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A Two-Stage Crack Detection Method for Concrete Bridges Using Convolutional Neural Networks

Yundong LI, Weigang ZHAO, Xueyan ZHANG, Qichen ZHOU

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Summary :

Crack detection is a vital task to maintain a bridge's health and safety condition. Traditional computer-vision based methods easily suffer from disturbance of noise and clutters for a real bridge inspection. To address this limitation, we propose a two-stage crack detection approach based on Convolutional Neural Networks (CNN) in this letter. A predictor of small receptive field is exploited in the first detection stage, while another predictor of large receptive field is used to refine the detection results in the second stage. Benefiting from data fusion of confidence maps produced by both predictors, our method can predict the probability belongs to cracked areas of each pixel accurately. Experimental results show that the proposed method is superior to an up-to-date method on real concrete surface images.

Publication
IEICE TRANSACTIONS on Information Vol.E101-D No.12 pp.3249-3252
Publication Date
2018/12/01
Publicized
2018/09/05
Online ISSN
1745-1361
DOI
10.1587/transinf.2018EDL8150
Type of Manuscript
LETTER
Category
Artificial Intelligence, Data Mining

Authors

Yundong LI
  North China University of Technology
Weigang ZHAO
  Shijiazhuang Tiedao University
Xueyan ZHANG
  North China University of Technology
Qichen ZHOU
  North China University of Technology

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