Automatic pavement crack classification on two-dimensional VIAPIX images

Wissam Kaddah; Marwa Elbouz; Yousri Ouerhani; Ayman Alfalou; Marc Desthieux

doi:10.1117/12.2518839

13 May 2019 Automatic pavement crack classification on two-dimensional VIAPIX images

Wissam Kaddah, Marwa Elbouz, Yousri Ouerhani, Ayman Alfalou, Marc Desthieux

Proceedings Volume 10995, Pattern Recognition and Tracking XXX; 1099508 (2019) https://doi.org/10.1117/12.2518839
Event: SPIE Defense + Commercial Sensing, 2019, Baltimore, MD, United States

Abstract

Road maintenance management presents a complex task for road authorities. The first presumption for the evaluation analysis and correct road construction rehabilitation is to have precise and up-to-date information about road pavement condition and level degradation. Different road crack types were proposed in the state of art in order to provide useful information for making pavement maintenance strategies. For this reason, we present in this paper a novel research to automatically detect and classify road cracks on two-dimensional digital images. Indeed, our proposed package is composed of two methods: crack detection and crack classification. The first method consists in detecting the cracks on images acquired by the VIAPIX® system developed by our company ACTRIS. To do so, we are based on our unsupervised approach cited in for road crack detection on two-dimensional pavement images. Then, in order to categorize each of the detected cracks, the second method of our package is applied. Based on principal component analysis (PCA), our method permits the classification of all the detected cracks into three types: vertical, horizontal, and oblique. The obtained results demonstrate the efficiency of our robust approaches in terms of good detection and classification on a variety of pavement images.

Conference Presentation

Citation Download Citation

Wissam Kaddah, Marwa Elbouz, Yousri Ouerhani, Ayman Alfalou, and Marc Desthieux "Automatic pavement crack classification on two-dimensional VIAPIX images", Proc. SPIE 10995, Pattern Recognition and Tracking XXX, 1099508 (13 May 2019); https://doi.org/10.1117/12.2518839

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