6533b7d8fe1ef96bd126a196

RESEARCH PRODUCT

Real-time flaw detection on a complex object: comparison of results using classification with a support vector machine, boosting, and hyperrectangle-based method

subject

description

We present a classification work performed on industrial parts using artificial vision, a support vector machine (SVM), boost- ing, and a combination of classifiers. The object to be controlled is a coated heater used in television sets. Our project consists of detect- ing anomalies under manufacturer production, as well as in classi- fying the anomalies among 20 listed categories. Manufacturer speci- fications require a minimum of ten inspections per second without a decrease in the quality of the produced parts. This problem is ad- dressed by using a classification system relying on real-time ma- chine vision. To fulfill both real-time and quality constraints, three classification algorithms and a tree-based classification method are compared. The first one, hyperrectangle based, proves to be well adapted for real-time constraints. The second one is based on the Adaboost algorithm, and the third one, based on SVM, has a better power of generalization. Finally, a decision tree allowing improving classification performances is presented. © 2006 SPIE and