煤矿井下钻场目标检测数据集  

A dataset of drilling site object detection in underground coal mines

作　　者：周蔚 董立红[1] 叶鸥 厍向阳[1] 段雪瑶 彭志奎 王思倩 赵楠楠 郭旭鹏 ZHOU Wei;DONG Lihong;YE Ou;SHE Xiangyang;DUAN Xueyao;PENG Zhikui;WANG Siqian;ZHAO Nannan;GUO Xupeng(Xi’an University of Science and Technology,Xi’an 710054,P.R.China)

机构地区：[1]西安科技大学,西安710054

出　　处：《中国科学数据（中英文网络版）》2024年第2期300-312,共13页China Scientific Data

基　　金：国家自然科学基金(62002285)。

摘　　要：煤矿井下钻场打钻是解决瓦斯灾害、水害、隐蔽地质灾害的重要措施,可以显著提升我国煤矿井下灾害防治水平。为了实时监测打钻过程并提高打钻效率,需要进行煤矿井下钻场目标检测,即对打钻现场所涉及的重要目标进行识别和定位。相对于传统的煤矿井下钻场目标检测方法,基于深度学习的煤矿井下钻场目标检测方法可以提升目标检测的精度、时效性和稳定性,但需依赖高质量的数据集。目前,煤矿井下钻场目标检测研究主要依赖于小规模的私有数据集,难以为深度神经网络模型训练提供充足而可靠的数据。本研究通过采用煤矿用本安型执法记录仪对煤矿井下打钻现场进行拍摄,经过数据清洗、数据标注、专家抽检核查等步骤,构建了标准化的煤矿井下钻场目标检测数据集,并使用主流的YOLO系列目标检测模型进行数据质量评估。本数据集包含了来自不同钻场和环境背景条件下的70948张图片,涵盖了夹持器、钻机卡盘、煤矿工人、矿井安全帽和钻杆等5类目标,并提供了PASCAL VOC格式的标注文件。本数据集可为煤矿井下钻场目标检测研究提供强有力的数据支撑,对推动智能化煤矿井下监测预警具有重要作用。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.

关 键 词：煤矿井下 打钻 深度学习 目标检测 数据集 

分 类 号：TD823[矿业工程—煤矿开采]