为解决煤岩CT裂隙图像识别中矸石影响以及不同尺度裂隙识别的问题，设计并实现了一种基于深度学习的煤岩裂隙提取网络模型(MCSN)，该模型基于U-Net网络，利用其编码器－解码器结构和跳跃连接，可实现从复杂煤岩体中分割出完整的裂隙结构图像。首先，通过煤岩工业CT扫描系统获取煤岩体内部扫描图片后，人工标注出CT图像中的裂隙结构，并利用数据增强扩充标注的原始数据制作出煤岩CT裂隙数据集；然后，将训练好的VGG16模型权重通过迁移学习技术移至U-Net编码器部分，使得整个主干特征提取网络具有更强的裂隙结构特征提取能力；同时采用深度可分离空洞卷积模块(DCAC)和残差模块对U-Net模型中解码器部分进行改进，有效提升了CT图像中裂隙结构的识别能力，展现出了优越的分割精度和鲁棒性。为验证提出的煤岩裂隙提取网络模型的有效性，将MCSN的提取结果与经典的卷积神经网络及阈值分割方法的结果进行了对比，实验对比结果显示，提出的模型在定性分析和定量分析方面优势明显。这种多尺度融合的策略可以有效提取出复杂煤岩体图像中的裂隙，提高了裂隙识别效率和精度。将该模型应用到巷道围岩钻孔裂隙识别中，通过对钻孔成像仪采集到的窥孔视频和平面展开图进行裂隙提取，并结合二者提取结果进行交叉验证，得到了精准的巷道围岩裂隙分布范围，给出了穿层抽采钻孔的注浆封孔范围，提高了煤层瓦斯抽采体积分数。

To address the challenges of fracture recognition in the CT scanning images of coal or rock, particularly the interference of gangue and the recognition of fractures at different scales, proposed and implemented a network model for coal-rock fracture extraction based on deep learning (MCSN). According to the U-Net architecture, the model utilized its encoder-decoder structure and skip connections to segment the fracture structure images from complex coal-rock body images. Firstly, the fracture structures in the CT scanning images were annotated manually using the internal scan images captured by a coal industrial CT scanning system. And the annotated original data was augmented to create a coal-rock CT fracture dataset. Subsequently, to make the extraction network of main features have a stronger extraction capability of fracture structure features, the weights of a pre-trained VGG16 model were transferred to the U-Net encoder through a transfer learning technique. Simultaneously, the decoder part of the U-Net model was improved using the deep separable dilated convolutional modules (DCAC) and residual modules to effectively boost the recognition capability of fracture structures in the CT images, demonstrating a superior segmentation accuracy and robustness. To validate the effectiveness of the coal-rock fracture extraction network model proposed, the results obtained by the MCSN were compared with those of classical convolutional neural networks and threshold segmentation methods. Experimental comparisons revealed a significant advantage of the model proposed in both qualitative and quantitative analyses. The proposed model, employing a multi-scale fusion strategy, demonstrated the capability to effectively extract fractures in complex coal-rock images, thereby enhancing the efficiency and accuracy of fracture identification. The model was applied to the identification of fractures in roadway surrounding rock based on borehole imaging. Fracture extraction was performed through the analysis of borehole videos and planar unfolded images collected by a borehole imaging instrument. The results from both sources were cross-validated to obtain an accurate distribution of fractures in roadway surrounding rock. Furthermore, the model provides guidelines for the injection and sealing of boreholes, increasing volume fraction of coal seam gas extraction.