Grazing Sheep Behaviour Recognition Based on Improved YOLOV5

文献类型： 外文期刊

第一作者： Hu, Tianci

作者： Hu, Tianci;Jiang, Chengxiang;Bai, Tao;Hu, Tianci;Jiang, Chengxiang;Chand, Nividita Varun;Guo, Leifeng;Yan, Ruirui;Chand, Nividita Varun;Bai, Tao;Bai, Tao;Qi, Jingwei

作者机构：

关键词： improved YOLOV5; pasture; grazing sheep; behaviour recognition

期刊名称：SENSORS （ 影响因子：3.9； 五年影响因子：4.1 ）

ISSN：

年卷期： 2023 年 23 卷 10 期

页码：

收录情况： SCI

摘要： Fundamental sheep behaviours, for instance, walking, standing, and lying, can be closely associated with their physiological health. However, monitoring sheep in grazing land is complex as limited range, varied weather, and diverse outdoor lighting conditions, with the need to accurately recognise sheep behaviour in free range situations, are critical problems that must be addressed. This study proposes an enhanced sheep behaviour recognition algorithm based on the You Only Look Once Version 5 (YOLOV5) model. The algorithm investigates the effect of different shooting methodologies on sheep behaviour recognition and the model's generalisation ability under different environmental conditions and, at the same time, provides an overview of the design for the real-time recognition system. The initial stage of the research involves the construction of sheep behaviour datasets using two shooting methods. Subsequently, the YOLOV5 model was executed, resulting in better performance on the corresponding datasets, with an average accuracy of over 90% for the three classifications. Next, cross-validation was employed to verify the model's generalisation ability, and the results indicated the handheld camera-trained model had better generalisation ability. Furthermore, the enhanced YOLOV5 model with the addition of an attention mechanism module before feature extraction results displayed a mAP(@0.5) of 91.8% which represented an increase of 1.7%. Lastly, a cloud-based structure was proposed with the Real-Time Messaging Protocol (RTMP) to push the video stream for real-time behaviour recognition to apply the model in a practical situation. Conclusively, this study proposes an improved YOLOV5 algorithm for sheep behaviour recognition in pasture scenarios. The model can effectively detect sheep's daily behaviour for precision livestock management, promoting modern husbandry development.

分类号：

  • 相关文献
作者其他论文 更多>>

微信小程序