COMPARISON OF TRADITIONAL AND NEURAL CLASSIFIERS FOR PAVEMENT-CRACK DETECTION

被引：71

作者：

KASEKO, MS

LO, ZP

RITCHIE, SG

机构：

[1] Dept. of Civ. and Envir. Engrg., Univ. of Nevada, Las Vegas, NV

[2] Printrak International, Inc., Anaheim, CA

[3] Inst. of Transp Studies, Dept. of Civ. and Envir. Engrg., University of California, Irvine, CA

来源：

JOURNAL OF TRANSPORTATION ENGINEERING-ASCE | 1994年 / 120卷 / 04期

关键词：

D O I：

10.1061/(ASCE)0733-947X(1994)120:4(552)

中图分类号：

TU [建筑科学];

学科分类号：

0813 ;

摘要：

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 arc presented and compared, and the relative merits of these techniques arc 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.

引用

页码：552 / 569

页数：18

共 23 条

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