Abstract: In recent years, there has been rapid development in intelligent aquaculture and fisheries resource conservation, leading to an increased demand for fish tracking technologies. Traditional fish tracking methods rely heavily on visual observation and tag tracking, which suffer from low efficiency, limited applicability, and low accuracy, hindering their widespread adoption. With the rapid advancement of deep learning in computer vision, deep learning-based fish-tracking technologies can provide accurate, objective, scalable, and automated tracking methods. Firstly, this paper introduces the tracking objects and four deep learning-based fish tracking methods: semantic segmentation, instance segmentation, object detection, and object classification. Secondly, it describes how fish tracking technologies capture fish trajectories, postures, fish quantities, and fish lengths, which are important tracking information for fish targets. Furthermore, the application of deep learning-based fish tracking technologies in fish diseases, fish feeding behavior, and fish health status is discussed. The paper also explores the main challenges of current deep learning-based fish tracking technologies, including low contrast and texture blurring, image color distortion, occlusion, and deformation, along with some corresponding solutions. Finally, the paper concludes and provides an outlook on the future development of deep learning-based fish-tracking technologies. It suggests that deep learning-based fish tracking technologies offer higher accuracy and objectivity, providing more solutions for practical applications in different scenarios. This technology is expected to play a more significant role in aquaculture management, fish scientific research, and marine environment conservation, offering more data and support to relevant fields.

Key words: fish tracking, deep learning, fish body measurement, fish behavior

摘要： 近年来，水产养殖和渔业资源保护的智能化发展迅速，对鱼类跟踪技术的需求也随之增加。传统的鱼类跟踪方法主要依赖于目视观察和标签追踪，存在效率低、应用范围有限、准确率不高等问题，限制了其推广应用。随着深度学习技术在计算机视觉领域的快速发展，基于深度学习的鱼类跟踪技术能够提供准确、客观、可扩展和自动化的跟踪方法。首先，介绍了鱼类跟踪技术的跟踪对象和四种深度学习鱼类追踪方法，分别是语义分割、实例分割、目标检测和目标分类。其次，介绍了鱼类跟踪技术如何获取鱼类轨迹与姿态、鱼类数量以及鱼类体长等鱼类目标跟踪信息。然后，介绍了基于深度学习的鱼类跟踪技术在鱼类疾病、鱼类摄食行为以及鱼类健康状态方面的应用，并从低对比度和纹理模糊、图像颜色失真以及遮挡和变形等三个方面，探讨了目前基于深度学习的鱼类跟踪技术的主要问题和一些相应的解决方法。最后，对基于深度学习的鱼类跟踪技术的发展前景进行了总结和展望。研究认为：基于深度学习的鱼类跟踪技术具有更高的准确度和客观性，为不同场景下的实际应用提供了更多解决方案，该技术有望在水产养殖管理、鱼类科学研究以及海洋环境保护等领域发挥更重要的作用，为相关领域提供更多的数据和支持。

关键词: 鱼类跟踪, 深度学习, 鱼体测量, 鱼类行为