Defect Detection and Classification of PCB Based on RT-DETR

Printed circuit board PCB defect detection is crucial for ensuring PCB quality. However, small defect sizes on PCBs and the substantial parameter and computational requirements of deep learning models pose challenges in detection accuracy and deployment on resource-constrained devices. To address these issues, we introduce an optimized PCB defect detection method based on the RT-DETR model. Firstly, we propose the ContextAdown-D-LKAEfficientFormerV2 lightweight network to replace the ResNet18 backbone, reducing parameters and computations while enhancing small defect feature extraction. Secondly, we present the Bi-Slim-Neck lightweight structure to replace the CCFM component in the original model, achieving lightweight design and improved feature fusion capabilities to leverage effective features fully. Lastly, we propose the InnerShapeIoU loss function to replace the GIoU loss function, accounting for the influence of PCB defect bounding box shapes and scales on regression, and generating auxiliary bounding boxes suitable for PCB defect detection tasks and detectors. This enhances model generalization and detection accuracy. Experi mental results show that the improved model achieves detection accuracies of 97 mAP50 and 55 mAP50:95 , with a 23.8 reduction in parameters and a 44.9 decrease in computations compared to the original model. Thisindicates that the improved method significantly boosts parameter efficiency and reduces computational complexity while maintaining high detection accuracy.