Shallow mud detection algorithm for submarine channels based on improved YOLOv5s

Hou, Jiankang; Zhang, Cunyong

doi:10.1016/j.heliyon.2024.e31029

Heliyon

2024

DOI: 10.1016/j.heliyon.2024.e31029

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Shallow mud detection algorithm for submarine channels based on improved YOLOv5s

Jiankang Hou,

Cunyong Zhang

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2024

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Optimization and Application of Improved YOLOv9s-UI for Underwater Object Detection

Pan,

Chen,

et al. 2024

Applied Sciences

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The You Only Look Once (YOLO) series of object detection models is widely recognized for its efficiency and real-time performance, particularly under the challenging conditions of underwater environments, characterized by insufficient lighting and visual disturbances. By modifying the YOLOv9s model, this study aims to improve the accuracy and real-time capabilities of underwater object detection, resulting in the introduction of the YOLOv9s-UI detection model. The proposed model incorporates the Dual Dynamic Token Mixer (D-Mixer) module from TransXNet to improve feature extraction capabilities. Additionally, it integrates a feature fusion network design from the LocalMamba network, employing channel and spatial attention mechanisms. These attention modules effectively guide the feature fusion process, significantly enhancing detection accuracy while maintaining the model’s compact size of only 9.3 M. Experimental evaluation on the UCPR2019 underwater object dataset shows that the YOLOv9s-UI model has higher accuracy and recall than the existing YOLOv9s model, as well as excellent real-time performance. This model significantly improves the ability of underwater target detection by introducing advanced feature extraction and attention mechanisms. The model meets portability requirements and provides a more efficient solution for underwater detection.

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Optimization and Application of Improved YOLOv9s-UI for Underwater Object Detection

Pan,

Chen,

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

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Shallow mud detection algorithm for submarine channels based on improved YOLOv5s

Cited by 1 publication

References 59 publications

Optimization and Application of Improved YOLOv9s-UI for Underwater Object Detection

Optimization and Application of Improved YOLOv9s-UI for Underwater Object Detection

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