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

Yundong LI  Weigang ZHAO  Xueyan ZHANG  Qichen ZHOU  

IEICE TRANSACTIONS on Information and Systems   Vol.E101-D   No.12   pp.3249-3252
Publication Date: 2018/12/01
Online ISSN: 1745-1361
DOI: 10.1587/transinf.2018EDL8150
Type of Manuscript: LETTER
Category: Artificial Intelligence, Data Mining
crack detection,  two-stage predictors,  convolutional neural networks,  bridge inspection,  

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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.