<b>An Open Paradigm Dataset for Intelligent Monitoring of Underground Drilling Scenarios 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.在煤矿安全监测与自动化领域，高质量专业化数据集对于人工智能算法的研发与验证至关重要。当前尚无针对煤矿工业场景的专用综合性基准数据集，这极大限制了人工智能算法在煤矿开采领域的研究进展。本研究首次构建了面向煤矿掘进工作面的专用基准数据集（DsDPM 66），该数据集包含从66个钻井作业场景的视频中提取的105096张图像，并针对目标检测、姿态估计等计算机视觉任务完成了精细人工标注。此外，本研究基于该数据集开展了大量基准测试实验，应用了包括但不限于YOLOv8、DETR在内的多种先进人工智能算法。实验结果表明，该数据集可有效提升各类煤矿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，可为煤矿安全监测与自动化技术的发展提供宝贵的资源支撑。