Deep-learning-based research on detection algorithms for marine fish

Mengwei Li

doi:10.1117/12.2660251

3 February 2023 Deep-learning-based research on detection algorithms for marine fish

Mengwei Li

Proceedings Volume 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022); 125112L (2023) https://doi.org/10.1117/12.2660251
Event: Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 2022, Hulun Buir, China

Abstract

The identification of marine fish species is significant for marine fish research and conservation and the development and utilization of marine fish resources. To increase the speed and accuracy of detection of marine fish species in natural environments, this document proposes a fish recognition method based on the improved YOLOv5 model. First, the efficient channel attention mechanism module is incorporated into the backbone network to enhance the learning of local and global features of the feature map; then, the GIoU loss function is modified into the Focal-EIoU loss function to reduce the impact of low-quality samples on the detection effect. Under the same training conditions, the experimental results show that the improved YOLOv5s model achieves 94.6% detection precision, 93.8% recall rate, and 92.4% mean average precision. The enhanced fish detection method based on YOLOv5 proposed in this study has good accuracy and effectiveness and can meet the detection of marine fish species in a natural environment.

Citation Download Citation

Mengwei Li "Deep-learning-based research on detection algorithms for marine fish", Proc. SPIE 12511, Third International Conference on Computer Vision and Data Mining (ICCVDM 2022), 125112L (3 February 2023); https://doi.org/10.1117/12.2660251

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