Filter-based feature selection for rail defect detection

Authors:
C. Mandriota;M. Nitti;N. Ancona;E. Stella;A. Distante
Affiliations:
Istituto di Studi sui Sistemi Intelligenti per 1'Automazione, C.N.R., Via Amendola, 166/5, 70126 Bari, Italy;Istituto di Studi sui Sistemi Intelligenti per 1'Automazione, C.N.R., Via Amendola, 166/5, 70126 Bari, Italy;Istituto di Studi sui Sistemi Intelligenti per 1'Automazione, C.N.R., Via Amendola, 166/5, 70126 Bari, Italy;Istituto di Studi sui Sistemi Intelligenti per 1'Automazione, C.N.R., Via Amendola, 166/5, 70126 Bari, Italy;Istituto di Studi sui Sistemi Intelligenti per 1'Automazione, C.N.R., Via Amendola, 166/5, 70126 Bari, Italy
Venue:
Machine Vision and Applications
Year:
2004

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Abstract

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 detects: corrugation.