YAN Zhirui，WANG Hongwei，GENG Yide. Coal-rock interface image recognition method based on improved DeeplabV3+ and transfer learning[J]. Coal Science and Technology，2023，51（S1）：429−439
. DOI: 10.13199/j.cnki.cst.2022-1392| Citation: |
YAN Zhirui，WANG Hongwei，GENG Yide. Coal-rock interface image recognition method based on improved DeeplabV3+ and transfer learning[J]. Coal Science and Technology，2023，51（S1）：429−439 . DOI: 10.13199/j.cnki.cst.2022-1392
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Coal-rock identification is one of the key technologies to realize intelligent and unmanned mining. To further improve the accuracy and efficiency of coal-rock interface image recognition based on machine vision, a coal-rock interface image recognition network model based on the improved DeeplabV3+ and transfer learning is proposed. Firstly, the lightweight MobilenetV2 module is used as the backbone feature extraction network to reduce the net-work model parameters and improve the semantic segmentation efficiency; Secondly, the Convolutional Block Attention Module (CBAM) is introduced into the encoder and decoder to improve the model feature extraction ability, effectively fuse feature information at different levels, and improve the model segmentation accuracy; Thirdly, the transfer learning training method is adopted to overcome the difference of sample distribution and enhance the generalization of the model, so as to adapt to the coal-rock recognition tasks in different application scenarios. The performance of the model is verified by using the self-made coal rock segmentation data set and the coal rock segmentation dataset of the fully-mechanized mining face. The model is compared with FCN, SegNet, U-net, DeeplabV3+network models, and the accuracy, average intersection ratio, and inference time indexes are selected to evaluate the model recognition effect. The ablation experiment results show that the accuracy and the mean intersection over union of the improved DeeplabV3+ network model in the self-made coal-rock segmentation dataset are 94.67% and 93.48%, respectively, and the test time is 42.58 ms·sheet-1. In addition, the model inference time can reach 6.14 ms·sheet-1 after optimization with the inference acceleration framework TensorRT. Compared with other models, the improved DeeplabV3+ shows stronger ability to extract detailed features of coal-rock boundaries, higher segmentation accuracy and processing efficiency. Finally, the dataset of coal-rock image segmentation with coal-rock layers in the fully mechanized mining face is constructed. The improved DeepLabV3+model is trained and tested on the dataset by using the transfer learning method, which realizes the coal-rock interface image recognition of the underground fully mechanized mining face, and verifies the feasibility and stability of this method in the actual coal rock image recognition task.
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