAM) is integrated into the ICNet network structure, and large-size convolution

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Song, Zhendong, Yupeng Ma, Fang Tan, and Xiaoyi Feng. "Hybrid Dilated and Recursive Recurrent Convolution Network for Time-Domain Speech Enhancement." Applied Sciences 12, no. 7 (2022): 3461. http://dx.doi.org/10.3390/app12073461.

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In this paper, we propose a fully convolutional neural network based on recursive recurrent convolution for monaural speech enhancement in the time domain. The proposed network is an encoder-decoder structure using a series of hybrid dilated modules (HDM). The encoder creates low-dimensional features of a noisy input frame. In the HDM, the dilated convolution is used to expand the receptive field of the network model. In contrast, the standard convolution is used to make up for the under-utilized local information of the dilated convolution. The decoder is used to reconstruct enhanced frames.

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Viriyasaranon, Thanaporn, Seung-Hoon Chae, and Jang-Hwan Choi. "MFA-net: Object detection for complex X-ray cargo and baggage security imagery." PLOS ONE 17, no. 9 (2022): e0272961. http://dx.doi.org/10.1371/journal.pone.0272961.

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Deep convolutional networks have been developed to detect prohibited items for automated inspection of X-ray screening systems in the transport security system. To our knowledge, the existing frameworks were developed to recognize threats using only baggage security X-ray scans. Therefore, the detection accuracy in other domains of security X-ray scans, such as cargo X-ray scans, cannot be ensured. We propose an object detection method for efficiently detecting contraband items in both cargo and baggage for X-ray security scans. The proposed network, MFA-net, consists of three plug-and-play mo

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Zhang, Jianming, Chaoquan Lu, Jin Wang, Lei Wang, and Xiao-Guang Yue. "Concrete Cracks Detection Based on FCN with Dilated Convolution." Applied Sciences 9, no. 13 (2019): 2686. http://dx.doi.org/10.3390/app9132686.

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In civil engineering, the stability of concrete is of great significance to safety of people’s life and property, so it is necessary to detect concrete damage effectively. In this paper, we treat crack detection on concrete surface as a semantic segmentation task that distinguishes background from crack at the pixel level. Inspired by Fully Convolutional Networks (FCN), we propose a full convolution network based on dilated convolution for concrete crack detection, which consists of an encoder and a decoder. Specifically, we first used the residual network to extract the feature maps of the in

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Rahman, Takowa, Md Saiful Islam, and Jia Uddin. "MRI-Based Brain Tumor Classification Using a Dilated Parallel Deep Convolutional Neural Network." Digital 4, no. 3 (2024): 529–54. http://dx.doi.org/10.3390/digital4030027.

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Brain tumors are frequently classified with high accuracy using convolutional neural networks (CNNs) to better comprehend the spatial connections among pixels in complex pictures. Due to their tiny receptive fields, the majority of deep convolutional neural network (DCNN)-based techniques overfit and are unable to extract global context information from more significant regions. While dilated convolution retains data resolution at the output layer and increases the receptive field without adding computation, stacking several dilated convolutions has the drawback of producing a grid effect. Thi

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Cao, Ruifen, Xi Pei, Ning Ge, and Chunhou Zheng. "Clinical Target Volume Auto-Segmentation of Esophageal Cancer for Radiotherapy After Radical Surgery Based on Deep Learning." Technology in Cancer Research & Treatment 20 (January 1, 2021): 153303382110342. http://dx.doi.org/10.1177/15330338211034284.

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Radiotherapy plays an important role in controlling the local recurrence of esophageal cancer after radical surgery. Segmentation of the clinical target volume is a key step in radiotherapy treatment planning, but it is time-consuming and operator-dependent. This paper introduces a deep dilated convolutional U-network to achieve fast and accurate clinical target volume auto-segmentation of esophageal cancer after radical surgery. The deep dilated convolutional U-network, which integrates the advantages of dilated convolution and the U-network, is an end-to-end architecture that enables rapid t

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Yan, Yang, Liu Yang, and Wenbo Huang. "Fundus-DANet: Dilated Convolution and Fusion Attention Mechanism for Multilabel Retinal Fundus Image Classification." Applied Sciences 14, no. 18 (2024): 8446. http://dx.doi.org/10.3390/app14188446.

