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Comprehensive Characterization of Extended Defects in Semiconductor Materials by a Scanning Electron Microscope

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SCP-DETR: A efficient small-object-enhanced feature pyramid approach for PCB defect detection.

Yuanyuan Wang¹, Tongtong Yin¹, Xiuchuan Chen¹

¹College of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, China.

|August 29, 2025

Summary

This summary is machine-generated.

This study introduces SCP-DETR, an improved method for detecting small defects on printed circuit boards (PCBs). SCP-DETR enhances accuracy by optimizing feature fusion and using specialized convolutions, significantly improving defect detection performance.

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Area of Science:

Computer Vision
Artificial Intelligence
Manufacturing Technology

Background:

Printed circuit board (PCB) defects impact electronic component quality.
Existing detection methods struggle with background interference and small defect sizes.

Purpose of the Study:

To develop an improved PCB defect detection method.
To enhance accuracy for small and subtle defects.
To reduce computational overhead in feature fusion.

Main Methods:

Proposed SCP-DETR based on RT-DETR.
Incorporated S2 feature layer with Space-to-Depth Convolution (SPDConv) for efficient small target detection.
Utilized a CO-Fusion module with CSP Omni-Kernel Module (CSPOKM) for multi-scale feature learning.
Replaced downsampling with Pinwheel-shaped Convolution (PSConv) for better small target feature representation.

Main Results:

Achieved 97% mAP@0.5, outperforming RT-DETR-r18 by 3%.
Reached 53.4% mAP@0.5:0.95, an improvement of 2.2%.
Improved mAP@0.5 by 5.6% compared to YOLOv11m.

Conclusions:

SCP-DETR demonstrates superior performance in PCB defect detection.
The method offers significant potential for industrial production quality control.
The enhanced feature fusion and specialized convolutions effectively address challenges in detecting small defects.