采矿与岩层控制工程学报2025，Vol.7Issue(1)：94-108,15.DOI:10.13532/j.jmsce.cn10-1638/td.2024-1246
基于优化的U-net网络掘进工作面煤岩识别方法研究
Research on coal and rock identification method of excavation working face based on optimized U-net network
摘要
Abstract
To improve the accuracy of coal rock recognition,this study collected the original images of coal rock from the excavation face in Yushujing coal mine of Shanghai Temple Mining Co.Inner Mongolia,and produced a deep learning dataset.The dataset is trained by three kinds of network models,including FCN fully convolutional neural network(FCN network),U-net Semantic Segmentation Network(U-net Network),and U-net Network improved by adding Canny Edge Detection Algorithm,and the training results were compared and analyzed.The results show that the accuracy of the three network models is 89.25%,93.52%and 94.55%,respectively.When the number of training times reaches 100,the accuracy of the improved U-net network model increased by 1.03%.In coal rock identification,the U-net network model achieved higher accuracy than the FCN network model and showed better performance in the testing session.In the prediction session,the recognition of the edge part of the coal rock was achieved with more accurate treatment.The method can provide a reference for improvement of the accuracy of coal rock recognition.关键词
煤岩识别/深度学习/U-net网络/Canny边缘检测算法Key words
coal-rock identification/deep learning/U-net network/Canny edge detection algorithm分类
矿山工程引用本文复制引用
栾恒杰,杨玉晴,刘建康,蒋宇静,刘建荣,马德良,张孙豪..基于优化的U-net网络掘进工作面煤岩识别方法研究[J].采矿与岩层控制工程学报,2025,7(1):94-108,15.基金项目
国家自然科学基金资助项目(52204099) （52204099）
山东省自然科学基金资助项目(ZR2022QE203) （ZR2022QE203）
省部共建矿山岩层智能控制与绿色开采国家重点实验室培育基地开放基金资助项目(MDPC2024ZR03) （MDPC2024ZR03）
