Research on fish target lightweight detection algorithm based on improved YOLOv5s

It is difficult to detect fish targets accurately and quickly because fish target detection algorithms face problems such as underwater background complexity, scale variation, and occlusion. To solve this problem, an improved YOLOv5 lightweight detection algorithm is proposed. Firstly, the building block of lightweight classification network ShuffleNet v2 is used as the feature extraction network, and the computational and parametric quantities of the model are reduced and the operation speed is improved by using the lightweight network ShuffleNet v2 instead of Backbone as the backbone network; secondly, the Repulsion Loss is used to improve the model based on the principle of sample bounding box exclusion and attraction. anti-obscuration ability and make full use of the occluded positive samples in the training process; finally, the CA attention mechanism is introduced to enhance the model's ability to accurately locate the target. The experimental results show that the number of parameters of the improved lightweight model is only 5.3MB, the number of parameters is reduced by 1.8MB, the detection accuracy mAP reaches 95.2%, which is 3.6 percentage points higher than that of the original model, and the amount of computation is only 8.7GFLOPs, and the amount of computation is reduced by 5.7G. Compared with the current mainstream target detection algorithms SSD, YOLOv3, YOLOv7, etc., it not only has superior detection accuracy and inference speed, but also significantly reduces the amount of computation, realizing the balance between lightweight and detection accuracy, and this study provides a reference for the research of fish target lightweighting.