A Robust and Lightweight Detector for Ship Target with Complex Background in SAR Image

Wang, Hengtao; Zhang, Shang

doi:10.1155/2022/8199418

Cited by 3 publications

(3 citation statements)

References 20 publications

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“…A novel target detection method was proposed in [32] to reduce the SAR ship detection time and space complexity. The 3S-YOLO network was proposed in [33] to improve the real-time performance of model detection. 3S-YOLO is a lightweight feature extraction and fusion network, ensuring the model's detection accuracy.…”

Section: Introductionmentioning

confidence: 99%

Detection of SAR Image Multiscale Ship Targets in Complex Inshore Scenes Based on Improved YOLOv5

Wang,

Hou,

Xin

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Synthetic aperture radar (SAR) can operate around the clock and in all weather, and therefore high-resolution SAR images have been frequently applied for ship inspection. However, current ship target detection and identification methods have limited detection accuracy and lead to missing detection of small targets due to speckle noise caused by the imaging principle of SAR imagery and complex nearshore interference. Therefore, this paper proposes an improved YOLOv5 method to address the problem of low accuracy in multi-ship target detection tasks in complex scenes. The developed scheme enhances the ship target detection performance while reducing the number of parameters. Specifically, first, we improve the size of the input SAR images and optimize the anchor frames of ship targets in the training dataset to locate small target ships more accurately. Then, symmetric pyramidal non-local block(APNB) and Sim attention mechanism (SimAM) are introduced to reduce near-shore background interference. Additionally, to make the C3 module output richer and with more feature information, channel shuffling is performed after the C3 output to enhance the information exchange between channels. Finally, to reduce the number of parameters and computational cost during model training, the normal convolution in the NECK part is replaced with Ghost convolution. The index F1 of the proposed method on the High-Resolution SAR Image Dataset (HRSID) and SAR Ship Detection Dataset (SSDD) reaches the highest of 91.3% and 95.8%, respectively. MAPs (0.5:0.95) for the two datasets are both the highest, which are at least 2% higher than the suboptimal method. In selected specific inshore scenes, the ship detection performance of the proposed method outperforms current advanced methods for multiscale ships. It is shown that the proposed method can extract ship features effectively in complex scenes and its effectiveness is further validated on the large scene AIR-SARShip-1 dataset.

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Section: Introductionmentioning

confidence: 99%

Detection of SAR Image Multiscale Ship Targets in Complex Inshore Scenes Based on Improved YOLOv5

Wang,

Hou,

Xin

et al. 2024

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Despite the better performance of deep learning approaches compared to traditional methods, SAR image ship detection still faces a number of challenges [43,44], including the following:…”

Section: Introductionmentioning

confidence: 99%

A new method of ship detection in complex background of SAR images based on YOLO

Zhang,

Huo

2024

JCEIM

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Synthetic Aperture Radar (SAR) is a remote sensing technology capable of high-resolution imaging of the ground surface under any weather and light conditions. SAR image ship detection is a technique that uses the ship target features in SAR images to identify and locate ships, which is of great value for applications in the fields of maritime traffic management, marine environment monitoring, and maritime security and defence. However, SAR image ship detection also faces some challenges, such as complex sea surface background, low signal-to-noise ratio, and the diversity and density of ship targets, which lead to the shortcomings of traditional feature extraction and classifier-based detection methods in terms of accuracy and efficiency. To address these challenges, this paper proposes a new method for ship detection in SAR images based on YOLOv7. When dealing with SAR images containing clutter interference and complex backgrounds, many models face the dual challenge of insufficient real-time and accuracy. To address this problem, this study proposes a new PCAN aggregation network, which firstly introduces the SimAM attention mechanism, which significantly enhances the model's ability to identify the differences between ships and background clutter, further improves the ability of focusing on target features, and effectively It further improves the ability to focus on target features, effectively reduces the interference of background noise, and improves the detection accuracy in complex scenes. At the same time, the PConv strategy is added to further lighten the model structure, thus accelerating the computation process and improving the inference efficiency. We trained and tested the model on the SAR-Ship-Dataset dataset, and achieved an accuracy of 91.1%, an F1 score of 92.9, and an FPS of 46, demonstrating its excellent detection capability. By comparing and analysing the model with several classical and cutting-edge methods in the current field, the proposed model in this study demonstrates obvious performance advantages, and in addition, through ablation experiments, we further confirm the effectiveness and contribution of PCAN.

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“…With the continuous development of computer technology, object detection algorithm based on deep learning have achieved rapid development and has been widely used in autonomous driving, face recognition, crop disease and pest recognition, defect detection, and other fields [5][6][7]. There are two main types of object detection algorithms based on deep learning: One is a two-stage target detection algorithm that divides feature extraction and target localization into two stages, such as R-CNN [8,9] (Region Proposals for Convolutional Neural Networks), Fast R-CNN [10], and Faster R-CNN [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Research on Asphalt Pavement Disease Detection Based on Improved YOLOv5s

Zhugeng

Liang

2023

Journal of Sensors

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Pavement disease detection and classification is one of the key problems in computer vision and intelligent analysis. This is an automated target detection technology with great development potential, which can improve the detection efficiency of road management departments. The research based on the convolutional neural network is aimed at realizing asphalt pavement disease detection based on low resolution, occlusive interference, and complex environment. Considering the powerful function of the convolutional neural network and its successful application in object detection, we apply it to asphalt pavement disease detection, and the detection results are used for subsequent analysis and decision-making. At present, most of the research on pavement disease detection focuses on crack detection, and the detection of multiclass diseases is less, and its detection accuracy and speed need to be improved, which does not meet the actual engineering application. Therefore, a rapid asphalt pavement disease detection method based on improved YOLOv5s was proposed. The complex scene data enhancement technique was developed, which is used to enhance and extend the original data to improve the robustness of the model. The improved lightweight attention module SCBAM was integrated into the backbone network, which can enhance the feature extraction ability and improve the detection performance of the model for small targets. The spatial pyramid pooling was improved into SPPF to fuse the input features, which can solve the multiscale problem of the target and improve the reasoning efficiency of the model to a certain extent. The experimental results showed that, after the model is improved, the average accuracy of pavement disease reaches 94.0%. Compared with YOLOv5s, the precision of the improved YOLOv5-pavement is increased by 3.1%, the recall rate is increased by 4.4%, the F1 score is increased by 3.7%, and the mAP is increased by 3.8%. For transverse cracks, longitudinal cracks, mesh cracks, potholes, and repaired pavement, the detection accuracy of pavement disease detection method based on YOLOv5-pavement is improved by 3.4%, 3.1%, 4.0%, 7.5%, and 4.8%, respectively, compared with that based on YOLOv5s. The proposed method provides support for the detection work of pavement diseases.

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A Robust and Lightweight Detector for Ship Target with Complex Background in SAR Image

Cited by 3 publications

References 20 publications

Detection of SAR Image Multiscale Ship Targets in Complex Inshore Scenes Based on Improved YOLOv5

Detection of SAR Image Multiscale Ship Targets in Complex Inshore Scenes Based on Improved YOLOv5

A new method of ship detection in complex background of SAR images based on YOLO

Research on Asphalt Pavement Disease Detection Based on Improved YOLOv5s

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