Transformers or Not: A Comparative Study on Defect Inspection in Printed Circuit Boards

A Printed Circuit Board (PCB) is a fundamental component in the manufacturing process of electronic devices. When talking about the process of fabrication of these devices, the quality control stage represents, in general, the biggest cost in production. This stage is usually realized by humans, which are prone to fail, as well as being slower and less consistent when compared to computers. This study presents the utlization of six distincts Artificial Intelligence archtiectures trained for PCB defect detection. The results of the experiments highlighted key differences in training time, accuracy, and computational complexity across various models. YOGA-s achieved a good balance with the second-best mAP 50 accuracy (83.2%) and a moderate training time of 50 minutes. YOLOv11n, despite its shorter training time (40 minutes), outperformed YOGA-s in mAP 50-95 (42.7%). DETR demonstrated the highest accuracy (92.4% mAP 50), but at the cost of significantly longer training times and higher complexity. LW-DETR, while efficient, showed limited performance improvements as its scale increased. YOLOv11n emerged as the most cost-effective option for PCB inspection tasks.