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. 2025 Aug 8;15(1):29063.
doi: 10.1038/s41598-025-14957-2.

An improved EAE-DETR model for defect detection of server motherboard

Jian Chi  1 , Keming Zhang #  1 , Hongpu Zhang #  1 , Guowang Niu #  1 , Zhihao Zheng #  2
Affiliations

An improved EAE-DETR model for defect detection of server motherboard

Jian Chi et al. Sci Rep. .

Erratum in

Abstract

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.

Keywords: Defect detection; Dynamic convolution; Feature fusion; Server motherboard; Sparse attention.

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Conflict of interest statement

Declarations. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
The overall framework of the RT-DETR model.
Fig. 2
Fig. 2
The overall framework of the EAE-DETR model.
Fig. 3
Fig. 3
EfficientVIM-CGLU module diagram.
Fig. 4
Fig. 4
Diagram of the module structure of AIFI-ASSA.
Fig. 5
Fig. 5
The EUCB-SC module.
Fig. 6
Fig. 6
Eight different kinds of defects for PCBA-DET.
Fig. 7
Fig. 7
Signature class information.
Fig. 8
Fig. 8
PCBA-DET dataset display graph.
Fig. 9
Fig. 9
PKU-Market-PCB dataset display graph.
Fig. 10
Fig. 10
Comparison of detection results between RT-DETR-R18 and EAE-DETR.
Fig. 11
Fig. 11
Comparison of correct detection results between RT-DETR-R18 and EAE-DETR.
Fig. 12
Fig. 12
Comparison of error detection results between RT-DETR-R18 and EAE-DETR.
See this image and copyright information in PMC

References

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    1. Zhou, Y. et al. Review of vision-based defect detection research and its perspectives for printed circuit board. J. Manuf. Syst.70, 557–578 (2023).
    1. Jiang, W. et al. PCB defects target detection combining multi-scale and attention mechanism. Eng. Appl. Artif. Intell.123, 106359 (2023).
    1. Chen, M. et al. A comprehensive review of deep learning-based PCB defect detection. IEEE Access.11, 139017–139038 (2023).

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