Next Article in Journal
Numerical Optimization of a Microfluidic Assisted Microarray for the Detection of Biochemical Interactions
Previous Article in Journal
Cell Docking, Movement and Cell-Cell Interactions of Heterogeneous Cell Suspensions in a Cell Manipulation Microdevice
Article Menu

Export Article

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

Adaptive Road Crack Detection System by Pavement Classification

Computer Engineering Department, Polytechnic School, University of Alcalá, Alcalá de Henares, Madrid 28871, Spain
Infrastructure Management Division, ACCIONA Engineering, c\Marcelina 3, Madrid 28029, Spain
Author to whom correspondence should be addressed.
Received: 27 August 2011 / Revised: 27 September 2011 / Accepted: 9 October 2011 / Published: 12 October 2011
(This article belongs to the Section Physical Sensors)


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 road distress detection; road surface classification; linear features; multi-class SVM; local binary pattern; gray-level co-occurrence matrix
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Gavilán, M.; Balcones, D.; Marcos, O.; Llorca, D.F.; Sotelo, M.A.; Parra, I.; Ocaña, M.; Aliseda, P.; Yarza, P.; Amírola, A. Adaptive Road Crack Detection System by Pavement Classification. Sensors 2011, 11, 9628-9657.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top