%0 Journal Article
%T Adaptive Road Crack Detection System by Pavement Classification
%A Miguel Gavil¨¢n
%A David Balcones
%A Oscar Marcos
%A David F. Llorca
%A Miguel A. Sotelo
%A Ignacio Parra
%A Manuel Oca£¿a
%A Pedro Aliseda
%A Pedro Yarza
%A Alejandro Am¨ªrola
%J Sensors
%D 2011
%I MDPI AG
%R 10.3390/s111009628
%X 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.
%K road distress detection
%K road surface classification
%K linear features
%K multi-class SVM
%K local binary pattern
%K gray-level co-occurrence matrix
%U http://www.mdpi.com/1424-8220/11/10/9628