Contemporary research on ichthyological behavior predominantly investigates underwater environments. However, the intricate nature of aquatic ecosystems often hampers subaqueous observations of fish behavior due to interference. Transitioning the observational perspective from subaqueous to supra-aquatic enables a more direct assessment of fish physiology and habitat conditions. In this study, we utilized the YOLOv5 convolutional neural network target detection model to develop a fish jumping behavior (FJB) recognition model. A dataset comprising 877 images of fish jumping, captured via a camera in a reservoir, was assembled for model training and validation. After training and validating the model, its recognition accuracy was further tested in real aquatic environments. The results show that YOLOv5 outperforms YOLOv7, YOLOv8, and YOLOv9 in detecting splashes. Post 50 training epochs, YOLOv5 achieved over 97 % precision and recall in the validation set, with an F1 score exceeding 0.9. Furthermore, an enhanced YOLOv5-SN model was devised by integrating specific rules related to ripple size variation and duration, attributable to fish jumping. This modification significantly mitigates noise interference in the detection process. The model's robustness against weather variations ensures reliable detection of fish jumping behavior under diverse meteorological conditions, including rain, cloudiness, and sunshine. Different meteorological elements exert varying effects on fish jumping behavior. The research results can lay the foundation for intelligent perception in aquatic ecology assessment and aquaculture.

中文翻译：

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增强水生环境中的生态传感：使用 YOLOv5 自动识别鱼的跳跃行为


当代鱼类行为研究主要调查水下环境。然而，由于干扰，水生生态系统的复杂性往往会阻碍对鱼类行为的水下观察。将观察视角从水下过渡到水上，可以更直接地评估鱼类的生理和栖息地条件。在本研究中，我们利用 YOLOv5 卷积神经网络目标检测模型开发了鱼跳行为 （FJB） 识别模型。一个数据集包含 877 张鱼跳跃图像，通过水库中的摄像头捕获，用于模型训练和验证。在训练和验证模型后，在真实的水生环境中进一步测试了其识别准确性。结果表明，YOLOv5 在检测飞溅方面优于 YOLOv7、YOLOv8 和 YOLOv9。经过 50 个训练 epoch 后，YOLOv5 在验证集中实现了超过 97% 的准确率和召回率，F1 分数超过 0.9。此外，通过整合与鱼跳相关的涟漪大小变化和持续时间相关的具体规则，设计了一个增强的 YOLOv5-SN 模型。这种修改大大减轻了检测过程中的噪声干扰。该模型对天气变化的鲁棒性确保了在各种气象条件（包括雨、多云和阳光）下可靠地检测鱼的跳跃行为。不同的气象因素对鱼类跳跃行为的影响不同。研究结果可为水生生态评价和水产养殖中的智能感知奠定基础。