首页|Research on coal-rock identification method and data augmentation algorithm of comprehensive working face based on FL-Segformer
Research on coal-rock identification method and data augmentation algorithm of comprehensive working face based on FL-Segformer
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
万方数据
Coal-rock interface identification technology was pivotal in automatically adjusting the shearer's cutting drum during coal mining.However,it also served as a technical bottleneck hindering the advancement of intelligent coal mining.This study aimed to address the poor accuracy of current coal-rock identification technology on comprehensive working faces,coupled with the limited availability of coal-rock datasets.The loss function of the SegFormer model was enhanced,the model's hyperparameters and learning rate were adjusted,and an automatic recognition method was proposed for coal-rock interfaces based on FL-SegFormer.Additionally,an experimental platform was constructed to simulate the dusty environment during coal-rock cutting by the shearer,enabling the collection of coal-rock test image datasets.The morphology-based algorithms were employed to expand the coal-rock image datasets through image rotation,color dither-ing,and Gaussian noise injection so as to augment the diversity and applicability of the datasets.As a result,a coal-rock image dataset comprising 8424 samples was generated.The findings demonstrated that the FL-SegFormer model achieved a Mean Intersection over Union(MIoU)and mean pixel accuracy(MPA)of 97.72％and 98.83％,respectively.The FL-SegFormer model outperformed other models in terms of recognition accuracy,as evidenced by an MIoU exceeding 95.70％of the original image.Furthermore,the FL-SegFormer model using original coal-rock images was validated from No.15205 working face of the Yulin test mine in northern Shaanxi.The calculated average error was only 1.77％,and the model operated at a rate of 46.96 frames per second,meeting the practical application and deployment requirements in underground settings.These results provided a theoretical foundation for achieving automatic and efficient mining with coal mining machines and the intelligent development of coal mines.
Coal-rock interface recognitionSegformerDatasets augmentationComprehensive working faceImage semantic segmentation
Yun Zhang、Liang Tong、Xingping Lai、Shenggen Cao、Baoxu Yan、Yanbin Yang、Yongzi Liu、Wei He
展开 >
College of Energy Engineering,Xi'an University of Science& Technology,Xi'an,Shaanxi 710054,China
State Key Laboratory of Coal Resources and Safe Mining,China University of Mining & Technology,Xuzhou,Jiangsu 221116,China
NETL
NSTL
万方数据
