YOLO-HMC: An Improved Method for PCB Surface Defect Detection-Reference-Cited by-同舟云学术

YOLO-HMC: An Improved Method for PCB Surface Defect Detection

Published:2024 Issue: Volume:73 Page:1-11
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Yuan Minghao¹^ORCID,Zhou Yongbing¹^ORCID,Ren Xiaoyu¹^ORCID,Zhi Hui²^ORCID,Zhang Jian¹^ORCID,Chen Haojie³^ORCID

Affiliation:

1. School of Mechanical Engineering, Southwest Jiaotong University, Chengdu, China

2. Sichuan Changhong Electric Company Ltd., Mianyang, China

3. State Key Laboratory of Digital Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, China

Funder

Major Science and Technology Special Project “business-driven digital twin simulation software for electronic information manufacturing industry” of Sichuan Province of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/19/10367905/10384383.pdf?arnumber=10384383

Reference45 articles.

1. A lightweight surface defect detection framework combined with dual-domain attention mechanism

2. Review of vision-based defect detection research and its perspectives for printed circuit board

3. Laser-induced thermography: An effective detection approach for multiple-type defects of printed circuit boards (PCBs) multilayer complex structure

4. A Survey on Evolved LoRa-Based Communication Technologies for Emerging Internet of Things Applications

5. Development of hybrid optical sensor based on deep learning to detect and classify the micro-size defects in printed circuit board

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