The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
균열 감지는 교량의 건강과 안전 상태를 유지하는 데 중요한 작업입니다. 기존의 컴퓨터 비전 기반 방법은 실제 교량 검사 시 소음 교란 및 혼란으로 인해 쉽게 어려움을 겪습니다. 이러한 한계를 해결하기 위해 우리는 이 편지에서 CNN(Convolutional Neural Networks)을 기반으로 한 XNUMX단계 균열 탐지 접근 방식을 제안합니다. 작은 수용 필드의 예측 변수는 첫 번째 탐지 단계에서 활용되는 반면, 큰 수용 필드의 또 다른 예측 변수는 두 번째 단계에서 탐지 결과를 구체화하는 데 사용됩니다. 두 예측변수에 의해 생성된 신뢰도 맵의 데이터 융합을 통해 우리의 방법은 각 픽셀의 균열된 영역에 속하는 확률을 정확하게 예측할 수 있습니다. 실험 결과는 제안된 방법이 실제 콘크리트 표면 영상에서 최신 방법보다 우수함을 보여주었다.
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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부
Yundong LI, Weigang ZHAO, Xueyan ZHANG, Qichen ZHOU, "A Two-Stage Crack Detection Method for Concrete Bridges Using Convolutional Neural Networks" in IEICE TRANSACTIONS on Information,
vol. E101-D, no. 12, pp. 3249-3252, December 2018, doi: 10.1587/transinf.2018EDL8150.
Abstract: 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.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2018EDL8150/_p
부
@ARTICLE{e101-d_12_3249,
author={Yundong LI, Weigang ZHAO, Xueyan ZHANG, Qichen ZHOU, },
journal={IEICE TRANSACTIONS on Information},
title={A Two-Stage Crack Detection Method for Concrete Bridges Using Convolutional Neural Networks},
year={2018},
volume={E101-D},
number={12},
pages={3249-3252},
abstract={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.},
keywords={},
doi={10.1587/transinf.2018EDL8150},
ISSN={1745-1361},
month={December},}
부
TY - JOUR
TI - A Two-Stage Crack Detection Method for Concrete Bridges Using Convolutional Neural Networks
T2 - IEICE TRANSACTIONS on Information
SP - 3249
EP - 3252
AU - Yundong LI
AU - Weigang ZHAO
AU - Xueyan ZHANG
AU - Qichen ZHOU
PY - 2018
DO - 10.1587/transinf.2018EDL8150
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E101-D
IS - 12
JA - IEICE TRANSACTIONS on Information
Y1 - December 2018
AB - 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.
ER -