Automated Optical Inspection of Printed Circuit Boards Using Machine Vision

Abstract

A printed circuit board (PCB) is an essential part of every electronic equipment. Because of technical innovation and the rise of the consumer market, the requirement to make PCBs in large quantities has become a need throughout the years. Small flaws in a PCB signal path can have a negative impact on an electrical device's operation. Initially, the PCB inspection quality control procedure was done manually, which was not only time-consuming but also prone to human errors. Later research focused on traditional machine vision techniques such as template matching, image subtraction, and morphological operations. These approaches were efficient and timesaving, but they were susceptible to noise, orientation, and size. Faults in complicated patterns were difficult to identify, and only a limited number of defects could be recognized using traditional machine vision. To address these concerns, we propose a PCB fault detection system based on the most recent version of you-only-look-once (YOLOv5). YOLOv5 weights pre-trained on the Common Objects in Context (COCO) dataset were used to execute transfer learning on the bare PCB defect dataset, which consisted of 693 pictures with 6 different types of faults. Furthermore, data augmentation was performed to improve the model's training performance and robustness. The medium YOLOv5 model produced promising test results with a mean average precision (mAP) of 96.92 percent, precision of 97.18 percent, and recall of 96.86 percent.