Liu Yanhong, Zhou Fang, Zheng Wenxin, Bai Tao, Chen Xinwen, Guo Leifeng
College of Computer and Information Engineering, Xinjiang Agricultural University, Urumqi 830052, China.
Agricultural Information Institute, Chinese Academy of Agricultural Sciences, Beijing 100080, China.
Sensors (Basel). 2024 Dec 5;24(23):7791. doi: 10.3390/s24237791.
The sleeping and eating behaviors of horses are important indicators of their health. With the development of the modern equine industry, timely monitoring and analysis of these behaviors can provide valuable data for assessing the physiological state of horses. To recognize horse behaviors in stalls, this study builds on the SlowFast algorithm, introducing a novel loss function to address data imbalance and integrating an SE attention module in the SlowFast algorithm's slow pathway to enhance behavior recognition accuracy. Additionally, YOLOX is employed to replace the original target detection algorithm in the SlowFast network, reducing recognition time during the video analysis phase and improving detection efficiency. The improved SlowFast algorithm achieves automatic recognition of horse behaviors in stalls. The accuracy in identifying three postures-standing, sternal recumbency, and lateral recumbency-is 92.73%, 91.87%, and 92.58%, respectively. It also shows high accuracy in recognizing two behaviors-sleeping and eating-achieving 93.56% and 98.77%. The model's best overall accuracy reaches 93.90%. Experiments show that the horse behavior recognition method based on the improved SlowFast algorithm proposed in this study is capable of accurately identifying horse behaviors in video data sequences, achieving recognition of multiple horses' sleeping and eating behaviors. Additionally, this research provides data support for livestock managers in evaluating horse health conditions, contributing to advancements in modern intelligent horse breeding practices.
马的睡眠和进食行为是其健康状况的重要指标。随着现代养马业的发展，及时监测和分析这些行为可为评估马的生理状态提供有价值的数据。为了识别马在厩舍中的行为，本研究基于SlowFast算法进行构建，引入了一种新颖的损失函数来解决数据不平衡问题，并在SlowFast算法的慢速路径中集成了一个SE注意力模块，以提高行为识别准确率。此外，采用YOLOX取代SlowFast网络中的原始目标检测算法，减少视频分析阶段的识别时间，提高检测效率。改进后的SlowFast算法实现了对马在厩舍中行为的自动识别。识别站立、仰卧和侧卧三种姿势的准确率分别为92.73%、91.87%和92.58%。在识别睡眠和进食两种行为方面也表现出较高的准确率，分别达到93.56%和98.77%。该模型的最佳总体准确率达到93.90%。实验表明，本研究提出的基于改进后的SlowFast算法的马行为识别方法能够准确识别视频数据序列中的马行为，实现对多匹马睡眠和进食行为的识别。此外，本研究为畜牧管理人员评估马的健康状况提供了数据支持，有助于推动现代智能养马实践的发展。
