An Improved YOLOv3 Network for PCB Defect Detection-Reference-Cited by-同舟云学术

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An Improved YOLOv3 Network for PCB Defect Detection

Published:2021-10-22 Issue: Volume: Page:
ISSN:
Container-title:2021 China Automation Congress (CAC)
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Author:

Shao Guifang¹,Zheng Yuyun¹,Li Tiejun²,Wu Jianyi¹,Luo Jincai³,Gao Fengqiang¹,Ma Jianqi¹,Liu Tundong¹

Affiliation:

1. Xiamen University,Xiamen Key Lab. of Big Data Intelligent Analysis and Decision,Department of Automation,Xiamen,China

2. Xiamen University,School of Aerospace Engineering,Xiamen,China

3. Jimei University,School of Information Engineering,Xiamen,China

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9727224/9727236/09728216.pdf?arnumber=9728216

Reference18 articles.

1. Detection of Defects in Printed Circuit Boards by Clustering the Etalon and Defected Samples

2. Improved bare PCB defect detection approach based on deep feature learning

3. SSIM-NET: Real-Time PCB Defect Detection Based on SSIM and MobileNet-V3

4. Defect detection in printed circuit boards using You-Only- Look-Once convolutional neural networks;adibhatla;Electronics,2020

5. An Efficient and Robust Target Detection Algorithm for Identifying Minor Defects of Printed Circuit Board Based on PHFE and FL-RFCN

Cited by 3 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Research on PCB defect detection using artificial intelligence: a systematic mapping study;Evolutionary Intelligence;2024-04-02

2. Printed circuit board inspection using computer vision;Multimedia Tools and Applications;2023-07-14

3. MR-YOLO: An Improved YOLOv5 Network for Detecting Magnetic Ring Surface Defects;Sensors;2022-12-15

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