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Adaptive Road Crack Detection System by Pavement Classification
Computer Engineering Department, Polytechnic School, University of Alcalá, Alcalá de Henares, Madrid 28871, Spain
Infrastructure Management Division, ACCIONA Engineering, c\Marcelina 3, Madrid 28029, Spain
* Author to whom correspondence should be addressed.
Received: 27 August 2011; in revised form: 27 September 2011 / Accepted: 9 October 2011 / Published: 12 October 2011
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.
Keywords: road distress detection; road surface classification; linear features; multi-class SVM; local binary pattern; gray-level co-occurrence matrix
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Cite This Article
MDPI and ACS Style
Gavilán, M.; Balcones, D.; Marcos, O.; Llorca, D.F.; Sotelo, M.A.; Parra, I.; Ocaña, M.; Aliseda, P.; Yarza, P.; Amírola, A. Adaptive Road Crack Detection System by Pavement Classification. Sensors 2011, 11, 9628-9657.
Gavilán M, Balcones D, Marcos O, Llorca DF, Sotelo MA, Parra I, Ocaña M, Aliseda P, Yarza P, Amírola A. Adaptive Road Crack Detection System by Pavement Classification. Sensors. 2011; 11(10):9628-9657.
Gavilán, Miguel; Balcones, David; Marcos, Oscar; Llorca, David F.; Sotelo, Miguel A.; Parra, Ignacio; Ocaña, Manuel; Aliseda, Pedro; Yarza, Pedro; Amírola, Alejandro. 2011. "Adaptive Road Crack Detection System by Pavement Classification." Sensors 11, no. 10: 9628-9657.