Collaborative Learning Classification Model for PCBs Defect Detection Against Image and Label Uncertainty-Reference-Cited by-同舟云学术

Collaborative Learning Classification Model for PCBs Defect Detection Against Image and Label Uncertainty

Published:2023 Issue: Volume:72 Page:1-8
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Yu Xinyi¹^ORCID,Han-Xiong Li²^ORCID,Yang Haidong³^ORCID

Affiliation:

1. School of Mechanical and Electrical Engineering, Central South University, Changsha, China

2. Department of Advanced Design and Systems Engineering, City University of Hong Kong, Kowloon Tong, Hong Kong

3. Guangdong Engineering Research Center for Green Manufacturing and Energy Efficiency Optimization, Guangdong University of Technology, Guangzhou, China

Funder

Guangdong-Hong Kong Joint Project

Key Research Project in Foshan city

Research Project from Pengcheng Laboratory

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/10012124/10012303.pdf?arnumber=10012303

Reference39 articles.

1. Delving Deep Into Label Smoothing

2. HRIPCB: a challenging dataset for PCB defects detection and classification

3. 3D Filtering by Block Matching and Convolutional Neural Network for Image Denoising

4. Efficient Wiener filtering without preconditioning

5. Tri-state median filter for image denoising

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