| Citation: |
WANG Dengke，WANG Longhang，QIN Yaguang，et al. Fracture identification and 3D reconstruction of coal-rock combinations based on VRA-UNet network[J]. Coal Science and Technology，2025，53（2）：96−108. DOI: 10.12438/cst.2024-1441
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In the 3D reconstruction of coal-rock combinations fractures, in response to the problem that traditional threshold segmentation methods cannot accurately determine the threshold size between coal and rock, resulting in poor fracture segmentation performance, a new VRA-UNet coal-rock combinations fracture identification model based on deep learning theory is proposed, providing an optimized solution for accurate identification of coal-rock combinations fractures. Firstly, the VGG16 module is used as the backbone feature extraction network to enhance the model’s generalization ability and prevent the initialization of model parameters from being too random. Secondly, to address the complex fracture topology and strong non-uniformity of coal-rock combinations, an attention module (ResCBAM) with spatial and channel dimensions is introduced into the up-sampling part to enhance the model's feature extraction ability and alleviate the problem of gradient disappearance. Finally, an asymmetric atrous pyramid module (AC-ASPP) utilizing convolution kernels of different scales is added at the end of the downsampling, which reduced the computational complexity and improved the computational efficiency of the model while keeping the receptive field unchanged. The effectiveness of the model is verified using a dataset of CT scan images of coal-rock combinations. The research results indicate that the VRA-UNet model performs well in crack extraction and recognition, with an average intersection to union ratio, pixel average value, and recognition accuracy of 85.22%, 90.80%, and 91.95%, respectively; Compared with mainstream segmentation networks UNet, PSPNet, DeeplabV3+, FCN, and SegNet the average intersection to union ratio of the VRA-UNet model has increased by 6.05%, 16.7%, 10.77%, 6.87%, and 6.4% respectively. The average pixel value has increased by 7.13%, 13.29%, 12.84%, 7.4%, and 7.53% and the recognition accuracy has risen by 3.82%, 14.45%, 7.4%, 5.58%, and 4.31% respectively; The fractal dimension of the fracture structure identified by VRA-UNet maintains good consistency with the fractal dimension of the original CT scan fracture structure, accurately reproducing the distribution characteristics of the internal fracture structure of the coal-rock combinations.
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