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The Visual Colorimetric Detection of Multi-nucleotide Polymorphisms on a Pneumatic Droplet Manipulation Platform

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Published on: September 27, 2016

A lightweight detection algorithm of PCB surface defects based on YOLO.

Shiwei Yu¹, Feng Pan¹, Xiaoqiang Zhang¹

¹CGN Digital Technology Co., Ltd., Shanghai, China.

|April 18, 2025

Summary

This summary is machine-generated.

This study introduces a lightweight YOLO-based algorithm for PCB defect detection, significantly reducing computational load and model size while improving accuracy. The enhanced model is ideal for deployment on resource-constrained platforms.

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Area of Science:

Electronics
Computer Vision
Artificial Intelligence

Background:

Printed Circuit Board (PCB) defect detection faces challenges with low accuracy and high computational demands.
Existing algorithms often struggle with cluttered backgrounds and subtle defect variations.

Purpose of the Study:

To develop a lightweight and accurate PCB defect detection algorithm.
To optimize the model for efficient deployment on low-arithmetic platforms.

Main Methods:

Utilized GhostNet in the Backbone for model lightness.
Improved the neck network with depthwise separable convolution to reduce parameters.
Integrated Swin-Transformer with C3 module (C3STR) to handle complex image backgrounds and defect types.
Replaced PANet with Bidirectional Feature Pyramid Network (BIFPN) for enhanced multi-scale feature fusion.

Main Results:

Achieved a 47.2% reduction in parameter count.
Reduced GFLOPs by 48.5% and model weight by 42.4%.
Observed a 2.4% increase in mean Average Precision (mAP) with only a 2.0% decrease in Frames Per Second (FPS).

Conclusions:

The proposed lightweight YOLO-based algorithm offers a superior balance between accuracy and computational efficiency for PCB defect detection.
The model's reduced complexity makes it highly suitable for real-time applications on embedded systems and low-arithmetic platforms.