Journal
JOURNAL OF COMPUTING IN CIVIL ENGINEERING
Volume 32, Issue 2, Pages -Publisher
ASCE-AMER SOC CIVIL ENGINEERS
DOI: 10.1061/(ASCE)CP.1943-5487.0000736
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This work focuses on solving two challenging problems in pavement crack detection: (1)noises caused by complicated pavement textures and intensity inhomogeneity cannot be removed effectively, which makes crack extraction difficult; and (2)sealed cracks and cracks with similar intensity and width cannot be separated correctly, which makes data analysis and budgeting inaccurate. Here, a unified crack and sealed crack detection approach is proposed that can detect and separate both cracks and sealed cracks under the same framework. It trains a deep convolutional neural network to preclassify a pavement image into crack, sealed crack, and background regions. A blockwise thresholding method is developed to segment the crack/sealed crack pixels efficiently and effectively. Finally, tensor voting-based curve detection is applied to extract the crack/sealed crack. The proposed approach is validated using 800 images (each 2,000 x 4,000pixels); the experimental results demonstrate that this approach accurately distinguishes cracks from sealed cracks and achieves very good detection performance (recall = 0.951; precision = 0.847). (C) 2018 American Society of Civil Engineers.
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