A study on the application of improved YOLOv5 model for fish target detection

Yushuang Bai; Xiaoqiang Yu

doi:10.1117/12.3034079

13 June 2024 A study on the application of improved YOLOv5 model for fish target detection

Yushuang Bai, Xiaoqiang Yu

Proceedings Volume 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024); 1318039 (2024) https://doi.org/10.1117/12.3034079
Event: International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 2024, Guangzhou, China

Abstract

Fish target detection is of great significance for research in production automation and so on. In order to quickly and accurately get the location of fish targets and their categories, this paper proposes a fish target detection method with improved YOLOv5 model. The experimental results show that the mAP of the improved YOLOv5 model is significantly improved over the original model, in which the WIoU loss function is introduced to improve the accuracy of the regression frames, and the accuracy on the self-constructed dataset is 92.32%, which is an improvement of 5.80 percentage points compared with the baseline model. The YOLOv5-based algorithm enables fast and accurate identification of fish localization and species, and meets real-time requirements.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Yushuang Bai and Xiaoqiang Yu "A study on the application of improved YOLOv5 model for fish target detection", Proc. SPIE 13180, International Conference on Image, Signal Processing, and Pattern Recognition (ISPP 2024), 1318039 (13 June 2024); https://doi.org/10.1117/12.3034079

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