Crack detection is one of the most important works in the system of pavement management. Cracks do not have a certain shape and the appearance of cracks usually changes drastically in different lighting conditions, making it hard to be detected by the algorithm with imagery analytics. To address these issues, we propose an effective U-shaped fully convolutional neural network called UCrackNet. First, a dropout layer is added into the skip connection to achieve better generalization. Second, pooling indices is used to reduce the shift and distortion during the up-sampling process. Third, four atrous convolutions with different dilation rates are densely connected in the bridge block, so that the receptive field of the network could cover each pixel of the whole image. In addition, multi-level fusion is introduced in the output stage to achieve better performance. Evaluations on the two public CrackTree206 and AIMCrack datasets demonstrate that the proposed method achieves high accuracy results and good generalization ability.
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Opto-Electronic Engineering
ISSN: 1003-501X
CN: 51-1346/O4
Monthly, included in CA, Scopus, CSCD
CN: 51-1346/O4
Monthly, included in CA, Scopus, CSCD
Pavement crack detection based on the U-shaped fully convolutional neural network
Author Affiliations
Correspondence: ymh@zjut.edu.cn
First published at:Dec 22, 2020
Abstract
References
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Funds:
National Natural Science Foundation of China (61871350) and Zhejiang Provincial National Science Foundation of China (GG19E050005)
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Chen Hanshen, Yao Minghai, Qu Xinyu. Pavement crack detection based on the U-shaped fully convolutional neural network[J]. Opto-Electronic Engineering, 2020, 47(12): 200036.
