@article{sensors:gavilan2011,
author = "Gavil{\'a}n, Miguel and Balcones, David and Marcos, {\'O}scar and Llorca, David F. and Sotelo, Miguel {\'A}ngel and Parra, Ignacio and Oca{\~n}a, Manuel and Pedro Aliseda and Pedro Yarza and Alejandro Am{\'i}rola",
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.",
doi = "10.3390/s111009628",
issn = "1424-8220",
journal = "Sensors",
keywords = "road distress detection; road surface classification; linear features; multi-class SVM; local binary pattern; gray-level co-occurrence matrix",
number = "10",
pages = "9628-9657",
title = "{A}daptive {R}oad {C}rack {D}etection {S}ystem by {P}avement {C}lassification",
url = "http://www.mdpi.com/1424-8220/11/10/9628/",
volume = "11",
year = "2011",
}