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The difficulty of classifying retinal fundus images with one or more illnesses present or missing is known as fundus multi-lesion classification. The challenges faced by current approaches include the inability to extract comparable morphological features from images of different lesions and the inability to resolve the issue of the same lesion, which presents significant feature variances due to grading disparities. This paper proposes a multi-disease recognition network model, Fundus-DANet, based on the dilated convolution. It has two sub-modules to address the aforementioned issues: the int

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Yang, Xing-Yao, Shao-Dong Zhang, Rui Xiao, Jiong Yu, and Zi-Yang Li. "Speech Recognition of Accented Mandarin Based on Improved Conformer." Sensors 23, no. 8 (2023): 4025. http://dx.doi.org/10.3390/s23084025.

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The convolution module in Conformer is capable of providing translationally invariant convolution in time and space. This is often used in Mandarin recognition tasks to address the diversity of speech signals by treating the time-frequency maps of speech signals as images. However, convolutional networks are more effective in local feature modeling, while dialect recognition tasks require the extraction of a long sequence of contextual information features; therefore, the SE-Conformer-TCN is proposed in this paper. By embedding the squeeze-excitation block into the Conformer, the interdependen

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Wang, Ran, Ruyu Shi, Xiong Hu, and Changqing Shen. "Remaining Useful Life Prediction of Rolling Bearings Based on Multiscale Convolutional Neural Network with Integrated Dilated Convolution Blocks." Shock and Vibration 2021 (January 25, 2021): 1–11. http://dx.doi.org/10.1155/2021/6616861.

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Remaining useful life (RUL) prediction is necessary for guaranteeing machinery’s safe operation. Among deep learning architectures, convolutional neural network (CNN) has shown achievements in RUL prediction because of its strong ability in representation learning. Features from different receptive fields extracted by different sizes of convolution kernels can provide complete information for prognosis. The single size convolution kernel in traditional CNN is difficult to learn comprehensive information from complex signals. Besides, the ability to learn local and global features synchronously

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Yang, Hongbo, and Shi Qiu. "A Novel Dynamic Contextual Feature Fusion Model for Small Object Detection in Satellite Remote-Sensing Images." Information 15, no. 4 (2024): 230. http://dx.doi.org/10.3390/info15040230.

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Ground objects in satellite images pose unique challenges due to their low resolution, small pixel size, lack of texture features, and dense distribution. Detecting small objects in satellite remote-sensing images is a difficult task. We propose a new detector focusing on contextual information and multi-scale feature fusion. Inspired by the notion that surrounding context information can aid in identifying small objects, we propose a lightweight context convolution block based on dilated convolutions and integrate it into the convolutional neural network (CNN). We integrate dynamic convolutio

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Ho, David Joon, and Qian Lin. "Person Segmentation Using Convolutional Neural Networks With Dilated Convolutions." Electronic Imaging 2018, no. 10 (2018): 455–1. http://dx.doi.org/10.2352/issn.2470-1173.2018.10.imawm-455.

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Contreras, Jonatan, Martine Ceberio, and Vladik Kreinovich. "Why Dilated Convolutional Neural Networks: A Proof of Their Optimality." Entropy 23, no. 6 (2021): 767. http://dx.doi.org/10.3390/e23060767.

