Road surface crack identification by using different classifiers on digital images

  • Authors:

  • Heydar Toossian Shandiz;Hosein Ghasemzadeh Tehrani;Hadi Hadizadeh

  • Affiliations:

  • Shahrood University of Technology, Electrical, Civil Engineering Faculty, Shahrood, Iran;Shahrood University of Technology, Electrical, Civil Engineering Faculty, Shahrood, Iran;Shahrood University of Technology, Electrical, Civil Engineering Faculty, Shahrood, Iran

  • Venue:
  • SSIP'05 Proceedings of the 5th WSEAS international conference on Signal, speech and image processing
  • Year:
  • 2005

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Abstract

In this paper different classifier are used to identifying different type of cracks on road surface. As our experience shows Region Growing Classifier (RGC) method can be used to divide all surface road images in two main groups. First group covers alligator and block cracks. Longitudinal, transverse cracks and other kind of distress are put in second group. In first group, wavelet Statistic Feature Classifier (WSFC), vertical and horizontal histogram and proximity are used for classification. They help to judge about the kind of crack based on digital image from road surface. Histogram, RGC and proximity are classifiers which are used in second group. Multi layer Perceptron neural network is used to judge about the cracks.