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.

集约化水产养殖的发展增加了对基于视频的水下监测技术产生多类鱼类统计数据的需求。然而，复杂的海洋环境，例如光线波动，形状变形，鱼的相似外观和遮挡物，使其成为一项具有挑战性的任务。因此，该领域的研究相对较少。本文提出了一种实时的多类鱼类种群统计方法（RMCF）。鱼存量统计的准确性比以前的最佳方法达到了95.6％。所提出的方法使用YOLOv4作为骨干网络和基于深度学习的并行两分支结构，以在真实的海洋牧场环境中执行鱼的实时检测和跟踪。两分支结构包含检测和跟踪分支，检测分支可以检测鱼类并提高跟踪精度和在线跟踪时间。跟踪分支跟踪鱼并进行数字统计。最后，我们结合检测和跟踪分支来生成多类鱼类种群统计数据。在这里，检测分支有助于跟踪分支实现多类跟踪。通过跟踪结果，我们可以进一步分析不同鱼类随时间变化的趋势。与最新的视频跟踪和检测方法相比，实验结果表明，该方法在复杂的真实海洋环境中提供了更好的鱼类检测和跟踪性能。我们将检测和跟踪分支结合起来，以生成多类鱼类种群统计数据。在这里，检测分支有助于跟踪分支实现多类跟踪。通过跟踪结果，我们可以进一步分析不同鱼类随时间变化的趋势。与最新的视频跟踪和检测方法相比，实验结果表明，该方法在复杂的真实海洋环境中提供了更好的鱼类检测和跟踪性能。我们将检测和跟踪分支结合起来，以生成多类鱼类种群统计数据。在这里，检测分支有助于跟踪分支实现多类跟踪。通过跟踪结果，我们可以进一步分析不同鱼类随时间变化的趋势。与最新的视频跟踪和检测方法相比，实验结果表明，该方法在复杂的真实海洋环境中提供了更好的鱼类检测和跟踪性能。