With the development of urban metro, the research on structural diseases of shield tunnels has been becoming a hot research topic, especially the leakage water diseases. Deep learning-based algorithms have shown impressive performance in image processing domain, such as image classification, image recognition or image retrieval. In this paper, we propose a novel image recognition algorithm for water leakage diseases of shield tunnels based on deep learning algorithm. Water leakage images are classified into six categories, each of which are extracted deep representation for image recognition. We compare our method with Otsu algorithm (OA), Region Growing Algorithm (RGA), and Watershed Algorithm (WA) to show the effectiveness of our proposed method.

随着城市地铁的发展，盾构隧道结构病害的研究已成为研究的热点，尤其是渗漏水病害。基于深度学习的算法在图像处理领域表现出了令人印象深刻的性能，例如图像分类，图像识别或图像检索。本文提出了一种基于深度学习算法的盾构隧道渗水病害图像识别新算法。漏水图像分为六类，每类都提取了深层表示以进行图像识别。我们将我们的方法与Otsu算法（OA），区域增长算法（RGA）和分水岭算法（WA）进行了比较，以证明我们提出的方法的有效性。