复杂环境下炮孔智能检测混合神经网络模型OA

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.