Adaptive Road Crack Detection System by Pavement Classification

Sensors

Miguel Gavilán¹  David Balcones¹  Oscar Marcos¹  David F. Llorca¹  Miguel A. Sotelo¹  Ignacio Parra¹  Manuel Oca༚¹  Pedro Aliseda²  Pedro Yarza² 

[1] Computer Engineering Department, Polytechnic School, University of Alcalá, Alcalá de Henares, Madrid 28871, Spain; E-Mails:;Infrastructure Management Division, ACCIONA Engineering, c\Marcelina 3, Madrid 28029, Spain; E-Mails:

关键词: road distress detection;    road surface classification;    linear features;    multi-class SVM;    local binary pattern;    gray-level co-occurrence matrix;   

DOI  :  10.3390/s111009628

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2011 by the authors; licensee MDPI, Basel, Switzerland.