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Adaptive Road Crack Detection System by Pavement Classification

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Author(s): Miguel Gavilán | David Balcones | Oscar Marcos | David F. Llorca | Miguel A. Sotelo | Ignacio Parra | Manuel Ocaña | Pedro Aliseda | Pedro Yarza | Alejandro Amírola

Journal: Sensors
ISSN 1424-8220

Volume: 11;
Issue: 10;
Start page: 9628;
Date: 2011;
Original page

Keywords: road distress detection | road surface classification | linear features | multi-class SVM | local binary pattern | gray-level co-occurrence matrix

ABSTRACT
This paper presents a road distress detection system involving the phases needed to properly deal with fully automatic road distress assessment. A vehicle equipped with line scan cameras, laser illumination and acquisition HW-SW is used to storage the digital images that will be further processed to identify road cracks. Pre-processing is firstly carried out to both smooth the texture and enhance the linear features. Non-crack features detection is then applied to mask areas of the images with joints, sealed cracks and white painting, that usually generate false positive cracking. A seed-based approach is proposed to deal with road crack detection, combining Multiple Directional Non-Minimum Suppression (MDNMS) with a symmetry check. Seeds are linked by computing the paths with the lowest cost that meet the symmetry restrictions. The whole detection process involves the use of several parameters. A correct setting becomes essential to get optimal results without manual intervention. A fully automatic approach by means of a linear SVM-based classifier ensemble able to distinguish between up to 10 different types of pavement that appear in the Spanish roads is proposed. The optimal feature vector includes different texture-based features. The parameters are then tuned depending on the output provided by the classifier. Regarding non-crack features detection, results show that the introduction of such module reduces the impact of false positives due to non-crack features up to a factor of 2. In addition, the observed performance of the crack detection system is significantly boosted by adapting the parameters to the type of pavement.

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