Surface classification for road distress detection system enhancement

Authors:
M. Gavil$#225/n;D. Balcones;M. A. Sotelo;D. F. Llorca;O. Marcos;C. Fern$#225/ndez;I. Garcí/a;R. Quintero
Affiliations:
Computer Engineering Department, University of Alcal$#225/, Madrid, Spain;Computer Engineering Department, University of Alcal$#225/, Madrid, Spain;Computer Engineering Department, University of Alcal$#225/, Madrid, Spain;Computer Engineering Department, University of Alcal$#225/, Madrid, Spain;Computer Engineering Department, University of Alcal$#225/, Madrid, Spain;Computer Engineering Department, University of Alcal$#225/, Madrid, Spain;Computer Engineering Department, University of Alcal$#225/, Madrid, Spain;Computer Engineering Department, University of Alcal$#225/, Madrid, Spain
Venue:
EUROCAST'11 Proceedings of the 13th international conference on Computer Aided Systems Theory - Volume Part I
Year:
2011

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Abstract

This paper presents a vision-based road surface classification in the context of infrastructure inspection and maintenance, proposed as stage for improving the performance of a distress detection system. High resolution road images are processed to distinguish among surfaces arranged according to the different materials used to build roads and their grade of granulation and striation. A multi-class Support Vector Machine (SVM) classification system using mainly Local Binary Pattern (LBP), Gray-Level Co-occurrence Matrix (GLCM) and Maximally Stable Extremal Regions (MSER) derived features is described. The different texture analysis methods are compared based on accuracy and computational load. Experiments with real application images show a significant improvement on the the distress detection system performance by combining several feature extraction methods.