Multi-class fish stock statistics technology based on object classification and tracking algorithm

The development of intensive aquaculture has increased the need for video-based underwater monitoring technology to generate statistics on multi-class fish. However, the complex marine environment, e.g., light fluctuations, shape deformations, similar appearance of fish, and occlusions, makes this a challenging task. Therefore, there are relatively few studies in this field. This paper proposes a real-time multi-class fish stock statistics method (RMCF). The accuracy of fish stock statistics has reached 95.6% over the previous best approach. The proposed method uses YOLOv4 as a backbone network and a parallel two-branch structure based on deep learning to perform real-time detection and tracking of fish in a real marine ranch environment. The two-branch structure contains detection and tracking branches, where the detection branch detects fish species and improves tracking accuracy and online tracking time. The tracking branch tracks the fish and making a number statistics. Finally, we combine the detection and tracking branches to generate multi-class fish stock statistics. Here, the detection branch helps the tracking branch realize multi-class tracking. With the tracking results, we further analyze the changing trends of different fish over time. Compared to state-of-the-art video tracking and detection methods, the experiment results demonstrate the proposed method provides better fish detection and tracking performance in a complex real-world marine environment.

Automated summary

This study proposes a real-time multi-class fish stock statistics method using YOLOv4 and deep learning technology for fish detection and tracking in complex marine environments, achieving higher accuracy in fish stock statistics. The method combines detection and tracking branches to generate multi-class fish stock statistics, enabling multi-class tracking and further analysis of changing trends over time. Experiment results show that this method outperforms state-of-the-art video tracking and detection methods in real-world marine environments.