Water leakage image recognition of shield tunnel via learning deep feature representation

被引：36

作者：

Xiong, Leijin ^{[1
]}

Zhang, Dingli ^{[1
]}

Zhang, Yu ^{[1
]}

机构：

[1] Beijing Jiaotong Univ, Key Lab Urban Underground Engn, Minist Educ, Beijing 100044, Peoples R China

来源：

JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION | 2020年 / 71卷

基金：

国家重点研发计划;

关键词：

Shield tunnel; Water leakage; Deep learning; Image recognition; OBJECT DETECTION; CONSTRUCTION;

D O I：

10.1016/j.jvcir.2019.102708

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

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. (C) 2019 Elsevier Inc. All rights reserved.

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