针对页岩层理构造发育、裂缝细小的特点，提出了一种基于UU-Net（multi-path U-shaped network）深度学习模型的页岩露头裂缝自动识别深度学习模型。首先，对裂缝图像数据集进行手工标注和二值化预处理；然后，将数据集分为训练集和测试集，通过参数优化迭代对模型进行训练和验证，得到精度符合要求的露头多尺度裂缝识别模型；最后，将该模型应用于四川盆地页岩露头图像和岩石薄片图像上分别进行不同尺度的裂缝提取，并定量统计裂缝的长度和倾角信息。实际应用结果表明：相比于手工标注，深度学习裂缝识别效率明显提高；大尺度裂缝识别准确率达到98%，中小尺度裂缝识别准确率约为96%，微裂缝识别准确率约为94%；训练的模型可以应用于其他区块页岩露头或岩石薄片图像的多尺度裂缝识别中，模型具有较好的泛化能力。

Aiming at the characteristics of shale bedding structures and fine fracture developments, an automatic multi-scale fracture recognition method for shale outcrop images based on a UU-Net (multi-path U-shaped network) deep neural network was presented. The process begins with the manual labeling and binarization of fracture image datasets. The datasets were then divided into training and testing subsets. Through iterative parameter optimization, the model was trained and validated to achieve the desired accuracy, resulting in a multi-scale fracture recognition model for outcrop images. The model was subsequently applied to shale outcrop images and rock slice images from the Sichuan Basin to extract fractures at different scales. Quantitative statistics on fracture length and dip angle information were provided. The application results demonstrate as follows. Compared to manual labeling, the efficiency of crack recognition using deep learning is significantly improved. The accuracy of large-scale fracture identification is 98%, while the accuracy for medium- and small-scale fractures is approximately 96%, and for micro-fractures, it is about 94%. The trained model can be applied to multi-scale fracture identification of shale outcrops or rock slice images in other regions, indicating strong generalization capabilities.