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 inte...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 dithering,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 FLSegFormer model outperformed other models in terms of recognition accuracy,as evidenced by an MloU 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.展开更多
基金funded by the National Natural Science Foundation of China(52004201,52274143,52204153)China Postdoctoral Science Foundation(2021T140551).
文摘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 dithering,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 FLSegFormer model outperformed other models in terms of recognition accuracy,as evidenced by an MloU 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.
