An Efficient Vision Transformer Model for PCB Component Classification

Printed circuit board (PCB) assemblies in everyday electronic devices are mass-produced. As a result of this production volume, a fast way of visual inspection is necessary. An integral part of visual inspection systems is PCB component classification. In this paper, we have explored use of the Vision Transformer (ViT), which is a recent state-of-the-art image classification approach, for PCB component classification. We have employed several ViT models that are available in the literature and also proposed a new compact, efficient, and high performing ViT model, named as ViT-Mini. We have conducted extensive experiments on the FICS-PCB dataset in order to comparatively evaluate the ViT models' performance. The highest achieved accuracy is 99.46% for capacitor and resistor classification and 96.52% for classification of capacitor, resistor, inductor, transistor, diode, and IC. The proposed compact model's performance is comparable with the ones obtained with larger models, which indicates its suitability for real-time applications.