Comparison of Traditional and Neural Classifiers for Pavement-Crack Detection

Document Type



This paper presents a comparative evaluation of traditional and neural‐network classifiers to detect cracks in video images of asphalt‐concrete pavement surfaces. The traditional classifiers used are the Bayes classifier and the k‐nearest neighbor (k‐NN) decision rule. The neural classifiers are the multilayer feed‐forward (MLF) neural‐network classifier and a two‐stage piecewise linear neural‐network classifier. Included in the paper is a theoretical background of the classifiers, their implementation procedures, and a case study to evaluate their performance in detection and classification of crack segements in pavement images. The results are presented and compared, and the relative merits of these techniques are discussed. The research reported in this paper is part of an ongoing research project, the objective of which is to develop a neural‐network‐based methodology for the processing of video images for automated detection, classification, and quantification of cracking on pavement surfaces.


Asphalt; Automation; Concrete; Concrete—Cracking; Cracking; Data collection; Imaging techniques; Neural networks; Pavement condition; Pavements; Pavements—Cracking; Pavements; Asphalt; Pavements; Asphalt—Cracking; Pavements; Concrete; Pavements; Concrete--Cracking


Civil and Environmental Engineering | Construction Engineering and Management | Engineering | Environmental Sciences


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