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Research on Behavior Recognition and Online Monitoring System for Liaoning Cashmere Goats Based on Deep Learning

文献类型： 外文期刊

作者： Chen, Geng 1 ; Yuan, Zhiyu 1 ; Luo, Xinhui 1 ; Liang, Jinxin 1 ; Wang, Chunxin 1 ;

作者机构： 1.Jilin Acad Agr Sci, Anim Husb & Vet Res Inst, Shengtai St, Changchun 130033, Peoples R China

2.Jilin Agr Univ, Coll Anim Sci & Technol, Xincheng St 2888, Changchun 130118, Peoples R China

关键词： deep learning; Liaoning Cashmere Goat; YOLOv8n; behavior recognition

期刊名称：ANIMALS （ 影响因子：2.7； 五年影响因子：3.2 ）

ISSN： 2076-2615

年卷期： 2024 年 14 卷 22 期

页码：

收录情况： SCI

摘要： Liaoning Cashmere Goats are a high-quality dual-purpose breed valued for both their cashmere and meat. They are also a key national genetic resource for the protection of livestock and poultry in China, with their intensive farming model currently taking shape. Leveraging new productivity advantages and reducing labor costs are urgent issues for intensive breeding. Recognizing goatbehavior in large-scale intelligent breeding not only improves health monitoring and saves labor, but also improves welfare standards by providing management insights. Traditional methods of goat behavior detection are inefficient and prone to cause stress in goats. Therefore, the development of a convenient and rapid detection method is crucial for the efficiency and quality improvement of the industry. This study introduces a deep learning-based behavior recognition and online detection system for Liaoning Cashmere Goats. We compared the convergence speed and detection accuracy of the two-stage algorithm Faster R-CNN and the one-stage algorithm YOLO in behavior recognition tasks. YOLOv8n demonstrated superior performance, converging within 50 epochs with an average accuracy of 95.31%, making it a baseline for further improvements. We improved YOLOv8n through dataset expansion, algorithm lightweighting, attention mechanism integration, and loss function optimization. Our improved model achieved the highest detection accuracy of 98.11% compared to other state-of-the-art (SOTA) target detection algorithms. The Liaoning Cashmere Goat Online Behavior Detection System demonstrated real-time detection capabilities, with a relatively low error rate compared to manual video review, and can effectively replace manual labor for online behavior detection. This study introduces detection algorithms and develops the Liaoning Cashmere Goat Online Behavior Detection System, offering an effective solution for intelligent goat management.

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