<b>An open paradigm dataset for intelligent monitoring of underground drilling o</b><b>perations</b><b> in coal mines</b>

In the field of coal mine safety monitoring and automation, high-quality and specialized datasets are crucial for the development and validation of artificial intelligence algorithms. Currently, there is no comprehensive benchmark dataset specifically for coal mine industrial scenarios, which significantly limits the research progress of AI algorithms in the coal mining industry. This study has constructed for the first time a benchmark dataset (DsDPM 66) specifically for coal mine heading faces, containing 105,096 images obtained from videos of 66 drilling operation scenes. The dataset has been meticulously annotated manually to suit 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 experimental results show that the proposed dataset can effectively improve the accuracy of various object detection and pose estimation models in coal mines, filling the data gap in the coal mining field and providing valuable resources for the development of coal mine safety monitoring and automation technologies. Due to storage constraints, the remaining categories of the dataset, including coal_miner, compressed_oxygen_self_rescuer, and mining_helmet, are available at https://doi.org/10.6084/m9.figshare.26135107.v1.在煤矿安全监测与自动化领域，高质量的专业化数据集对于人工智能算法的开发与验证至关重要。目前，针对煤矿工业场景的综合性基准数据集尚属空白，这极大地制约了人工智能算法在煤矿行业的研究进展。本研究首次构建了针对煤矿掘进工作面的基准数据集（DsDPM 66），包含从66个钻孔作业场景视频中获取的105,096张图像。该数据集经过人工精细标注，适用于目标检测（object detection）、姿态估计（pose estimation）等计算机视觉任务。此外，本研究在该数据集上开展了大量基准实验，应用了包括但不限于YOLOv8和DETR在内的多种先进人工智能算法。实验结果表明，所提出的数据集能够有效提升煤矿场景下各类目标检测与姿态估计模型的精度，填补了煤矿领域的数据空白，并为煤矿安全监测与自动化技术的发展提供了宝贵资源。受存储限制，数据集的其余类别（包括coal_miner、compressed_oxygen_self_rescuer和mining_helmet）可通过链接https://doi.org/10.6084/m9.figshare.26135107.v1获取。