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One of the most effective image processing techniques is the use of convolutional neural networks that use convolutional layers. In each such layer, the value of the layer’s output signal at each point is a combination of the layer’s input signals corresponding to several neighboring points. To improve the accuracy, researchers have developed a version of this technique, in which only data from some of the neighboring points is processed. It turns out that the most efficient case—called dilated convolution—is when we select the neighboring points whose differences in both coordinates are divis

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Hu, Guoping, Fangzheng Zhao, and Bingqi Liu. "Estimation of the Two-Dimensional Direction of Arrival for Low-Elevation and Non-Low-Elevation Targets Based on Dilated Convolutional Networks." Remote Sensing 15, no. 12 (2023): 3117. http://dx.doi.org/10.3390/rs15123117.

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This paper addresses the problem of the two-dimensional direction-of-arrival (2D DOA) estimation of low-elevation or non-low-elevation targets using L-shaped uniform and sparse arrays by analyzing the signal models’ features and their mapping to 2D DOA. This paper proposes a 2D DOA estimation algorithm based on the dilated convolutional network model, which consists of two components: a dilated convolutional autoencoder and a dilated convolutional neural network. If there are targets at low elevation, the dilated convolutional autoencoder suppresses the multipath signal and outputs a new signa

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Lu, Yongzong, Pengfei Liu, and Chong Tan. "MA-YOLO: A Pest Target Detection Algorithm with Multi-Scale Fusion and Attention Mechanism." Agronomy 15, no. 7 (2025): 1549. https://doi.org/10.3390/agronomy15071549.

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Agricultural pest detection is critical for crop protection and food security, yet existing methods suffer from low computational efficiency and poor generalization due to imbalanced data distribution, minimal inter-class variations among pest categories, and significant intra-class differences. To address the high computational complexity and inadequate feature representation in traditional convolutional networks, this study proposes MA-YOLO, an agricultural pest detection model based on multi-scale fusion and attention mechanisms. The SDConv module reduces computational costs through depthwi

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Hu, Yicheng, Shufang Tian, and Jia Ge. "Hybrid Convolutional Network Combining Multiscale 3D Depthwise Separable Convolution and CBAM Residual Dilated Convolution for Hyperspectral Image Classification." Remote Sensing 15, no. 19 (2023): 4796. http://dx.doi.org/10.3390/rs15194796.

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In recent years, convolutional neural networks (CNNs) have been increasingly leveraged for the classification of hyperspectral imagery, displaying notable advancements. To address the issues of insufficient spectral and spatial information extraction and high computational complexity in hyperspectral image classification, we introduce the MDRDNet, an integrated neural network model. This novel architecture is comprised of two main components: a Multiscale 3D Depthwise Separable Convolutional Network and a CBAM-augmented Residual Dilated Convolutional Network. The first component employs depthw

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C, Victoria Priscilla, and Hemamalini V. "A Three-Component Feature Extraction Using DDS_SE-NET for Efficient Deep Learning-Based Image Steganalysis for Real-World Images." Indian Journal of Science and Technology 17, no. 32 (2024): 3335–43. https://doi.org/10.17485/IJST/v17i32.1870.

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Abstract <strong>Objectives:</strong> The main objective of the work is to ensure security by creating an architecture for steganalysis to detect real-world stego images. The accuracy of Major previous works using real-world datasets varies. This architecture works well with real-world datasets. <strong>Methods:</strong> This study introduces DDS_SE-Net (Dilated Depthwise Separable convolutions with Squeeze and Excitation-Net), a CNN-based Image Steganalysis architecture, which combines the power of Dilation and Depthwise Separable Convolutions in the Feature Extraction (FE) sta

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Wang, Yanjie, Shiyu Hu, Guodong Wang, Chenglizhao Chen, and Zhenkuan Pan. "Multi-scale dilated convolution of convolutional neural network for crowd counting." Multimedia Tools and Applications 79, no. 1-2 (2019): 1057–73. http://dx.doi.org/10.1007/s11042-019-08208-6.

