Printed Circuit Board Defect Detection Using Convolutional Neural Networks

Abstract

The increasing complexity and miniaturization of modern electronic devices necessitate highly reliable and defect-free Printed Circuit Boards (PCBs). Effective defect detection in PCBs is crucial to maintaining the quality and reliability of these devices. However, current PCB defect detection datasets exhibit significant limitations that hinder the development of robust and accurate models. Existing datasets are limited in scope, do not accurately mimic real-world defects, and fail to represent the diversity and complexity of industrial PCBs. For instance, PKU dataset is restricted to only a small amount of PCB boards and include defects introduced post-manufacturing using Photo Editing Applications, which do not closely mirror real-world manufacturing imperfections. Additionally, these datasets label each PCB board with only one fault class, despite real-life scenarios where multiple faults can occur simultaneously. These compounded limitations make the datasets less suitable for generalizing to the diverse and complex situations encountered in real-world PCB inspections. To address these challenges, this research aims to develop a comprehensive and realistic dataset created through chemical etching procedures, reflecting the true nature of manufacturing imperfections. Additionally, we trained advanced Convolutional Neural Network (CNN) models, including YOLOv8, HRNet, Cascade R-CNN, ATSS, RetinaNet, and Faster R-CNN, to detect six common PCB defects: missing pad, open circuit, short circuit, spur, spurious copper, and mouse bite. Our findings indicate that YOLOv8 demonstrated superior accuracy and speed, achieving a mean Average Precision (mAP) of 0.888 at 50% intersection over union (mAP50) and a detection speed of 16.3 frames per second (FPS). HRNet, while achieving the highest mAP50 of 0.905, was less suitable for real-time applications due to its lower frame rate of 9.2 FPS. ATSS and Cascade R-CNN offered balanced performance with mAP50s of 0.88 and detection speeds of 14.51 FPS. In contrast, RetinaNet and Faster R-CNN were less effective due to lower accuracy and slower processing times. This study underscores the inadequacies of existing PCB datasets and the necessity for more accurate and efficient defect detection models.