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Automatic inspection of metallic surface defects using genetic algorithms

Zheng, Hong, Kong, Lingxue and Nahavandi, Saeid 2002, Automatic inspection of metallic surface defects using genetic algorithms, Journal of materials processing technology, vol. 125, no. 126, pp. 427-433, doi: 10.1016/S0924-0136(02)00294-7.

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Title Automatic inspection of metallic surface defects using genetic algorithms
Author(s) Zheng, Hong
Kong, LingxueORCID iD for Kong, Lingxue orcid.org/0000-0001-6219-3897
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Journal name Journal of materials processing technology
Volume number 125
Issue number 126
Start page 427
End page 433
Publisher Elsevier Science B.V.
Place of publication Amsterdam, The Netherlands
Publication date 2002-09-09
ISSN 0924-0136
1873-4774
Keyword(s) automatic visual inspection
machine vision
morphology
genetic algorithms
Summary 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.


Language eng
DOI 10.1016/S0924-0136(02)00294-7
Field of Research 080109 Pattern Recognition and Data Mining
HERDC Research category C1 Refereed article in a scholarly journal
Copyright notice ©2002, Elsevier Science B.V.
Persistent URL http://hdl.handle.net/10536/DRO/DU:30001823

Document type: Journal Article
Collection: School of Engineering and Technology
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