Citation

Gavilán M, Balcones D, Marcos O, Llorca DF, Sotelo MA, Parra I, Ocaña M, Aliseda P, Yarza P, Amírola A. Sensors (Basel) 2011; 11(10): 9628-9657.

Affiliation

Computer Engineering Department, Polytechnic School, University of Alcalá, Alcalá de Henares, Madrid 28871, Spain; E-Mails: miguel.gavilan@aut.uah.es (M.G.); david.balcones@aut.uah.es (D.B.); oscar.marcos@aut.uah.es (O.M.); sotelo@aut.uah.es (M.A.S.); ignacio@aut.uah.es (I.P.); mocana@depeca.uah.es (M.O.).

Copyright

(Copyright © 2011, MDPI: Multidisciplinary Digital Publishing Institute)

DOI

10.3390/s111009628

PMID

22163717

PMCID

PMC3231249

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.

Language: en