工矿自动化2026，Vol.52Issue(3)：83-94,12.DOI:10.13272/j.issn.1671-251x.2026030038
基于CAF-YOLO的煤矿井下打钻作业识别与工序判别
Drilling operation recognition and process classification in underground coal mines based on CAF-YOLO
摘要
Abstract
Underground coal mine drilling scenes exhibit imaging degradation characteristics such as uneven illumination,cluttered background,and strong reflection coexisting.Meanwhile,targets such as drill rods and drill tails present slender and continuous structures with significant orientation variations,and occlusion and overlap frequently occur during drilling-in and drilling-out processes.The large variation in target scale and strong background texture interference make conventional detection relying only on horizontal bounding boxes difficult to meet engineering requirements.Oriented Bounding Box(OBB)detection has stronger angle representation and long-range dependency modeling capability,which makes the results of target localization,category classification,and angle description of bounding boxes more accurate.A method for drilling operation recognition and process classification in underground coal mines based on an oriented object detection model,CAF-YOLO,was proposed.CAF-YOLO was improved based on YOLOv13n-OBB.In the backbone network,a RepLKNet large-kernel structure module was introduced,and the dual-path collaborative modeling of large-kernel convolution and small-kernel convolution enhanced long-range contextual perception and improved the capability of continuous feature representation of slender structural targets such as drill rods and drill tails under occlusion and complex backgrounds.In the neck network,an adaptive feature fusion module was introduced,which combined local attention and global attention to generate fusion weights,dynamically reweighted and reorganized cross-layer features,suppressed redundant background responses,and enhanced discriminative features of small and occluded targets.Focal Loss was adopted in the classification branch to assign higher weights to hard samples,which alleviated the imbalance between positive and negative samples and between easy and hard samples,thereby improving the robustness and generalization performance of the model under complex underground working conditions.On this basis,a temporal state classification method for drilling videos was further constructed.The method utilized the spatial position,motion state,and personnel proximity information of key targets for video-level modeling to achieve accurate classification of drilling processes.Experimental results on the self-built UCMDO-OBB dataset showed that,compared with YOLOv13n-OBB,the precision of CAF-YOLO increased by 1.8％,the recall increased by 4.3％,mAP@0.5 increased by 3.2％,and mAP@0.5:0.95 increased by 3.3％,indicating significant improvement.In the process classification task,the accuracy reached 92.7％,the macro-average F1 score reached 0.912,and the mean absolute percentage error of the advancing stage was 4.1％.The proposed method effectively enabled video-level process analysis and provided technical support for intelligent monitoring and safety management of underground drilling operations.关键词
打钻识别/YOLOv13n/旋转目标检测/旋转框/上下文增强/自适应特征融合/时序状态判别Key words
drilling operation recognition/YOLOv13n/oriented object detection/oriented bounding box/context enhancement/adaptive feature fusion/temporal state classification分类
矿业与冶金引用本文复制引用
瞿雨馨,张富凯,郭峰,蔡琛昂,李爱军,董璐,常文静,孙一菲..基于CAF-YOLO的煤矿井下打钻作业识别与工序判别[J].工矿自动化,2026,52(3):83-94,12.基金项目
国家自然科学基金项目(62202145) （62202145）
河南省科技攻关项目(252102320210). （252102320210）
