This paper develops a deep learning-based approach that extends the PANet model by adding a semantic branch which refines the process of crack evaluation to reduce inaccuracies associated with crack discontinuities and image skeletonization. The PANet model is used to segment cracks from shield tunnel lining images, and an A* algorithm incorporated into the semantic branch computes the width and the shortest length of each crack from the segmented binary images. A comparison of experimental results shows that the performance of the proposed approach is better than those of Mask R-CNN, U-Net, and DeepCrack. The proposed approach also demonstrates its superiority at mitigating crack disjoint problems and skeletonization error. The error rates of length and width quantification for the A* algorithm are lower than those for the medial-axis-skeletonizing algorithm. The evaluation metrics indicate that the proposed model is an alternative approach to segment and quantify cracks in the field.

本文开发了一种基于深度学习的方法，通过添加语义分支来扩展 PANet 模型，该分支细化裂纹评估过程，以减少与裂纹不连续性和图像骨架化相关的不准确性。PANet 模型用于从盾构隧道衬砌图像中分割裂缝，语义分支中包含的 A* 算法从分割的二进制图像中计算每个裂缝的宽度和最短长度。实验结果对比表明，所提方法的性能优于 Mask R-CNN、U-Net 和 DeepCrack。所提出的方法还证明了其在减轻裂纹不相交问题和骨架化错误方面的优越性。A*算法的长度和宽度量化的错误率低于中轴骨架化算法的错误率。评估指标表明，所提出的模型是一种在现场分割和量化裂缝的替代方法。