MVRA-UNet: Multi-View Residual Attention U-Net for Precise Defect Segmentation on Magnetic Tile Surface-Reference-Cited by-同舟云学术

MVRA-UNet: Multi-View Residual Attention U-Net for Precise Defect Segmentation on Magnetic Tile Surface

Published:2023 Issue: Volume:11 Page:135212-135221
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Luo Fang¹^ORCID,Cui Yuan²,Liao Yong³^ORCID

Affiliation:

1. Department of Mechatronics and Automotive Engineering, Qingyuan Polytechnic, Qingyuan, China

2. Department of Intelligent Control, Guangzhou Light Industry Vocational School, Guangzhou, China

3. Microelectronics and Optoelectronics Technology Key Laboratory of Hunan Higher Education, School of Physics and Electronic Electrical Engineering, Xiangnan University, Chenzhou, China

Funder

National Natural Science Foundation of China

Natural Science Foundation of Hunan Province, China

Scientific Research Fund of Hunan Provincial Education Department

Applied Characteristic Disciplines of Electronic Science and Technology of Xiangnan University

Guangdong Provincial Department of Education Features Innovative Projects

Guangdong Provincial Department of Education Intelligent Robot Industry-Education Integration Innovation Platform for Higher Vocational Colleges

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/ielx7/6287639/10005208/10328735.pdf?arnumber=10328735

Reference57 articles.

1. Multimodal Fusion Convolutional Neural Network With Cross-Attention Mechanism for Internal Defect Detection of Magnetic Tile

2. A Four-Stage Product Appearance Defect Detection Method With Small Samples

3. ALGAN: Anomaly Detection by Generating Pseudo Anomalous Data via Latent Variables

4. Joint Attention-Guided Feature Fusion Network for Saliency Detection of Surface Defects

5. Mobile HTS Bulk Devices as Enabling Ton-Force Technology for Maglev Trains