Paper
5 July 2024 Improving the underwater object detection algorithm for YOLOv5
Jingtao Yang, Lihui Sun, Gang Wang
Author Affiliations +
Proceedings Volume 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024); 1318477 (2024) https://doi.org/10.1117/12.3032864
Event: 3rd International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 2024, Kuala Lumpur, Malaysia
Abstract
This paper addresses the challenges of target occlusion and the difficulty in detecting small underwater organisms due to the behavioral characteristics of underwater fauna. An improved YOLOv5 algorithm for underwater target detection is proposed to enhance performance in underwater environments. Firstly, YOLOv5 incorporates the Coordinate Attention module to further enhance feature extraction. Secondly, a BiFPN (Bidirectional Feature Pyramid Network) is employed to better integrate features of different scales. Finally, a DyHead (Detection Head based on attention mechanism) is added for target detection. Experimental results on the URPC2020 dataset show that the improved YOLOv5 network achieves a 1.7% increase in mAP@0.5 and a 1.1% increase in mAP@0.5-0.95. The parameter count of the improved YOLOv5 network only increases by 5.5%, with a computational overhead of 5.6%. Therefore, the proposed approach demonstrates superior performance in underwater target detection tasks.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Jingtao Yang, Lihui Sun, and Gang Wang "Improving the underwater object detection algorithm for YOLOv5", Proc. SPIE 13184, Third International Conference on Electronic Information Engineering and Data Processing (EIEDP 2024), 1318477 (5 July 2024); https://doi.org/10.1117/12.3032864
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KEYWORDS
Object detection

Detection and tracking algorithms

Target detection

Submerged target modeling

Feature fusion

Head

Environmental sensing

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