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Wang, Yanjie, Guodong Wang, Chenglizhao Chen, and Zhenkuan Pan. "Multi-scale dilated convolution of convolutional neural network for image denoising." Multimedia Tools and Applications 78, no. 14 (2019): 19945–60. http://dx.doi.org/10.1007/s11042-019-7377-y.

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Heo, Woon-Haeng, Hyemi Kim, and Oh-Wook Kwon. "Source Separation Using Dilated Time-Frequency DenseNet for Music Identification in Broadcast Contents." Applied Sciences 10, no. 5 (2020): 1727. http://dx.doi.org/10.3390/app10051727.

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We propose a source separation architecture using dilated time-frequency DenseNet for background music identification of broadcast content. We apply source separation techniques to the mixed signals of music and speech. For the source separation purpose, we propose a new architecture to add a time-frequency dilated convolution to the conventional DenseNet in order to effectively increase the receptive field in the source separation scheme. In addition, we apply different convolutions to each frequency band of the spectrogram in order to reflect the different frequency characteristics of the lo

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Zhang, Guokai, Xiao Liu, Dandan Zhu, et al. "3D Spatial Pyramid Dilated Network for Pulmonary Nodule Classification." Symmetry 10, no. 9 (2018): 376. http://dx.doi.org/10.3390/sym10090376.

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Lung cancer mortality is currently the highest among all kinds of fatal cancers. With the help of computer-aided detection systems, a timely detection of malignant pulmonary nodule at early stage could improve the patient survival rate efficiently. However, the sizes of the pulmonary nodules are usually various, and it is more difficult to detect small diameter nodules. The traditional convolution neural network uses pooling layers to reduce the resolution progressively, but it hampers the network’s ability to capture the tiny but vital features of the pulmonary nodules. To tackle this problem

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Xu, Jiawei, Jie Wu, Yu Lei, and Yuxiang Gu. "Application of Pseudo-Three-Dimensional Residual Network to Classify the Stages of Moyamoya Disease." Brain Sciences 13, no. 5 (2023): 742. http://dx.doi.org/10.3390/brainsci13050742.

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It is essential to assess the condition of moyamoya disease (MMD) patients accurately and promptly to prevent MMD from endangering their lives. A Pseudo-Three-Dimensional Residual Network (P3D ResNet) was proposed to process spatial and temporal information, which was implemented in the identification of MMD stages. Digital Subtraction Angiography (DSA) sequences were split into mild, moderate and severe stages in accordance with the progression of MMD, and divided into a training set, a verification set, and a test set with a ratio of 6:2:2 after data enhancement. The features of the DSA imag

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Wang Aili, 王爱丽, 张宇枭 Zhang Yuxiao, 吴海滨 Wu Haibin, 姜开元 Jiang Kaiyuan та 岩堀祐之 Iwahori Yuji. "基于空洞卷积胶囊网络的激光雷达数据分类". Chinese Journal of Lasers 48, № 11 (2021): 1110003. http://dx.doi.org/10.3788/cjl202148.1110003.

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Jin, Ran, Xiaozhen Han, and Tongrui Yu. "A Real-Time Image Semantic Segmentation Method Based on Multilabel Classification." Mathematical Problems in Engineering 2021 (May 31, 2021): 1–13. http://dx.doi.org/10.1155/2021/9963974.

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Image semantic segmentation as a kind of technology has been playing a crucial part in intelligent driving, medical image analysis, video surveillance, and AR. However, since the scene needs to infer more semantics from video and audio clips and the request for real-time performance becomes stricter, whetherthe single-label classification method that was usually used before or the regular manual labeling cannot meet this end. Given the excellent performance of deep learning algorithms in extensive applications, the image semantic segmentation algorithm based on deep learning framework has been

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Renton, Guillaume, Yann Soullard, Clément Chatelain, Sébastien Adam, Christopher Kermorvant, and Thierry Paquet. "Fully convolutional network with dilated convolutions for handwritten text line segmentation." International Journal on Document Analysis and Recognition (IJDAR) 21, no. 3 (2018): 177–86. http://dx.doi.org/10.1007/s10032-018-0304-3.

