Classification of Solder Joint Using Feature Selection Based on Bayes and Support Vector Machine

In this paper, a feature selection and a two-stage classifier for solder joint inspection have been proposed. Using a three-color (red, green, and blue) hemispherical light-emitting diode array illumination and a charge-coupled device color digital camera, images of solder joints can be obtained. The color features, including the average gray level and the percentage of highlights and template-matching feature, are extracted. After feature selection, based on the algorithm of Bayes, each solder joint is classified by its qualification. If the solder joint fails in the qualification test, it is classified into one of the pre-defined types based on support vector machine. The choice of the second stage classifier is based on the performance evaluation of various classifiers. The proposed inspection system has been implemented and tested with various types of solder joints in surface-mounted devices. The experimental results showed that the proposed scheme is not only more efficient, but also increases the recognition rate, because it reduces the number of needed extracted features.