Abnormal Behavior Fish and Population Detection Method based on Deep Learning

Zexin Zhao

doi:10.54097/fcis.v4i3.11018

Authors

Zexin Zhao

DOI:

https://doi.org/10.54097/fcis.v4i3.11018

Keywords:

Deep Learning, Target Detection, Deep Sort, YOLO v8, Fish Abnormal Behavior Recognition, Computer Vision

Abstract

This paper presents a detection model of fish with abnormal behavior and their number based on YOLO v8 and Deep Sort algorithm. The method firstly uses computer and acquisition system to monitor and analyze the fish behavior in real time, and can effectively detect the abnormal behavior of fish, such as abnormal swimming trajectory and abnormal residence time. The main work of this paper is to preprocess fish behavior videos, including video segmentation, data enhancement and other operations, and use data enhancement technology to improve the problem of fish occlusion in data set, which is easy to cause model false detection. Then, YOLO v8 and Deep Sort algorithm were used for multi-target tracking and target detection to extract the key information of fish behavior. Finally, through the analysis and comparison of the extracted information, the detection of fish with abnormal behavior and its quantity are realized. The experimental results show that the method proposed in this paper can effectively detect the abnormal behavior of fish, has high accuracy and real-time, and has certain application and popularization value.

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