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• 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. [ABSTRACT FROM AUTHOR]

• A detection and tracking model combining Yolov5 and Siamrpn++ was proposed. • It achieved high-precision target detection in the case of occlusion and deformation. • Multi-objective tracking task was accomplished using a single case algorithm. • Detection and tracking of abnormal fish will improve fish welfare. In recirculating aquaculture system, the abnormal behavior of fish is usually caused by poor water quality, hypoxia or diseases. Delayed recognition of this behavior will lead to large number of fish deaths. Thus, real-time detection and tracking of fish that behaviors abnormally is an effective way to promote the fish welfare and to improve the survival rate as well as economic benefits of aquaculture. However, due to the high-density breeding, the targets in the fish images are often quite small and in occlusion, which causes high false detection and target loss rate. This article proposes a combined end-to-end neural network to detect and track the abnormal behavior of porphyry seabream. The detection algorithm passes the initial value of the target into the tracking algorithm, and the tracking algorithm tracks subsequent frames to achieve end-to-end abnormal fish behavior detection and achieve high-speed and accurate tracking of abnormal behavior individuals. In the target detection part, YOLOV5s is improved by incorporating multi-level features and adding feature mapping. Compared with the original network, the detection precision A P 50 : 95 is increased by 8.8% while A P 50 reaches 99.4%. In the target tracking part, this paper achieves multi-target tracking of abnormal fish based on single-target tracking algorithm SiamRPN++. The tracking precision is 76.7%. By combining the two approaches, individual fish with abnormal behavior can be detected precisely and tracked in real time. [ABSTRACT FROM AUTHOR]