基于Prune-YOLOv5s的养殖鱼类缺氧风险评估方法  

Anoxic risk assessment method for cultured fish based on Prune-YOLOv5s

作　　者：陈庭槿 黄耀波 陈炫辛 周纪军 刘英[1] CHEN Tingjin;HUANG Yaobo;CHEN Xuanxin;ZHOUJijun;LIU Ying(College of Physics and Optoelectronic Engineering,Shenzhen University,Shenzhen 518061,Guangdong,China;Factulty of Education,Shenzhen University,Shenzhen 518061,Guangdong,China)

机构地区：[1]深圳大学物理与光电工程学院,广东深圳518061 [2]深圳大学教育学部,广东深圳518061

出　　处：《渔业现代化》2024年第5期81-89,共9页Fishery Modernization

基　　金：深圳大学横向项目“基于深度学习的便携式水下成像及实时检测系统(横KJ2023059)”;国家级大学生创新创业训练计划项目“普惠智慧渔业管理系统(202310590046)”。

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。最后通过鱼类缺氧试验对改进前后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%,在鱼类缺氧试验周期中表现良好。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。To address the issue of the traditional fish hypoxia detection methods,which have low accuracy and require a lot of labor,a Prune-YOLOv5s-based hypoxia risk assessment method for farmed fish is proposed.This paper introduces a hypoxia risk assessment method for cultured fish based on the Prune-YOLOv5s algorithm.This method first 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 quantifying ASR instances in fish,which indicates hypoxia risk.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 enhanced by 31.4%.The fish hypoxia assessment method achieves 97.4%accuracy in the test set of 39 test videos and performs well 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.

关 键 词：鱼类缺氧 人工智能 深度学习 YOLOv5s ASR系数 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] S932.2[自动化与计算机技术—计算机科学与技术]