Real-time detection and tracking of fish abnormal behavior based on improved YOLOV5 and SiamRPN++

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.