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2012 | OriginalPaper | Chapter
Surface Classification for Road Distress Detection System Enhancement
Authors : M. Gavilán, D. Balcones, M. A. Sotelo, D. F. Llorca, O. Marcos, C. Fernández, I. García, R. Quintero
Published in: Computer Aided Systems Theory – EUROCAST 2011
Publisher: Springer Berlin Heidelberg
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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.