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• Gavilán, M
• Balcones, D
• Marcos, O
• Llorca, D
• Sotelo, M
• Parra, I
• Ocaña, M
• Aliseda, P
• Yarza, P
• Amírola, A
• [+] on Google Scholar
• Gavilán, M
• Balcones, D
• Marcos, O
• Llorca, D
• Sotelo, M
• Parra, I
• Ocaña, M
• Aliseda, P
• Yarza, P
• Amírola, A
• [+] on PubMed
• Gavilán, M
• Balcones, D
• Marcos, O
• Llorca, D
• Sotelo, M
• Parra, I
• Ocaña, M
• Aliseda, P
• Yarza, P
• Amírola, A

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Sensors 2011, 11(10), 9628-9657; doi:10.3390/s111009628

Article

Adaptive Road Crack Detection System by Pavement Classification

Miguel Gavilán ¹ , David Balcones ¹ , Oscar Marcos ¹ , David F. Llorca ^1,* , Miguel A. Sotelo ¹ , Ignacio Parra ¹ , Manuel Ocaña ¹ , Pedro Aliseda ² , Pedro Yarza ²  and Alejandro Amírola ²

¹ Computer Engineering Department, Polytechnic School, University of Alcalá, Alcalá de Henares, Madrid 28871, Spain ² 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

(This article belongs to the Section Physical Sensors)

Download PDF Full-Text [3315 KB, uploaded 12 October 2011 11:33 CEST]

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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