An Underwater Target Wake Detection in Multi-Source Images Based on Improved YOLOv5

Shi, Yuchen

doi:10.1109/access.2023.3262703

Cited by 6 publications

(4 citation statements)

References 23 publications

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“…An improved YOLOv5 model was designed in [20] for detecting underwater target wakes in multi-source images. The model, enhanced with linear feature detection, distinguished between underwater and surface targets, as well as optical and infrared images.…”

Section: Related Workmentioning

confidence: 99%

HLASwin-T-ACoat-Net Based Underwater Object Detection

Manimurugan,

Narmatha,

Aborokbah

et al. 2024

IEEE Access

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Due to the limited light penetration in underwater environments, sonar equipment plays a crucial role in various commercial and military operations. However, underwater images often suffer from degradation due to scattering and absorption phenomena, resulting in poor visibility of submerged objects. To address this challenge, image enhancement techniques are essential for enhancing the appearance and visibility of underwater objects. This research proposes a novel approach called HLAST-ACNet, which combines the advantages of a hybrid Local Acuity Swin Transformer and an Adapted Coat-Net for Underwater Object Detection (UOD). The HLASwin-T-ACoat-Net leverages Contrast Limited Adaptive Histogram Equalization (CLAHE) to increase the quality of images. Additionally, it incorporates a path aggregation network to integrate deep and shallow feature maps and utilizes online complicated example mining to improve training efficiency. Furthermore, the algorithm improves Region of Interest (ROI) pooling by introducing ROI alignment, which mitigates quantization errors and enhances object detection accuracy. Compared to existing algorithms, the algorithms based on HLASTACNet demonstrate significant improvements in the URPC2018 and OUC datasets, achieving precision rates of 91.25% and 92.36%, respectively. The research model has a higher computational complexity than four existing methods, as evidenced by its GFLOPs, per-image processing time with a speed of 20ms, and the FPS measures for average processed frames per second reaching 2.28s. The research model effectively addressed the challenges and false detection with varying sizes of objects in complicated underwater environments.

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Section: Related Workmentioning

confidence: 99%

HLASwin-T-ACoat-Net Based Underwater Object Detection

Manimurugan,

Narmatha,

Aborokbah

et al. 2024

IEEE Access

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“…For example, Tang et al [24] proposed a moving object detection method based on dual-beam SAR that utilized two independent YOLO networks to fuse the sub-image results and improve detection accuracy. In tasks like aircraft detection in remote sensing photos [25], multi-source underwater object wake [26], and remote sensing building detection, the technique based on YOLOv5 proved accurate and effective [27]. FRN (Feature Refinement Network, FRN)-YOLO [28] was a feature refusion network for remote sensing object detection with high accuracy and robustness.…”

Section: Remote Sensing Object Detection Algorithm Of the Yolo Seriesmentioning

confidence: 99%

“…Shi et al [26] The improved YOLOv5 realizes the wake detection of underwater objects based on multisource images.…”

Section: Literature Paper Highlights Applicabilitymentioning

confidence: 99%

A Review: Remote Sensing Image Object Detection Algorithm Based on Deep Learning

Bai,

2023

Electronics

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Target detection in optical remote sensing images using deep-learning technologies has a wide range of applications in urban building detection, road extraction, crop monitoring, and forest fire monitoring, which provides strong support for environmental monitoring, urban planning, and agricultural management. This paper reviews the research progress of the YOLO series, SSD series, candidate region series, and Transformer algorithm. It summarizes the object detection algorithms based on standard improvement methods such as supervision, attention mechanism, and multi-scale. The performance of different algorithms is also compared and analyzed with the common remote sensing image data sets. Finally, future research challenges, improvement directions, and issues of concern are prospected, which provides valuable ideas for subsequent related research.

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“…Shi et al [19] proposed an improved YOLOv5 algorithm. The optical and thermal images were enhanced by data enhancement technology, which improved the generalization ability of the detection algorithm.…”

Section: Related Workmentioning

confidence: 99%

An Underwater Dense Small Object Detection Model Based on YOLOv5-CFDSDSE

Wang

et al. 2023

Electronics

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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, which improved the model’s accuracy while reducing the number of parameters. Then, Dyhead technology was adopted to achieve better scale perception, space perception, and task perception. In addition, the small object detection (SD) layer was added to combine feature information from different scales effectively, retain more detailed information, and improve the detection ability of small objects. Finally, the attention mechanism squeeze and excitation (SE) was introduced to enhance the feature extraction ability of the model. This paper used the self-made underwater small object dataset URPC_UODD for comparison and ablation experiments. The experimental results showed that the accuracy of the model proposed in this paper was better than the original YOLOv5s and other baseline models in the underwater dense small object detection task, and the number of parameters was also reduced compared to YOLOv5s. Therefore, YOLOv5-FCDSDSE was an innovative solution for underwater target detection tasks.

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An Underwater Target Wake Detection in Multi-Source Images Based on Improved YOLOv5

Cited by 6 publications

References 23 publications

HLASwin-T-ACoat-Net Based Underwater Object Detection

HLASwin-T-ACoat-Net Based Underwater Object Detection

A Review: Remote Sensing Image Object Detection Algorithm Based on Deep Learning

An Underwater Dense Small Object Detection Model Based on YOLOv5-CFDSDSE

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