矿业科学学报2026，Vol.11Issue(2)：265-275,11.DOI:10.19606/j.cnki.jmst.2025093
复杂环境下炮孔智能检测混合神经网络模型
A hybrid neural network model for intelligent blasthole detection in complex environments
摘要
Abstract
To address the difficulty of blasthole detection during the charging phase of drill-and-blast tunnelling,which is aggravated by dust interference and insufficient illumination,this study proposes an intelligent blasthole detection model based on a hybrid neural network.First,a multi-class classifi-cation module accurately categorises blasthole images acquired in complex environments;a feature transformation module then converts these images into equivalent ones with a clear background.Subse-quently,a dedicated blasthole detection module identifies the blastholes and localises their positions.By strengthening the feature-extraction capability of deformable convolutions,introducing a triple-atten-tion mechanism,and refining the loss function,the model achieves a significant improvement in detec-tion accuracy under adverse conditions.Experimental results demonstrate that,in complex environ-ments,the proposed model attains a detection precision of 94.47%and a recall of 86.32%.Com-pared with state-of-the-art deep-learning object detectors,the proposed model exhibits superior robust-ness and blasthole detection capability,reliably identifying blasthole locations that conventional models often miss,thereby providing a solid foundation for intelligent charging in tunnelling excavation.关键词
炮孔检测/复杂环境/智能装药/隧道智能建造/深度学习Key words
blasthole detection/complex environment/intelligent charging/intelligent tunnel construc-tion/deep learning分类
矿业与冶金引用本文复制引用
金庆雨,岳中文,周星源,陈佳瑶,刘化强..复杂环境下炮孔智能检测混合神经网络模型[J].矿业科学学报,2026,11(2):265-275,11.基金项目
国家自然科学基金(52174094) （52174094）
国家重点研发计划(2021YFC2902103) （2021YFC2902103）
