Improved YOLOv5-based target detection algorithm on water

Hongbo Li; Ming Zhang

doi:10.1117/12.3005084

9 October 2023 Improved YOLOv5-based target detection algorithm on water

Hongbo Li, Ming Zhang

Proceedings Volume 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023); 127910M (2023) https://doi.org/10.1117/12.3005084
Event: Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 2023, Qingdao, SD, China

Abstract

Target detection tasks on water have been affected by water surface fluctuations, light changes, occlusions, overlaps and targets that are too small, resulting in poor recognition accuracy and detection speed. To solve this problem, this paper proposes an improved water target detection algorithm based on YOLOv5. Firstly, recursive gated convolution module were used instead of ordinary convolution to achieve high-order spatial interactions and improve model performance and confidence. Secondly, in order to solve the problems of difficulty in detecting small targets, the input size of the image is increased and P6 detection heads are introduced to improve the detection accuracy of small targets. Finally, to alleviate the situation of poor overlapping target detection, coordinate attention(CA) mechanism is introduced and incorporated into the backbone to extract large range features, and reduce the number of parameters and computational overhead. Compared with the original model of YOLOv5s, the improved algorithm model improves the recognition accuracy by 4.6% and the recall by 3.4%, reaching 69.2% and 67.6% respectively. The running speed reaches 208FPS, meeting the requirements of real-time detection.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Hongbo Li and Ming Zhang "Improved YOLOv5-based target detection algorithm on water", Proc. SPIE 12791, Third International Conference on Advanced Algorithms and Neural Networks (AANN 2023), 127910M (9 October 2023); https://doi.org/10.1117/12.3005084

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