表面矿山人员对象视频跟踪方法基于YOLOv5-DeepSort算法
Jie Jiang1, Guoliang Xie2, Junjian Cui2
1Zhonglian Runshi Xinjiang Coal Industry Co., LTD, Changji, 831800, Changji Prefecture, Xinjiang, China. xinjiangrunshi@126.com.
Scientific reports
|May 16, 2025
概括
本研究提出了一种新方法,用于使用YOLOv5和Deepsort.在露天采矿中跟踪矿工. 该系统在具有挑战性的条件下实现了高精度,提高了人员安全.
科学领域:
- 计算机视觉 计算机视觉
- 人工智能的人工智能
- 采矿工程 采矿工程 采矿工程
背景情况:
- 露天地雷对智能监控提出了独特的挑战,原因是人工照明,缺乏颜色和低对象背景对比度.
- 有效的人员跟踪对于确保危险的采矿环境中的安全至关重要.
研究的目的:
- 开发和评估开采矿的智能人员跟踪系统.
- 通过实时监控,提高矿工的安全.
主要方法:
- 使用YOLOv5模型通过摄像头监控检测地表矿工.
- 采用DeepSort算法进行实时目标人员跟踪.
- 使用定制数据集进行实验,以验证系统性能.
主要成果:
- 实现了一致的准确性和平均平均精度 (mAP) 约92%的露天矿山人员跟踪.
- 在追踪系统中获得了90%的F1分数.
- 在具有挑战性的条件下展示实时跟踪能力,包括昏暗的光线,障碍物和光.
结论:
- YOLOv5-Deepsort方法提供了对露天矿工的精确跟踪.
- 这项技术有助于在采矿业务中保障生产安全.
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