Printed circuit boards (PCBs) are essential components that strongly influence the performance and reliability of modern electronic systems. However, minor and visually subtle manufacturing defects can degrade product quality and pose serious challenges for automated inspection systems. Existing deep learning–based methods often struggle to simultaneously achieve high detection accuracy, real-time processing speed, and compact model size. This study proposes an enhanced approach for real-time PCB defect detection using advanced object detection models. A dedicated dataset of bare PCBs was developed and carefully annotated with six defect categories: open circuits, missing holes, spurs, mouse bites, short circuits, and spurious copper. Multiple YOLO-based models were trained and evaluated on this dataset, among which the YOLOv9-small model demonstrated superior performance, achieving a mAP@50 of 99.2% and a mAP@50–95 of 63.1%. To further enhance computational efficiency, optimization techniques such as pruning and quantization were applied to the YOLOv9-small model. These optimizations reduced model size by 36.4% and increased processing speed by 24.7%, while maintaining high detection accuracy with a mAP@50 of 98.6%. Overall, the experimental results demonstrate that the optimized YOLOv9-based model provides a highly accurate, efficient, and practical solution for automated PCB defect detection in real-world electronics manufacturing environments.