Work

CMDPC_OBB: A Large-Scale Image Dataset for Coal Mine Drill Pipe Counting based on Oriented Bounding Box

Year: 2026

Type: data-paper

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.

Source: Scientific Data

DOI: 10.1038/s41597-026-07474-y