Small Parts Classification with Flexible Machine Vision and a Hybrid Classifier
| Autor: | Keyur D. Joshi, Brian Surgenor |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
Artificial neural network
Machine vision business.industry Computer science 020206 networking & telecommunications Pattern recognition 02 engineering and technology Support vector machine Cable gland Software 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business Classifier (UML) |
| Zdroj: | M2VIP |
| DOI: | 10.1109/m2vip.2018.8600819 |
| Popis: | A Flexible Machine Vision (FMV) Inspection System has been developed that requires minimal retuning in hardware and software as applications are changed up. The flexibility of the system was evaluated by applying it to an inspection problem with three different types of small parts: plastic gears, plastic connectors and metallic coins, with minimal retuning when moving from one application to the others. The system was required to differentiate between 4 different known styles of each part plus one unknown style, for a total of 5 classes. In previous work, a hybrid Support Vector Machine (SVM) classifier was developed for the connector application. When applied to the coin application, the hybrid SVM could not achieve the target performance of 95% accuracy. A new hybrid that method that combines SVM and an Artificial Neural Network (ANN) or ANN-SVM classifier was subsequently developed to overcome this problem and the results are presented in this paper. The image library used in this study is available at http://my.me.queensu.ca/People/Surgenor/Laboratory/Database.html |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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