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Related Experiment Video

Updated: May 28, 2026

High-precision Electromagnetic Flowmeter with Empty Pipe Detection via Complex Programmable Logic Device-based Waveform Recognition

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EP-YOLO: A Printed Circuit Board Defect Detection Network Integrating Coordinate Attention and Multi-Level Gradient

Xiangsuo Fan^1,2,3,4, Can Yang¹, Ling Yu^1,4

¹School of Automation, Guangxi University of Science and Technology, Liuzhou 545006, China.

Sensors (Basel, Switzerland)

|May 27, 2026

Summary

This summary is machine-generated.

A new printed circuit board (PCB) defect detection network, EP-YOLO, enhances accuracy for small defects in complex backgrounds. This advanced model improves quality control in electronic manufacturing.

Keywords:

PCB YOLOv8 deep learning defect detection small goals

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Last Updated: May 28, 2026

High-precision Electromagnetic Flowmeter with Empty Pipe Detection via Complex Programmable Logic Device-based Waveform Recognition

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Published on: June 27, 2025

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

Published on: March 13, 2017

Area of Science:

Computer Vision
Artificial Intelligence
Electrical Engineering

Background:

Printed circuit board (PCB) defect detection is crucial for electronic product quality control.
Challenges include small defect targets, complex backgrounds, and numerous integrated components.

Purpose of the Study:

To develop an improved PCB defect detection network, EP-YOLO, integrating coordinate attention and gradient flow optimization.
Enhance detection accuracy for small and complex defects in PCBs.

Main Methods:

Reconstructed the detection head for improved small target detail capture.
Introduced a Shallow Context Feature Extraction (SCFE) network to fuse shallow and multi-scale features.
Designed a multi-level feature gradient flow optimization module (CCA) with coordinate attention and CSPFOK to suppress noise and enhance feature extraction.
Utilized SCYLLA-IoU (SIoU) for model training optimization.

Main Results:

EP-YOLO achieved 97.5% mAP50 on the PKU-Market-PCB dataset and 98.5% mAP50 on the DeepPCB dataset.
Demonstrated a parameter reduction of approximately 12.55% compared to other networks.
Outperformed popular object detection networks in PCB defect detection tasks.

Conclusions:

EP-YOLO offers a powerful and effective solution for industrial PCB defect detection.
The integrated attention and optimization modules significantly improve detection accuracy and efficiency.
The network shows strong potential for enhancing quality control in electronics manufacturing.