Fast detection of cannibalism behavior of juvenile fish based on deep learning

•A detection neural network is proposed for the cannibalism behavior of juvenile fish.•The network improves the detection accuracy by adding an attention mechanism and changing the feature fusion network.•The network improves the detection speed by using lightweight modules.•Experiments show that the algorithm can detect the cannibalism behavior in aquaculture scenes in real time. Special behavior detection of fish is an effective way to ensure fish welfare and improve the intelligent level of aquaculture. However, due to the influence of the breeding environment and physiological factors, fish are prone to cannibalism during the juvenile period. In order to effectively monitor the behavior of the fry, this article proposes a real-time detection scheme based on the improved You Only Look Once (YOLO)-v5 to detect the cannibalism of grouper fry in recirculating aquaculture system(RAS). The specific improvements are as follows: (1) Multi-head attention mechanism is used to obtain global information in the last block of the backbone network (2) According to the idea of BIfpn, nodes that contribute little in the feature network which combines different features the neck is deleted. At the same time, feature fusion is added for nodes whose input and output are in the same layer (3) The lightweight general upsampling operator Carafe is used to replace the original upsampling. The experimental results show that the improved YOLOv5 model achieves 97% accuracy in the detection of cannibalism behavior of juvenile fish, effectively solving the problems of small targets, severe occlusion and motion blur in the culture environment. Meanwhile, compared with the YOLOv5s model, the improved model has 12.6% and 14% improvement in detection accuracy and processing speed, respectively, and achieves the requirements of high accuracy and real-time detection. In the actual aquaculture environment, farmers can take corresponding measures according to the detection results, which can effectively improve the survival rate and economic benefits of aquaculture.