Abstract

This paper presents a deep learning (DL)-based method for the instance segmentation of cracks from shield tunnel lining images using a mask region-based convolutional neural network (Mask R-CNN) incorporated with a morphological closing operation. The Mask R-CNN herein is divided into a backbone architecture, a region proposal network (RPN), and a head architecture for specification, and the implementation details are introduced. Compared with the current image processing methods, the proposed DL-based method efficiently detects cracks in an image while simultaneously generating a high-quality segmentation mask for each crack. A shield tunnel lining image dataset is established for crack instance segmentation task. The established dataset contains a total of 1171 labelled crack instances in 761 images. The morphological closing operation was incorporated into a Mask R-CNN to form an integrated model to connect disjoint cracks that belong to one crack. Image tests were carried out among four trained models to explore the effect of the morphological closing operation, network depth, and feature pyramid network on crack segmentation performance, and a relative optimal model is found. The relative optimal model achieves a balanced accuracy of 81.94%, a F₁ score of 68.68%, and an intersection over union (IoU) of 52.72% with respect to 76 test images.

摘要

本文提出了一种基于深度学习（DL）的方法，该方法使用结合了形态学闭合操作的基于遮罩区域的卷积神经网络（Mask R-CNN）从盾构隧道衬砌图像中进行裂纹实例分割。这里的Mask R-CNN分为骨干架构，区域提议网络（RPN）和用于规范的头部架构，并介绍了实现细节。与当前的图像处理方法相比，所提出的基于DL的方法可以有效地检测图像中的裂缝，同时为每个裂缝生成高质量的分割蒙版。建立了盾构隧道衬砌图像数据集，用于裂纹实例分割任务。建立的数据集总共包含761张图像中的1171个标记裂纹实例。形态封闭操作被合并到Mask R-CNN中，以形成一个集成模型来连接属于一个裂纹的不相交的裂纹。在四个受过训练的模型中进行了图像测试，以探索形态闭合操作，网络深度和特征金字塔网络对裂纹分割性能的影响，并找到了相对最佳的模型。相对最优模型的平衡精度达到了81.94％，相对于76张测试图像，得分₁为68.68％，联合相交（IoU）为52.72％。