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Khotimah, Wijayanti Nurul, Farid Boussaid, Ferdous Sohel, et al. "SC-CAN: Spectral Convolution and Channel Attention Network for Wheat Stress Classification." Remote Sensing 14, no. 17 (2022): 4288. http://dx.doi.org/10.3390/rs14174288.

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Biotic and abiotic plant stress (e.g., frost, fungi, diseases) can significantly impact crop production. It is thus essential to detect such stress at an early stage before visual symptoms and damage become apparent. To this end, this paper proposes a novel deep learning method, called Spectral Convolution and Channel Attention Network (SC-CAN), which exploits the difference in spectral responses of healthy and stressed crops. The proposed SC-CAN method comprises two main modules: (i) a spectral convolution module, which consists of dilated causal convolutional layers stacked in a residual man

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You, Jiangchuan, and Zhenhong Shang. "Solar Filament Detection Based on Improved DeepLab V3+." Publications of the Astronomical Society of the Pacific 134, no. 1036 (2022): 064501. http://dx.doi.org/10.1088/1538-3873/ac6e07.

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Abstract A novel solar filament detection method based on an improved DeepLab V3+ is proposed to address the low detection accuracy of small solar filaments in Hα full-disk solar images. First, the Xception structure of the backbone network is fine-tuned, and the low-level feature information of the filaments is added to the decoder module of the network to improve the utilization of the solar filament features. Second, the receptive field of dilated convolution is expanded, and the information utilization rate is increased via cascaded dilated convolution to improve the detection accuracy of

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Wu, Junjie, Wen Liu, and Yoshihisa Maruyama. "Automated Road-Marking Segmentation via a Multiscale Attention-Based Dilated Convolutional Neural Network Using the Road Marking Dataset." Remote Sensing 14, no. 18 (2022): 4508. http://dx.doi.org/10.3390/rs14184508.

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Road markings, including road lanes and symbolic road markings, can convey abundant guidance information to autonomous driving cars. However, recent works have paid less attention to the recognition of symbolic road markings compared with road lanes. In this study, a road-marking-segmentation dataset named the RMD (Road Marking Dataset) is introduced to compensate for the lack of datasets and the limitations of the existing datasets. Furthermore, we propose a novel multiscale attention-based dilated convolutional neural network (MSA-DCNN) to tackle the proposed RMD. The proposed method employs

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Qin, Yanjun, Haiyong Luo, Fang Zhao, Chenxing Wang, and Yuchen Fang. "NDGCN: Network in Network, Dilate Convolution and Graph Convolutional Networks Based Transportation Mode Recognition." IEEE Transactions on Vehicular Technology 70, no. 3 (2021): 2138–52. http://dx.doi.org/10.1109/tvt.2021.3060761.

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Nguyen, Tuan Anh, and Thanh Ngoc Tran. "Improving Short-Term Electrical Load Forecasting with Dilated Convolutional Neural Networks: A Comparative Analysis." Journal of Robotics and Control (JRC) 6, no. 2 (2025): 560–69. https://doi.org/10.18196/jrc.v6i2.24967.

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Short-term load forecasting (STLF) is vital for grid stability and resource optimization for energy systems. Accurate forecasting helps maintain a stable power supply, reduce costs, and improve decision-making. Traditional convolutional neural networks (CNNs) capture local patterns well but struggle with long-term dependencies under fluctuating conditions. This study introduces an optimized Dilated Convolutional Neural Network (DCNN) to enhance accuracy in short- and long-term load forecasting. The key contribution is a new DCNN framework that expands the receptive field without adding computa

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Zhou, Yuepeng, Huiyou Chang, Yonghe Lu, and Xili Lu. "CDTNet: Improved Image Classification Method Using Standard, Dilated and Transposed Convolutions." Applied Sciences 12, no. 12 (2022): 5984. http://dx.doi.org/10.3390/app12125984.

