Printed Circuit Boards Defect Detection Method Based on Improved Fully Convolutional Networks-Reference-Cited by-同舟云学术

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Printed Circuit Boards Defect Detection Method Based on Improved Fully Convolutional Networks

Published:2022 Issue: Volume:10 Page:109908-109918
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Zheng Jianfeng¹^ORCID,Sun Xiaopeng¹^ORCID,Zhou Haixiang¹,Tian Chenyang¹,Qiang Hao¹

Affiliation:

1. School of Mechanical Engineering and Rail Transit, Changzhou University, Changzhou, China

Funder

Postgraduate Research and Practice Innovation Program of Jiangsu Province

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09918069.pdf?arnumber=9918069

Reference25 articles.

1. Machine Vision-Based Defect Detection in IC Images Using the Partial Information Correlation Coefficient

2. The Increasing Importance of Utilizing Non-intrusive Board Test Technologies for Printed Circuit Board Defect Coverage

3. Fast Eddy Current Testing Defect Classification Using Lissajous Figures

4. Defect detection of PCB based on Bayes feature fusion

5. Estimation of Deep Defect in Ferromagnetic Material by Low Frequency Eddy Current Method

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2. Seven decades of image super-resolution: achievements, challenges, and opportunities;EURASIP Journal on Advances in Signal Processing;2024-07-18

3. Flaw Detection in PCB Using Deep Learning and Image Processing;2024 2nd International Conference on Sustainable Computing and Smart Systems (ICSCSS);2024-07-10

4. Fault Detection in Printed Circuit Board (PCB) using Image Subtraction Method;International Journal of Innovative Technology and Exploring Engineering;2024-06-30

5. Measurement of Si pixel sensor alignment for the ALICE ITS detector;Measurement Science and Technology;2024-06-25

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