Coal mine underground drilling site object detection dataset

Drilling in underground coal mine is an important measure to solve gas disaster, water disaster and hidden geological disaster, which can significantly improve the level of coal mine disaster prevention and control. In order to monitor the drilling process in real time and improve drilling efficiency, object detection in coal mine underground drilling site is required. Object detection in coal mine underground drilling site is to identify and locate important targets involved in the drilling site. Compared with the traditional coal mine drilling site object detection method, the deep learning-based coal mine drilling site object detection method can improve the accuracy, timeliness and stability of the object detection, but it needs to rely on high-quality dataset. At present, research on object detection in coal mine underground drilling site mainly relies on small-scale private dataset, which is difficult to provide sufficient and reliable data for deep neural network model training. This study uses intrinsically safe law enforcement recorders for coal mine to photograph coal mine underground drilling site, and through steps such as data cleaning, data annotation, and expert spot inspection, a standardized object detection dataset for coal mine underground drilling site is constructed. This dataset contains 70,948 images from different drilling sites and environmental background conditions, covering five categories of objects: gripper, chuck, coal miner, mine safety helmet, and drill pipe, and provides annotated files in PASCAL VOC format. This dataset can provide strong data support for object detection research in coal mine underground drilling site, and plays an important role in promoting intelligent coal mine underground monitoring and early warning.