An open paradigm dataset for intelligent monitoring of underground drilling operations in coal mines.

Journal: Scientific data
Published Date: May 13, 2025

Abstract

The underground drilling environment in coal mines is critical and prone to accidents, with common accident types including rib spalling, roof falling, and others. High-quality datasets are essential for developing and validating artificial intelligence (AI) algorithms in coal mine safety monitoring and automation field. Currently, there is no comprehensive benchmark dataset for coal mine industrial scenarios, limiting the research progress of AI algorithms in this industry. For the first time, this study constructed a benchmark dataset (DsDPM 66) specifically for underground coal mine drilling operations, containing 105,096 images obtained from surveillance videos of multiple drilling operation scenes. The dataset has been manually annotated to support computer vision tasks such as object detection and pose estimation. In addition, this study conducted extensive benchmarking experiments on this dataset, applying various advanced AI algorithms including but not limited to YOLOv8 and DETR. The results indicate the proposed dataset highlights areas for improvement in algorithmic models and fills the data gap in the coal mining, providing valuable resources for developing coal mine safety monitoring.

Authors

  • Pengzhen Zhao
    School of Electrical Engineering, Shanghai DianJi University, Shanghai, 201306, China.
  • Xichao Wang
    The School of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China; Intelligent Decision and Control Technology Institute, Shanghai Dianji University, Shanghai 201306, China.
  • Shuainan Yu
    School of Electrical Engineering, Shanghai DianJi University, Shanghai, 201306, China.
  • Xiangqing Dong
    School of Electrical Engineering, Shanghai DianJi University, Shanghai, 201306, China.
  • Baojiang Li
    The School of Electrical Engineering, Shanghai Dianji University, Shanghai 201306, China; Intelligent Decision and Control Technology Institute, Shanghai Dianji University, Shanghai 201306, China. Electronic address: libj@sdju.edu.cn.
  • Haiyan Wang
    College of Chemistry and Material Science, Shandong Agricultural University, Tai'an 271018, PR China.
  • Guochu Chen
    School of Electrical Engineering, Shanghai DianJi University, Shanghai, 201306, China.

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