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Automatic inspection of metallic surface defects using genetic algorithms

journal contribution
posted on 2002-09-09, 00:00 authored by H Zheng, Lingxue KongLingxue Kong, Saeid Nahavandi
This paper is concerned with the problem of automatic inspection of metallic surface using machine vision. An experimental system has been developed to take images of external metallic surfaces and an intelligent approach based on morphology and genetic algorithms is proposed to detect structural defects on bumpy metallic surfaces. The approach employs genetic algorithms to automatically learn morphology processing parameters such as structuring elements and defect segmentation threshold. This paper describes the detailed procedures which include encoding scheme, genetic operation and evaluation function.

The proposed method has been implemented and tested on a number of metallic surfaces. The results suggest that the method can provide an accurate identification to the defects and can be developed into a viable commercial visual inspection system.


History

Journal

Journal of materials processing technology

Volume

125

Issue

126

Pagination

427 - 433

Publisher

Elsevier Science B.V.

Location

Amsterdam, The Netherlands

ISSN

0924-0136

eISSN

1873-4774

Language

eng

Publication classification

C1 Refereed article in a scholarly journal

Copyright notice

2002, Elsevier Science B.V.