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Unified Approach to Pavement Crack and Sealed Crack Detection Using Preclassification Based on Transfer Learning

Zhang, Kaige / Cheng, H. D. / Zhang, Boyu

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 $2,000 \times 4,000 pixels$ ); the experimental results demonstrate that this approach accurately distinguishes cracks from sealed cracks and achieves very good detection performance ( $recall = 0.951$ ; $precision = 0.847$ ).

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Unified Approach to Pavement Crack and Sealed Crack Detection Using Preclassification Based on Transfer Learning

Zhang, Kaige / Cheng, H. D. / Zhang, Boyu

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 $2,000 \times 4,000 pixels$ ); the experimental results demonstrate that this approach accurately distinguishes cracks from sealed cracks and achieves very good detection performance ( $recall = 0.951$ ; $precision = 0.847$ ).

Unified Approach to Pavement Crack and Sealed Crack Detection Using Preclassification Based on Transfer Learning

Zhang, Kaige / Cheng, H. D. / Zhang, Boyu

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 $2,000 \times 4,000 pixels$ ); the experimental results demonstrate that this approach accurately distinguishes cracks from sealed cracks and achieves very good detection performance ( $recall = 0.951$ ; $precision = 0.847$ ).

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Titel:

Unified Approach to Pavement Crack and Sealed Crack Detection Using Preclassification Based on Transfer Learning

Beteiligte:

Zhang, Kaige (Autor:in) / Cheng, H. D. (Autor:in) / Zhang, Boyu (Autor:in)

Erschienen in:

Journal of Computing in Civil Engineering ; 32

Erscheinungsdatum:

05.01.2018

ISSN:

0887-3801 , 1943-5487

DOI:

https://doi.org/10.1061/(ASCE)CP.1943-5487.0000736

Medientyp:

Aufsatz (Zeitschrift)

Format:

Elektronische Ressource

Sprache:

Unbekannt

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