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Convolutional neural networks (CNNs) have achieved great success in image classification tasks. In the process of a convolutional operation, a larger input area can capture more context information. Stacking several convolutional layers can enlarge the receptive field, but this increases the parameters. Most CNN models use pooling layers to extract important features, but the pooling operations cause information loss. Transposed convolution can increase the spatial size of the feature maps to recover the lost low-resolution information. In this study, we used two branches with different dilate

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Zhao, Haixia, You Zhou, Tingting Bai, and Yuanzhong Chen. "A U-Net Based Multi-Scale Deformable Convolution Network for Seismic Random Noise Suppression." Remote Sensing 15, no. 18 (2023): 4569. http://dx.doi.org/10.3390/rs15184569.

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Seismic data processing plays a key role in the field of geophysics. The collected seismic data are inevitably contaminated by various types of noise, which makes the effective signals difficult to be accurately discriminated. A fundamental issue is how to improve the signal-to-noise ratio of seismic data. Due to the complex characteristics of noise and signals, it is a challenge for the denoising model to suppress noise and recover weak signals. To suppress random noise in seismic data, we propose a multi-scale deformable convolution neural network denoising model based on U-Net, named MSDC-U

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Xiang, Zhenwu, Qi Mao, Jintao Wang, Yi Tian, Yan Zhang, and Wenfeng Wang. "Dmbg-Net: Dilated multiresidual boundary guidance network for COVID-19 infection segmentation." Mathematical Biosciences and Engineering 20, no. 11 (2023): 20135–54. http://dx.doi.org/10.3934/mbe.2023892.

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<abstract> <p>Accurate segmentation of infected regions in lung computed tomography (CT) images is essential for the detection and diagnosis of coronavirus disease 2019 (COVID-19). However, lung lesion segmentation has some challenges, such as obscure boundaries, low contrast and scattered infection areas. In this paper, the dilated multiresidual boundary guidance network (Dmbg-Net) is proposed for COVID-19 infection segmentation in CT images of the lungs. This method focuses on semantic relationship modelling and boundary detail guidance. First, to effectively minimize the loss of

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Ku, Tao, Qirui Yang, and Hao Zhang. "Multilevel feature fusion dilated convolutional network for semantic segmentation." International Journal of Advanced Robotic Systems 18, no. 2 (2021): 172988142110076. http://dx.doi.org/10.1177/17298814211007665.

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Recently, convolutional neural network (CNN) has led to significant improvement in the field of computer vision, especially the improvement of the accuracy and speed of semantic segmentation tasks, which greatly improved robot scene perception. In this article, we propose a multilevel feature fusion dilated convolution network (Refine-DeepLab). By improving the space pyramid pooling structure, we propose a multiscale hybrid dilated convolution module, which captures the rich context information and effectively alleviates the contradiction between the receptive field size and the dilated convol

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Zhuang, Zilong, Huichun Lv, Jie Xu, Zizhao Huang, and Wei Qin. "A Deep Learning Method for Bearing Fault Diagnosis through Stacked Residual Dilated Convolutions." Applied Sciences 9, no. 9 (2019): 1823. http://dx.doi.org/10.3390/app9091823.

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Real-time monitoring and fault diagnosis of bearings are of great significance to improve production safety, prevent major accidents, and reduce production costs. However, there are three primary concerns in the current research, namely real-time performance, effectiveness, and generalization performance. In this paper, a deep learning method based on stacked residual dilated convolutional neural network (SRDCNN) is proposed for real-time bearing fault diagnosis, which is subtly combined by the dilated convolution, the input gate structure of long short-term memory network (LSTM) and the resid

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Ni, Jian, Rui Wang, and Jing Tang. "ADSSD: Improved Single-Shot Detector with Attention Mechanism and Dilated Convolution." Applied Sciences 13, no. 6 (2023): 4038. http://dx.doi.org/10.3390/app13064038.

