Journal
IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS
Volume E101D, Issue 12, Pages 3249-3252Publisher
IEICE-INST ELECTRONICS INFORMATION COMMUNICATIONS ENG
DOI: 10.1587/transinf.2018EDL8150
Keywords
crack detection; two-stage predictors; convolutional neural networks; bridge inspection
Funding
- Beijing Natural Science Foundation [4182020]
- Key Laboratory for Health Monitoring and Control of Large Structures, Shijiazhuang
Ask authors/readers for more resources
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.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available