该小组的猪攻击和日常行为检测和跟踪基于改进的YOLOv5s和DeepSORT
Tianyu Cheng1, Fujie Sun2, Liang Mao3
1Jiangxi Vocational College of Mechanical & Electrical Technology, Nanchang, China.
PloS one
|October 31, 2025
概括
这项研究引入了一种改进的深度学习模型,用于自动检测和跟踪猪行为. 改进后的系统在识别饮食和站立等行为时,即使在具有挑战性的农场条件下,也能达到很高的准确性.
科学领域:
- 动物科学动物科学
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 自动猪行为监测对于动物福利和农场管理至关重要.
- 挑战包括农场环境中的可变照明和猪封闭.
- 现有的方法往往在准确性和稳定性方面扎.
研究的目的:
- 开发一种强大的深度学习方法,用于准确检测和跟踪猪行为.
- 改进现有的YOLOv5s和DeepSORT农场条件模型.
- 为无接触式自动猪监测提供可扩展的技术支持.
主要方法:
- 使用了改进的YOLOv5s模型,具有用于猪检测和行为识别的注意力机制.
- 实现了Shape-IoU以优化界限框回归,增强对阻塞的强度.
- 采用了改进的DeepSORT模型来追踪四种关键猪行为:吃,站,撒谎和攻击.
主要成果:
- 与标准YOLOv5s相比,改进的YOLOv5s算法实现了6.6%的精度增加 (mAP@0.5%从92.7%增加到99.3%).
- 追踪指标显示,在测试视频中,MOTA的94.5%和MOTP的94.9%的高性能.
- 该系统在各种照明和密度条件下,在猪检测和行为跟踪方面表现出高精度.
结论:
- 开发的深度学习方法显著提高了猪行为监测的准确性和稳定性.
- 改进的YOLOv5s和DeepSORT模型为无接触,自动化的猪监视提供了可靠的解决方案.
- 这项技术为先进的精密畜牧业提供了可扩展的基础.
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