This study addresses the challenges of missed and false detections in server motherboard defect identification, which arise from factors such as small target size, positional rotation deviations, and uneven scale distribution. To tackle these issues, we propose an enhanced detection model, EAE-DETR, which is based on an improved version of RT-DETR. Initially, we developed the CSP-EfficientVIM-CGLU module to enhance feature extraction capabilities while simultaneously reducing the model's parameter count through the implementation of dynamic gated convolution and global context modeling. Subsequently, we introduced the AIFI-ASSA module, designed to mitigate background noise interference and improve sensitivity to minor defects by employing an adaptive sparse self-attention mechanism. Lastly, we constructed the EUCB-SC upsampling module, which integrates depth convolution and channel shuffling strategies to enhance feature reconstruction efficiency. Experimental results on the PCBA-DET dataset indicate that EAE-DETR achieves a mean Average Precision (mAP) of 78.5% at IoU = 0.5 and 32.6% across IoU thresholds of 0.5 to 0.95, surpassing the baseline RT-DETR-R18 by 3.6% and 6.5%, respectively. Furthermore, the model demonstrates a reduction in parameter count by 21.7% and a decrease in computational load by 12.0%. On the PKU-Market-PCB dataset, the mAP50 reached 96.1%, and the mAP50:95 reached 65.1%.This model effectively facilitates high-precision and high-efficiency defect detection for server motherboards in complex industrial environments, thereby offering a robust solution for the intelligent manufacturing sector.
Chi et al. (Fri,) studied this question.