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GPU-enabled pavement distress image classification in real time

Doycheva, Kristina; Koch, Christian; König, Markus

Authors

Kristina Doycheva

Christian Koch

Markus König



Abstract

Pavement assessment is a crucial process for the maintenance of municipal roads. However, the detection of pavement distress is usually performed either manually or offline, which is not only time-consuming and subjective, but also results in an enormous amount of data being stored persistently before processing. State-of-the-art pavement image processing methods executed on a CPU are not able to analyse pavement images in real time. To compensate this limitation of the methods, we propose an automated approach for pavement distress detection. In particular, GPU implementations of a noise removal, a background correction and a pavement distress detection method were developed. The median filter and the top-hat transform are used to remove noise and shadows in the images. The wavelet transform is applied in order to calculate a descriptor value for classification purposes. The approach was tested on 1549 images. The results show that real-time pre-processing and analysis are possible.

Citation

Doycheva, K., Koch, C., & K├Ânig, M. (2017). GPU-enabled pavement distress image classification in real time. Journal of Computing in Civil Engineering, 31(3), doi:10.1061/(ASCE)CP.1943-5487.0000630. ISSN 0887-3801

Journal Article Type Article
Acceptance Date Jul 19, 2016
Online Publication Date Oct 10, 2016
Publication Date May 30, 2017
Deposit Date Jul 20, 2016
Publicly Available Date Oct 10, 2016
Journal Journal of Computing in Civil Engineering
Print ISSN 0887-3801
Electronic ISSN 1943-5487
Publisher Coasts, Oceans, Ports and Rivers Institute
Peer Reviewed Peer Reviewed
Volume 31
Issue 3
Article Number 04016061
DOI https://doi.org/10.1061/%28ASCE%29CP.1943-5487.0000630
Public URL http://eprints.nottingham.ac.uk/id/eprint/35192
Publisher URL https://ascelibrary.org/doi/10.1061/%28ASCE%29CP.1943-5487.0000630
Copyright Statement Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf
Additional Information No embargo. Updated OL 03.08.2018

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Copyright Statement
Copyright information regarding this work can be found at the following address: http://eprints.nottingham.ac.uk/end_user_agreement.pdf





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