A dataset of drilling site object detection in underground coal mines
Drilling in underground coal mine is an important measure for dealing with gas,water and hidden geological disasters,which can significantly enhance the effectiveness of disaster prevention and control in coal mining operations.In order to monitor the drilling process in real time and improve drilling efficiency,it is necessary to carry out object detection to identify and locate key targets at the drilling site.Compared with traditional object detection method,deep learning-based object detection method can improve accuracy,timeliness and stability of object detection,but it requires high-quality datasets to perform well.At present,research on object detection in underground coal mine drilling sites mainly relies on small-scale private datasets,which are insufficient for providing necessary or reliable data for deep neural network model training.In this study,we constructed a dataset of drilling site object detection using photos taken by intrinsic safety law enforcement recorders.This dataset is developed through several steps,including data cleaning,data labeling,and expert sampling verification.The mainstream YOLO series object detection model is used for data quality assessment.This dataset comprises 70,948 images from drilling sites under different environmental conditions,covering five categories of objects:gripper,chuck,coal miner,mine safety helmet,and drill pipe.It provides annotated files in PASCAL VOC format.This dataset can provide strong data support for object detection research in underground coal mine drilling sites,and plays an important role in promoting intelligent underground coal mine monitoring and early warning.
万方数据
维普
