基于深度学习的鱼类跟踪技术研究进展.

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 learningbased 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. [ABSTRACT FROM AUTHOR]