Abnormal fish detection helps producers adjust breeding strategies in a timely manner to prevent the occurrence of diseases and improve aquaculture efficiency and quality. The rapid development of computer vision technology provides a noninvasive method for abnormal fish detection, which can be used to identify and classify abnormal fish. This paper provides an overview of the research progress of computer vision techniques in detecting abnormal fish over the past two decades. For the first time, the abnormal fish detection task is divided into three external manifestations: abnormal physiological activities, abnormal trajectories, and abnormal surface features of fish. The traditional methods and deep learning methods in computer vision technology are further summarized for their application approaches in these three research types, and the commonly used classical algorithm models in abnormal fish detection are introduced comprehensively. In addition, this paper summarizes several common methods for obtaining public datasets in aquaculture and evaluation indicators of model accuracy, emphasizing two methods for researchers to collect experimental on-site data. Finally, based on the above work, this paper analyzes several challenges in abnormal fish detection, proposes feasible strategies for each challenge, and notes the importance of improving models to effectively integrate and analyze data from multiple platforms. This paper provides some reference value for research on abnormal fish.

鱼类异常检测有助于生产者及时调整养殖策略，预防疾病的发生，提高养殖效率和质量。计算机视觉技术的快速发展为异常鱼类检测提供了一种非侵入性的方法，可用于异常鱼类的识别和分类。本文综述了近二十年来计算机视觉技术在异常鱼类检测方面的研究进展。首次将异常鱼类检测任务分为鱼类异常生理活动、异常轨迹、异常表面特征三种外部表现。进一步总结了计算机视觉技术中的传统方法和深度学习方法在这三种研究类型中的应用途径，全面介绍了异常鱼类检测中常用的经典算法模型。此外，本文总结了水产养殖中公共数据集的几种常见获取方法以及模型精度的评价指标，重点介绍了研究人员收集实验现场数据的两种方法。最后，基于上述工作，本文分析了异常鱼类检测中的几个挑战，针对每个挑战提出了可行的策略，并指出了改进模型以有效集成和分析来自多个平台的数据的重要性。该文为异常鱼类的研究提供了一定的参考价值。本文总结了水产养殖中公共数据集的几种常用获取方法以及模型精度的评价指标，重点介绍了研究人员收集实验现场数据的两种方法。最后，基于上述工作，本文分析了异常鱼类检测中的几个挑战，针对每个挑战提出了可行的策略，并指出了改进模型以有效集成和分析来自多个平台的数据的重要性。该文为异常鱼类的研究提供了一定的参考价值。本文总结了水产养殖中公共数据集的几种常用获取方法以及模型精度的评价指标，重点介绍了研究人员收集实验现场数据的两种方法。最后，基于上述工作，本文分析了异常鱼类检测中的几个挑战，针对每个挑战提出了可行的策略，并指出了改进模型以有效集成和分析来自多个平台的数据的重要性。该文为异常鱼类的研究提供了一定的参考价值。并指出改进模型以有效集成和分析来自多个平台的数据的重要性。该文为异常鱼类的研究提供了一定的参考价值。并指出改进模型以有效集成和分析来自多个平台的数据的重要性。该文为异常鱼类的研究提供了一定的参考价值。