Improved YOLOv5s for typical carp target detection

To achieve efficient and real-time classification and recognition of freshwater fish on limited equipment resources, this study proposes a method based on the YOLOv5s network to learn freshwater fish dataset images based on machine vision technology. Firstly, replace the backbone network of YOLOv5s with ShuffleNetV2 of the lightweight network to improve the efficiency of architecture design and ensure the detection speed. Secondly, the lightweight convolution GSConv is introduced to reduce the complexity of the network, maintain the accuracy of the network, and achieve the lightweight effect. Finally, the combination of ShuffleNetV2 and lightweight convolution GSConv can significantly improve the lightweight effect while maintaining accuracy. The algorithm was validated with four datasets of Rhodeinae, goldfish, grass carp, and stone moroko, which are all typical carp. The results showed that the improved network model FLOPs is 4.9 G, which was 69% less than YOLOv5s, the model size was 6.1 MB, which was 58% less than YOLOv5s, and the improved model mAP0.5 could reach 95.5%.