Title

Comparison of Traditional and Neural Classifiers for Pavement-Crack Detection

Document Type

Article

Publication Date

7-1-1994

Publication Title

Journal of Transportation Engineering

Volume

120

Issue

4

First page number:

552

Last page number:

569

Abstract

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.

Keywords

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

Disciplines

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

Language

English

Permissions

Use Find in Your Library, contact the author, or interlibrary loan to garner a copy of the item. Publisher policy does not allow archiving the final published version. If a post-print (author's peer-reviewed manuscript) is allowed and available, or publisher policy changes, the item will be deposited.

Identifier

DOI: http://dx.doi.org/10.1061/(ASCE)0733-947X(1994)120:4(552)

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