Convolutional Neural Networks for Classifying Electronic Components in Industrial Applications

Abstract

Electronic component classification often constitutes the uncomplicated task of classifying a single object on a simple background. It is because, in many applications, a technological process employs constant lighting conditions, a fixed camera position, and a designated set of classified components. To date, there has not been an adequate attempt to develop a method for object classification under the above conditions in industrial applications. Therefore, this work focuses on the classification problem of a particular technological process. The process classifies electronic components on an assembly line using a fixed-mounted camera. The research investigated all the essential steps required to build a classification system, such as image acquisition, database creation, and neural network development. The first part of the experiment was devoted to creating an image dataset utilising the proposed image acquisition system. Then, custom and pre-trained networks were developed and tested. The results indicated that the pre-trained network (ResNet50) attained the highest accuracy (99.03%), which was better than the 98.99% achieved in relevant research on classifying elementary components. The proposed solution can be adapted to similar technological processes, where a defined set of components is classified under comparable conditions.