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Updated: May 1, 2026

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Published on: July 3, 2021

MicroDETR for efficient and lightweight PCB defect detection.

Yanru Hu¹, Xin Zou², Xiaoqin Huang²

¹School of Physics and Electronic Information Engineering, Ningxia Normal University, Guyuan City, 756000, Ningxia Hui Autonomous Region, China. yixing_166@qq.com.

Scientific Reports

|April 29, 2026

Summary

This summary is machine-generated.

This study introduces MicroDETR, a lightweight real-time detection transformer for printed circuit board (PCB) defect detection. MicroDETR significantly improves accuracy and efficiency, especially for micro-scale targets, outperforming existing models.

Keywords:

Adaptive feature aggregation Defect detection Efficient Fusion network RT-DETR

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Last Updated: May 1, 2026

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Picometer-Precision Atomic Position Tracking through Electron Microscopy

Published on: July 3, 2021

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

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Published on: March 13, 2017

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Published on: April 13, 2016

Area of Science:

Computer Vision
Machine Learning
Industrial Automation

Background:

Printed circuit board (PCB) defect detection is challenging due to dense micro-scale targets and complex background textures.
Existing models often suffer from parameter redundancy and limited efficiency for real-time applications.

Purpose of the Study:

To propose MicroDETR, a novel lightweight real-time detection transformer for enhanced PCB defect detection.
To optimize the model for improved accuracy, efficiency, and robustness, particularly for micro-scale defects.

Main Methods:

Developed an EfficientBlock backbone using depthwise separable convolution and channel shuffle for feature enhancement and parameter compression.
Implemented an Enhanced Multi-Scale Feature Fusion Network (EMSFN) for effective integration of multi-level features.
Introduced a Multi-Level Feature Aggregation Module (MLFAM) to reduce complexity and improve micro-target detection.

Main Results:

MicroDETR achieved significant improvements in mAP@0.5 (5.1%) and mAP@0.5-0.95 (2.5%), with enhanced recall and precision.
The model demonstrated a 15.21% reduction in parameters, 12.11% decrease in computational complexity, and improved processing speed.
Exceptional performance on small-scale targets (mAP@0.5 > 77.5%) and superior noise resistance were observed.

Conclusions:

MicroDETR offers an optimal balance between accuracy and efficiency for industrial defect detection.
The model's real-world applicability is validated through successful deployment on edge devices.
Publicly available code and models facilitate further research and application in PCB defect detection.