Abstract
To address the scarcity of publicly available underground coal mine monitoring image datasets and the challenges of accurate recognition under complex drilling environments, this study constructs a large-scale image dataset for coal mine drill pipe counting based on Oriented Bounding Boxes (CMDPC_OBB). The dataset is designed for multi-pose and multi-view drill pipe detection and counting tasks, improving data coverage in complex underground scenarios. CMDPC_OBB consists of two sub-datasets: a multi-object detection dataset (MOD_2D) and a structurally enhanced single-object classification dataset (SOC_3D). MOD_2D is built using a 15-frame interval sampling strategy, resulting in 114,869 field images annotated with rotated bounding boxes. SOC_3D extends the original samples at the data level by generating eight additional views per instance through single-image 3D reconstruction, yielding 4,023 images to enhance multi-view representation. Nine object detection and oriented detection models were evaluated on CMDPC_OBB. The highest mAP reaches 89.1%, demonstrating the dataset’s effectiveness and benchmarking value in complex underground environments.
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This work was supported in part by National Natural Science Foundation of China under Grant 62472145, in part by the Key Research and Development and Promotion in Henan Province (Science and Technology Research) under Grant 252102320210 and 252102211015.
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Zhang, F., Liu, X., Zhang, H. et al. CMDPC_OBB: A Large-Scale Image Dataset for Coal Mine Drill Pipe Counting based on Oriented Bounding Box. Sci Data (2026). https://doi.org/10.1038/s41597-026-07474-y
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DOI: https://doi.org/10.1038/s41597-026-07474-y
