LPViT: A Transformer Based Model for PCB Image Classification and Defect Detection

Abstract

PCB (printed circuit board) is an extremely important component of all electronic products, which has greatly facilitated human life. Meanwhile, tons of PCBs in the waste streams become a waste of resources, which puts the recycling and reuse of PCBs in urgent need. In the manufacturing and recycling of electronic products, the classification of PCBs, recognition of sub-components, and defect detection have been the key technology. Traditional manual detection and classification are subjective and rely on individuals' experience. With the development of artificial intelligence, lots of research efforts have been dedicated to the automated detection and recognition of PCBs. In this paper, we propose a transformer-based model, LPViT, for defect detection and classification of PCBs. We conduct the defect detection task on the dataset DeepPCB, which consists of six different types of PCB defects. Defect detection benefits both manufacturing and recycling of PCBs. Among many electronic products, a group of affordable, general-purpose, and small-size PCBs is very popular, which are referred to as micro-PCBs. The classification and recognition of those PCBs will greatly facilitate the recycling and reuse process. We conduct the classification task on a dataset called micro-PCB, which includes 12 types of popular, general-purpose, affordable, and small PCBs. Through comparative experiments, our system demonstrates its advantage in both classification and defect detection tasks.