Machine vision and background remover-based approach for PCB solder joints inspection

Abstract

A machine vision and background remover (BR)-based inspection method is proposed in this paper for solder defects inspection on a printed circuit board (PCB). The solder location is identified first using an unloaded PCB as the BR, thus reducing the amount of information needed for the following process. A set of candidate features is then calculated based on both binary and gray-level images. The defects are classified based on box plots of the feature value. The classification correctness reaches 97.3%. This methodology combines solder joints location identification and defects classification, making the inspection of PCB solder joints easier, without the requirement for special lighting or special instruments such as ultra-sonic and thermal sensors.