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<p>Conventional safety monitoring methods are increasingly inadequate for the complex conditions of modern coal mines. This study introduces a safety monitoring framework based on an enhanced YOLO model, specifically adapted for underground environments. Improvements include optimized anchor box design with K-means clustering, which reduces detection errors and improves localization accuracy. Evaluations on benchmark datasets demonstrate superior results, with mAP scores of 0.82 on UCF101, 0.85 on MS COCO, and 0.80 on a coal mine video dataset. When integrated with ConvLSTM, the system achieves higher accuracy in miner behavior recognition, while the incorporation of sensor data enables precise prediction of gas concentration, temperature, and humidity. Additionally, the decision-making module provides reliable early warnings of hazards such as gas leaks, fire, and unsafe behaviors, achieving the highest detection accuracy and an average response time of only 3&#xa0;s. The proposed system enhances detection performance, robustness, and real-time responsiveness, offering strong support for coal mine safety management.</p>

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Application of deep learning-based video AI analysis in coal mine safety monitoring

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年12月8日

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  • Gang Guo,
  • Xiaocheng Gao,
  • Jia Lei,
  • Haijun Lu,
  • Tao Yan,
  • Shuwei Yang,
  • Ziyang Yu,
  • Xingxing Wang

摘要

Conventional safety monitoring methods are increasingly inadequate for the complex conditions of modern coal mines. This study introduces a safety monitoring framework based on an enhanced YOLO model, specifically adapted for underground environments. Improvements include optimized anchor box design with K-means clustering, which reduces detection errors and improves localization accuracy. Evaluations on benchmark datasets demonstrate superior results, with mAP scores of 0.82 on UCF101, 0.85 on MS COCO, and 0.80 on a coal mine video dataset. When integrated with ConvLSTM, the system achieves higher accuracy in miner behavior recognition, while the incorporation of sensor data enables precise prediction of gas concentration, temperature, and humidity. Additionally, the decision-making module provides reliable early warnings of hazards such as gas leaks, fire, and unsafe behaviors, achieving the highest detection accuracy and an average response time of only 3 s. The proposed system enhances detection performance, robustness, and real-time responsiveness, offering strong support for coal mine safety management.