Filter-based feature selection for rail defect detection

被引：2
|
作者：
C. Mandriota
M. Nitti
N. Ancona
E. Stella
A. Distante
机构：
[1] Istituto di Studi sui Sistemi Intelligenti per l’Automazione,
[2] C.N.R.,undefined
来源：
Machine Vision and Applications | 2004年 / 15卷
关键词：
Rail detection; Filter bank; Texture feature; K-nearest neighbor classifier;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
Over the last few years research has been oriented toward developing a machine vision system for locating and identifying, automatically, defects on rails. Rail defects exhibit different properties and are divided into various categories related to the type and position of flaws on the rail. Several kinds of interrelated factors cause rail defects such as type of rail, construction conditions, and speed and/or frequency of trains using the rail. The aim of this paper is to present an experimental comparison among three filtering approaches, based on texture analysis of rail surfaces, to detect the presence/absence of a particular class of surface defects: corrugation.
引用
收藏
页码：179 / 185
页数：6
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