A flexible machine vision system for small part inspection based on a hybrid SVM/ANN approach

Machine vision inspection systems are often used for part classification applications to confirm that correct parts are available in manufacturing or assembly operations. Support vector machines (SVMs) and artificial neural networks (ANNs) are popular choices for classifiers. These supervised classifiers perform well when developed for specific applications and trained with known class images. Their drawback is that they cannot be easily applied to different applications without extensive retuning. Moreover, for the same application, they do not perform well if there are unknown class images. This paper proposes a novel solution to the above limitations of SVMs and ANNs, with the development of a hybrid approach that combines supervised and semi-supervised layers. To illustrate its performance, the system is applied to three different small part identification and sorting applications: (1) solid plastic gears, (2) clear plastic wire connectors and (3) metallic Indian coins. The ability of the system to work with different applications with minimal tuning and user inputs illustrates its flexibility. The robustness of the system is demonstrated by its ability to reject unknown class images. Four hybrid classification methods were developed and tested: (1) SSVM–USVM, (2) USVM–SSVM, (3) USVM–SANN and (4) SANN–USVM. It was found that SANN–USVM gave the best results with an accuracy of over 95% for all three applications. A software package known as FlexMVS for flexible machine vision system was written to illustrate the hybrid approach that enabled easy execution of the image conditioning, feature extraction and classification steps. The image library and database used in this study is available at http://my.me.queensu.ca/People/Surgenor/Laboratory/Database.html.