Intelligent Detection of Underwater Fish Speed Characteristics Based on Deep Learning

At present, the breeding area of Hainan province is 58,000 hectares, and the breeding industry is an important economic source of Hainan province. As an important breeding object in Hainan province, the daily activities and abnormal behaviors of the fish have a direct impact on the breeding yield and the breeding income. For mariculture fish, changes in behaviour are often reflected in the important behavioral feature of swimming speed. Fishes swim at different speeds when they are in different situation. Of course, the change of fish speed is not only related to their own behavior and health state, but also related to the water quality. When the water quality changes or the fish are subjected to some abnormal stimulation, fish swimming speed will change. Therefore, accurate and rapid acquisition of fish swimming speed can not only reflect the change of fish behavior intuitively, but also reflect the water quality to a certain extent, which is of great significance to the large breeding province. Based on this, in this paper, tracking algorithm combined YOLOv5 deep learning network and Kalman filter is used to conduct intelligent detection of the speed characteristics of underwater fish, and track and calculate the speed of a single fish, a number of fish and fish swarm respectively. The experimental results show that the tracking algorithm proposed in this paper can track the underwater fish and calculate the corresponding speed well.