Zhao Pengzhen, Wang Xichao, Yu Shuainan, Dong Xiangqing, Li Baojiang, Wang Haiyan, Chen Guochu
School of Electrical Engineering, Shanghai DianJi University, Shanghai, 201306, China.
Intelligent Decision and Control Technology Institute, Shanghai DianJi University, Shanghai, 201306, China.
Sci Data. 2025 May 13;12(1):780. doi: 10.1038/s41597-025-05118-1.
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.
煤矿井下钻探环境恶劣，事故频发，常见事故类型包括煤壁片帮、冒顶等。高质量数据集对于煤矿安全监测与自动化领域人工智能（AI）算法的开发与验证至关重要。目前，尚无针对煤矿工业场景的综合基准数据集，这限制了AI算法在该行业的研究进展。本研究首次构建了专门用于煤矿井下钻探作业的基准数据集（DsDPM 66），该数据集包含从多个钻探作业场景的监控视频中获取的105,096张图像。该数据集已进行人工标注，以支持目标检测和姿态估计等计算机视觉任务。此外，本研究对该数据集进行了广泛的基准测试实验，应用了包括但不限于YOLOv8和DETR在内的各种先进AI算法。结果表明，所提出的数据集突出了算法模型的改进领域，填补了煤矿开采中的数据空白，为煤矿安全监测的发展提供了宝贵资源。
