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.

Deep learning models have become central to automated Printed Circuit Board (PCB) defect detection. However, recent work has raised concerns about how reliably these models express confidence in their predictions, particularly when deployed in safety-critical inspection systems. This study conducts an empirical investigation of epistemic uncertainty across representative architectures used in PCB inspection: the two-stage Faster R-CNN detector, the one-stage YOLOv8 detector, and their corresponding classification counterparts, ResNet-50 and YOLOv8-Cls. Monte Carlo Dropout (MCD) was applied during inference to compute predictive entropy, mutual information, softmax variance, and bounding-box variability across multiple stochastic forward passes on both multiclass and binary inspection datasets. On the multiclass SolDef_AI dataset, Faster R-CNN achieved substantially stronger detection performance (mAP = 0.7607, F1 = 0.9304) and lower predictive entropy, with more stable localisation. In contrast, YOLOv8 produced markedly weaker performance (mAP = 0.2369, F1 = 0.3130) alongside higher entropy and greater bounding-box variability. On the binary Jiafuwen datasets, the YOLOv8-Cls model achieved higher overall performance (F1 = 0.6493) compared with the ResNet-50 classifier (F1 = 0.4904), reflecting its strength in simpler binary inspection tasks. Across uncertainty metrics, predictive entropy and mutual information were more sensitive to dataset size, showing higher and more variable values in the smaller multiclass dataset, whereas softmax variance and bounding-box variability appeared more architecture-dependent. These findings demonstrate that architectural choice, dataset structure, and task formulation jointly influence both performance and uncertainty behaviour. By integrating conventional metrics with uncertainty estimates, this study provides a transparent benchmark for assessing model confidence in automated optical inspection of PCBs.