Abstract: To address the issues of low accuracy and high labor requirements in traditional fish hypoxia detection methods, a hypoxia risk assessment method for cultured fish based on Prune-YOLOv5s has been proposed. This paper introduces a hypoxia risk assessment method for cultured fish based on the Prune-YOLOv5s algorithm. This method firstly collects data on aquatic surface respiration (ASR) performed by fish under hypoxic conditions to create a data set for fish hypoxia. The dataset is then utilized to train the YOLOv5s model. Then, the lightweight and improved YOLOv5s model was used to monitor the behavior of fish surface respiration during hypoxia in real time. The introduction of the ASR coefficient allows for the quantification of ASR instances in fish, which is indicative of hypoxia risk. And the fish hypoxia assessment module is designed to evaluate the risk of hypoxia. The improved performance of the YOLOv5s model before and after modifications and the accuracy of the fish hypoxia assessment module are tested through the fish hypoxia experiment.The test results show that compared with the YOLOv5s model, the detection accuracy, model size, inference speed and detection speed of the PruneYOLOv5s model have been significantly improved. Among them, the detection accuracy of the 65% PruneYOLOv5s model, which has the best comprehensive performance, has been increased by 0.6% compared with the original model. The size of the model is reduced to 45.3% of the original model. The inference speed is improved by 23.8%, and the detection speed is also improved by 31.4%. The fish hypoxia assessment method achieves 97.4% accuracy in the test set of 39 test videos, and has a good performance in the hypoxia cycle experiment. The research indicates that the Prune-YOLOv5s-based hypoxia risk assessment method for cultured fish can effectively detect hypoxic conditions and provide accurate risk alerts, showing high feasibility for practical application.

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摘要： 为解决传统鱼类缺氧检测方法准确率不高、需耗费大量人力的问题，提出了一种基于Prune-YOLOv5s的养殖鱼类缺氧风险评估方法。该方法首先采集鱼类缺氧进行水面呼吸（Aquatic surface respiration，ASR）时的数据集，并训练YOLOv5s模型，然后用经轻量化改进的YOLOv5s模型实时检测鱼类缺氧进行水面呼吸的行为，并引入鱼类ASR系数，设计鱼群缺氧评估模块实现鱼类缺氧风险评估。最后通过鱼类缺氧实验对改进前后YOLOv5s模型性能以及缺氧评估模块的准确率进行测试。结果显示：与原模型相比，Prune-YOLOv5s模型的性能得到明显提升，其中综合性能最优的65%_Prune_YOLOv5s模型，模型大小缩小至原模型的45.3%，在检测精度上提升0.6%，在推理速度上提升23.8%，在检测速度上提升31.4%。鱼群缺氧评估模块在测试集中的准确率可达97.4%，在鱼类缺氧实验周期中表现良好。研究表明，基于Prune-YOLOv5s的养殖鱼类缺氧风险评估方法能有效检测鱼类缺氧情况，准确给出风险提示，将在实际应用中具有较好的可行性。

关键词: 人工智能, 深度学习, YOLOv5s, 鱼类缺氧, ASR系数