基于人工智能的鱼类行为识别研究综述

鱼类行为识别对于生态学、水产养殖、渔业资源管理等方面具有重要意义,可以通过其行为模式判断其生长发育状况和活动水平,并间接评估环境因素对其影响,以减少鱼类生长应激反应,提高资源利用效率,为水产养殖的智能化发展奠定基础。近年来,基于人工智能技术的鱼类行为识别方法受到广泛关注,其具有无损性、低成本等优势。本文综述了近5年基于卷积神经网络、循环神经网络、双流卷积神经网络等人工智能方法的鱼类行为识别技术,对鱼类行为识别方法及数据集进行了归纳与分析,在此基础上,对未来的研究进行讨论与展望。

Review of Fish Behavior Recognition Methods Based on Artificial Intelligence

With the rapid development and expansion of global aquaculture and the diversification of farming models, the scale, intelligence, and informatization of the aquaculture industry have become trends in its development. Fish behavior recognition is of significant importance in ecology, aquaculture, and fisheries resource management. It enables the assessment of fish growth, developmental status, and activity levels based on their behavioral patterns, indirectly evaluating the impact of environmental factors. This can help reduce stress responses in fish growth, improve resource utilization efficiency, and lay the foundation for intelligent development in aquaculture. Traditional fish behavior identification mainly relies on manual observation and recording, which consumes a considerable amount of time and effort and is subject to subjectivity and uncertainty. In recent years, fish behavior recognition methods based on artificial intelligence get extensive attention, is lossless, such as low cost advantage. The fish behavior recognition technologies were reviewed based on artificial intelligence over the past five years, including convolutional neural networks, recurrent neural networks, and two-stream convolutional neural networks. It also provided a summary and analysis of fish behavior recognition methods and datasets. Based on these foundations, an outlook on future research directions was discussed and provided.