Automated Defect Identification System in Printed Circuit Boards Using Region-Based Convolutional Neural Networks

Printed Circuit Board (PCB) manufacturing demands accurate defect detection to ensure quality. Traditional methods, such as manual inspection or basic automated object inspection systems, are often time-consuming and inefficient. This work presents a deep learning architecture using Faster R-CNN with a ResNet-50 backbone to automatically detect and classify PCB defects, including Missing Holes (MHs), Open Circuits (OCs), Mouse Bites (MBs), Shorts, Spurs, and Spurious Copper (SC). The designed architecture involves data acquisition, annotation, and augmentation to enhance model robustness. In this study, the CNN-Resnet 50 backbone achieved a precision–recall value of 87%, denoting strong and well-balanced performance in PCB fault detection and classification. The model effectively identified defective instances, reducing false negatives, which is critical for ensuring quality assurance in PCB manufacturing. Performance evaluation metrics indicated a mean average precision (mAP) of 88% and an Intersection over Union (IoU) score of 72%, signifying high prediction accuracy across various defect classes. The developed model enhances efficiency and accuracy in quality control processes, making it a promising solution for automated PCB inspection.