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The detection of small objects is easily affected by background information, and a lack of context information makes detection difficult. Therefore, small object detection has become an extremely challenging task. Based on the above problems, we proposed a Single-Shot MultiBox Detector with an attention mechanism and dilated convolution (ADSSD). In the attention module, we strengthened the connection between information in space and channels while using cross-layer connections to accelerate training. In the multi-branch dilated convolution module, we combined three expansion convolutions with

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Priscilla, C. Victoria, and V. Hemamalini. "A Three-Component Feature Extraction Using DDS_SE-NET for Efficient Deep Learning-Based Image Steganalysis for Real-World Images." Indian Journal Of Science And Technology 17, no. 32 (2024): 3335–43. http://dx.doi.org/10.17485/ijst/v17i32.1870.

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Objectives: The main objective of the work is to ensure security by creating an architecture for steganalysis to detect real-world stego images. The accuracy of Major previous works using real-world datasets varies. This architecture works well with real-world datasets. Methods: This study introduces DDS_SE-Net (Dilated Depthwise Separable convolutions with Squeeze and Excitation-Net), a CNN-based Image Steganalysis architecture, which combines the power of Dilation and Depthwise Separable Convolutions in the Feature Extraction (FE) stage which deals with the specified issues in image steganal

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Ma, Hao, Chao Chen, Qing Zhu, Haitao Yuan, Liming Chen, and Minglei Shu. "An ECG Signal Classification Method Based on Dilated Causal Convolution." Computational and Mathematical Methods in Medicine 2021 (February 2, 2021): 1–10. http://dx.doi.org/10.1155/2021/6627939.

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The incidence of cardiovascular disease is increasing year by year and is showing a younger trend. At the same time, existing medical resources are tight. The automatic detection of ECG signals becomes increasingly necessary. This paper proposes an automatic classification of ECG signals based on a dilated causal convolutional neural network. To solve the problem that the recurrent neural network framework network cannot be accelerated by hardware equipment, the dilated causal convolutional neural network is adopted. Given the features of the same input and output time steps of the recurrent n

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Zhu, Yiqun, Guojian Jin, Tongfei Liu, et al. "Self-Attention and Convolution Fusion Network for Land Cover Change Detection over a New Data Set in Wenzhou, China." Remote Sensing 14, no. 23 (2022): 5969. http://dx.doi.org/10.3390/rs14235969.

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With the process of increasing urbanization, there is great significance in obtaining urban change information by applying land cover change detection techniques. However, these existing methods still struggle to achieve convincing performances and are insufficient for practical applications. In this paper, we constructed a new data set, named Wenzhou data set, aiming to detect the land cover changes of Wenzhou City and thus update the urban expanding geographic data. Based on this data set, we provide a new self-attention and convolution fusion network (SCFNet) for the land cover change detec

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Wei, Liran, Mingzhu Tang, Na Li, Jingwen Deng, Xinpeng Zhou, and Haijun Hu. "Multifractal-Aware Convolutional Attention Synergistic Network for Carbon Market Price Forecasting." Fractal and Fractional 9, no. 7 (2025): 449. https://doi.org/10.3390/fractalfract9070449.

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Accurate carbon market price prediction is crucial for promoting a low-carbon economy and sustainable engineering. Traditional models often face challenges in effectively capturing the multifractality inherent in carbon market prices. Inspired by the self-similarity and scale invariance inherent in fractal structures, this study proposes a novel multifractal-aware model, MF-Transformer-DEC, for carbon market price prediction. The multi-scale convolution (MSC) module employs multi-layer dilated convolutions constrained by shared convolution kernel weights to construct a scale-invariant convolut

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Shen, Sheng, Honghui Yang, Xiaohui Yao, Junhao Li, Guanghui Xu, and Meiping Sheng. "Ship Type Classification by Convolutional Neural Networks with Auditory-Like Mechanisms." Sensors 20, no. 1 (2020): 253. http://dx.doi.org/10.3390/s20010253.

