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Proceedings Paper

Real-time flaw detection on complex part: classification with SVM and Hyperrectangle-based method
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Paper Abstract

This paper presents a classification work performed on industrial parts using artificial vision, SVM and a combination of classifiers. Prior to this study, defect detection was performed by human inspectors. Unfortunately, the time involved in the inspection procedure was far too long and the misclassification rate too high. Our project consists in detecting anomalies under manufacturer production and cost constraints as well as in classifying the anomalies among twenty listed categories. Manufacturer’s specifications require a minimum of ten inspections per second without a decrease in the quality of the produced parts. This problem can be solved with a classification system relying on a real-time machine vision. To fulfill both real time and quality constraints, two classification algorithms and a tree based classification method were compared. The first one, Hyperrectangle based, has proved to be well adapted for real-time constraints. The second one, based on Support Vector Machine (SVM), is more robust, more complex and more greedy regarding the computing time. Finally, naïve rules were defined, to build a decision tree and to combine it with one of the previous classification algorithms.

Paper Details

Date Published: 3 May 2004
PDF: 8 pages
Proc. SPIE 5303, Machine Vision Applications in Industrial Inspection XII, (3 May 2004); doi: 10.1117/12.530838
Show Author Affiliations
Sebastien Bouillant, Univ. de Bourgogne (France)
Johel Miteran, Univ. de Bourgogne (France)
Michel Paindavoine, Univ. de Bourgogne (France)
Fabrice Meriaudeau, Univ. de Bourgogne (France)

Published in SPIE Proceedings Vol. 5303:
Machine Vision Applications in Industrial Inspection XII
Jeffery R. Price; Fabrice Meriaudeau, Editor(s)

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