PCB defect detection based on an enhanced dab-deformable-DETR

In the electronics manufacturing sector, Printed Circuit Boards (PCBs) are crucial components whose quality is directly linked to the performance and reliability of end products. With the ongoing trend towards higher density and miniaturization in electronic devices, the demands on PCB quality are increasingly stringent, making traditional manual inspection methods insufficient for meeting the needs of high efficiency and accuracy. In response to these challenges, this study introduces an innovative PCB defect detection approach utilizing deep learning, specifically the advanced DAB Deformable DETR model. This model, an evolution beyond the conventional DETR framework, integrates multi-scale convolution and anchor weight optimization. These enhancements not only elevate the model's proficiency in identifying small and intricate defects but also streamline the training process, curtailing both the duration and resources necessitated. This method was rigorously tested and validated on a PCB defect dataset, and the experimental results indicate that it not only achieves a mean Average Precision (mAP) comparable to current advanced methods but also offers substantial advantages in terms of detection speed and model robustness.