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Concrete cracks detection based on FCN with dilated convolution

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成果类型:
期刊论文
作者:
Zhang, Jianming;Lu, Chaoquan;Wang, Jin*;Wang, Lei;Yue, Xiao-Guang
通讯作者:
Wang, Jin
作者机构:
[Zhang, Jianming; Wang, Jin; Lu, Chaoquan] Changsha Univ Sci & Technol, Sch Comp & Commun Engn, Hunan Prov Key Lab Intelligent Proc Big Data Tran, Changsha 410114, Hunan, Peoples R China.
[Wang, Jin] Fujian Univ Technol, Sch Informat Sci & Engn, Fuzhou 350118, Fujian, Peoples R China.
[Wang, Lei] Changsha Univ Sci & Technol, Sch Civil Engn, Changsha 410114, Hunan, Peoples R China.
[Yue, Xiao-Guang] Rajamangala Univ Technol Rattanakosin, Rattanakosin Int Coll Creat Entrepreneurship, Nakhon Pathom 73170, Thailand.
通讯机构:
[Wang, Jin] C
[Wang, Jin] F
Changsha Univ Sci & Technol, Sch Comp & Commun Engn, Hunan Prov Key Lab Intelligent Proc Big Data Tran, Changsha 410114, Hunan, Peoples R China.
Fujian Univ Technol, Sch Informat Sci & Engn, Fuzhou 350118, Fujian, Peoples R China.
语种:
英文
关键词:
Crack detection;Dilated convolution;FCN;Residual network;Semantic segmentation
期刊:
Applied Sciences-Basel
ISSN:
2076-3417
年:
2019
卷:
9
期:
13
基金类别:
Funding: This research was funded in part by the National Natural Science Foundation of China under Grant Nos. 61772454, 61811540410 and 61811530332, the Scientific Research Fund of Hunan Provincial Education Department under Grant No. 16A008, the Postgraduate Scientific Research Innovation Fund of Hunan Province under Grant No. CX2018B565, the "Double First-class" International Cooperation and Development Scientific Research Project of Changsha University of Science and Technology under Grant No. 2019IC34, and the Postgraduate Training Innovation Base Construction Project of Hunan Province under Grant No. 2017-451-30.
机构署名:
本校为第一且通讯机构
院系归属:
计算机与通信工程学院
土木工程学院
摘要:
In civil engineering, the stability of concrete is of great significance to safety of people's life and property, so it is necessary to detect concrete damage effectively. In this paper, we treat crack detection on concrete surface as a semantic segmentation task that distinguishes background from crack at the pixel level. Inspired by Fully Convolutional Networks (FCN), we propose a full convolution network based on dilated convolution for concrete crack detection, which consists of an encoder and a decoder. Specifically, we first used the resi...

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