工矿自动化2026，Vol.52Issue(1)：106-113,139,9.DOI:10.13272/j.issn.1671-251x.2025090105
基于改进YOLOv12的煤矸石智能识别方法
An intelligent coal gangue recognition method based on improved YOLOv12
摘要
Abstract
To address the difficulty of accurately and efficiently recognizing coal gangue caused by complex environmental factors such as high dust concentration and highly variable illumination in mines,this study improved the YOLOv12 network model and proposed an intelligent coal gangue recognition method based on improved YOLOv12.A Dual-Scale Sparse Attention(DSSA)mechanism was designed to enhance the model's attention to multi-scale coal gangue target regions and its spatial perception capability.A Multi-Condition Feature Refinement(MCFR)mechanism was designed to perform condition-guided fusion of deep and shallow features,which effectively enhanced the discriminative representation between coal and coal gangue.A Dynamic Multi-Task Balance Loss(DMTBL)function was constructed to achieve adaptive weight adjustment among localization,classification,and confidence,thereby strengthening the model's learning capability for hard sample regions.Experimental results showed that the improved YOLOv12 achieved a precision,recall,and mAP of 96.5％,94.9％,and 95.8％,respectively,in the coal gangue recognition task,representing improvements of 3.8％,4.5％,and 4.5％over the original YOLOv12,which effectively addressed issues such as missed detection,false positives,and blurred boundaries while maintaining a high inference speed of 47.7 frames per second.Visualization results of activation heatmaps showed that the improved YOLOv12 accurately focused on the target object regions when processing coal gangue with different structures and texture complexities,with no obvious background interference,and the activated regions basically cover the main contours of coal blocks and coal gangue.关键词
煤矸石识别/改进YOLOv12/双尺度稀疏注意力/多条件特征精炼/动态多任务平衡损失Key words
coal gangue recognition/improved YOLOv12/dual-scale sparse attention/multi-condition feature refinement/dynamic multi-task balance loss分类
矿业与冶金引用本文复制引用
周伟,李广棵..基于改进YOLOv12的煤矸石智能识别方法[J].工矿自动化,2026,52(1):106-113,139,9.基金项目
河南省科技攻关项目(252102110375). （252102110375）