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Ship type classification with radiated noise helps monitor the noise of shipping around the hydrophone deployment site. This paper introduces a convolutional neural network with several auditory-like mechanisms for ship type classification. The proposed model mainly includes a cochlea model and an auditory center model. In cochlea model, acoustic signal decomposition at basement membrane is implemented by time convolutional layer with auditory filters and dilated convolutions. The transformation of neural patterns at hair cells is modeled by a time frequency conversion layer to extract auditor

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Deng, Jiaqi, Chenglong Sun, Xin Liu, Gang Du, Liangzhong Jiang, and Xu Yang. "High-Frequency Workpiece Image Recognition Model Based on Hybrid Attention Mechanism." Applied Sciences 15, no. 1 (2024): 94. https://doi.org/10.3390/app15010094.

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High-frequency workpieces are specialized items characterized by complex internal textures and minimal variance in properties. Under intricate lighting conditions, existing mainstream image recognition models struggle with low precision when applied to the identification of high-frequency workpiece images. This paper introduces a high-frequency workpiece image recognition model based on a hybrid attention mechanism, HAEN. Initially, the high-frequency workpiece dataset is enhanced through geometric transformations, random noise, and random lighting adjustments to augment the model’s generaliza

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Guo, Yuheng, Wei Zhan, Zhiliang Zhang, Yu Zhang, and Hongshen Guo. "FRPNet: A Lightweight Multi-Altitude Field Rice Panicle Detection and Counting Network Based on Unmanned Aerial Vehicle Images." Agronomy 15, no. 6 (2025): 1396. https://doi.org/10.3390/agronomy15061396.

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Rice panicle detection is a key technology for improving rice yield and agricultural management levels. Traditional manual counting methods are labor-intensive and inefficient, making them unsuitable for large-scale farmlands. This paper proposes FRPNet, a novel lightweight convolutional neural network optimized for multi-altitude rice panicle detection in UAV images. The architecture integrates three core innovations: a CSP-ScConv backbone with self-calibrating convolutions for efficient multi-scale feature extraction; a Feature Pyramid Shared Convolution (FPSC) module that replaces pooling w

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Xie, Wen, Licheng Jiao, and Wenqiang Hua. "Complex-Valued Multi-Scale Fully Convolutional Network with Stacked-Dilated Convolution for PolSAR Image Classification." Remote Sensing 14, no. 15 (2022): 3737. http://dx.doi.org/10.3390/rs14153737.

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Polarimetric synthetic aperture radar (PolSAR) image classification is a pixel-wise issue, which has become increasingly prevalent in recent years. As a variant of the Convolutional Neural Network (CNN), the Fully Convolutional Network (FCN), which is designed for pixel-to-pixel tasks, has obtained enormous success in semantic segmentation. Therefore, effectively using the FCN model combined with polarimetric characteristics for PolSAR image classification is quite promising. This paper proposes a novel FCN model by adopting complex-valued domain stacked-dilated convolution (CV-SDFCN). Firstly

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Liu, Junwen, Yongjun Zhang, Jianbin Xie, Yan Wei, Zewei Wang, and Mengjia Niu. "Head Detection Based on DR Feature Extraction Network and Mixed Dilated Convolution Module." Electronics 10, no. 13 (2021): 1565. http://dx.doi.org/10.3390/electronics10131565.

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Pedestrian detection for complex scenes suffers from pedestrian occlusion issues, such as occlusions between pedestrians. As well-known, compared with the variability of the human body, the shape of a human head and their shoulders changes minimally and has high stability. Therefore, head detection is an important research area in the field of pedestrian detection. The translational invariance of neural network enables us to design a deep convolutional neural network, which means that, even if the appearance and location of the target changes, it can still be recognized effectively. However, t

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