SDD-RT-DETR: A Lightweight and Efficient Printed Circuit Board Surface Defect Detection Method Based on an Improved RT-DETR Toward Sustainable Manufacturing

In electronic manufacturing, efficient detection of printed circuit board (PCB) surface defects is essential for reducing rework rates and minimizing material waste, thereby supporting sustainable manufacturing. To address the challenge that existing methods struggle to balance detection accuracy and real-time performance in complex industrial environments, this paper proposes a lightweight and high-performance PCB surface defect detection model, termed SDD-RT-DETR. Built upon Real-Time Detection Transformer (RT-DETR), the proposed model introduces a Faster-Block backbone to improve feature extraction efficiency, replaces the original feature fusion module with HS-FPN to enhance multi-scale representation, and employs the Wise-Focaler-MPDIoU loss to optimize bounding box regression. Experiments conducted on an expanded PCB defect dataset containing 3403 images show that SDD-RT-DETR achieves improvements of 2.3% in mAP and 3.6% in inference speed over the baseline, while reducing parameters by 5.04 M and FLOPs by 12.7 G. These results demonstrate that the proposed method effectively balances accuracy, efficiency, and computational cost, offering a practical solution for low-energy and sustainable intelligent electronic manufacturing systems.