1. 基于 Prune-YOLOv5s 的养殖鱼类缺氧风险评估方法.
- Author
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陈庭槿, 黄耀波, 陈炫辛, 周纪军, and 刘英
- Subjects
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HYPOXEMIA , *RISK assessment , *ARTIFICIAL intelligence , *DEEP learning , *ACQUISITION of data - Abstract
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% Prune YOLOv5s 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. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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