Joint Attention-Guided Feature Fusion Network for Saliency Detection of Surface Defects-Reference-Cited by-同舟云学术

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

Published:2022 Issue: Volume:71 Page:1-12
ISSN:0018-9456
Container-title:IEEE Transactions on Instrumentation and Measurement
language:
Short-container-title:IEEE Trans. Instrum. Meas.

Author:

Jiang Xiaoheng¹^ORCID,Yan Feng¹^ORCID,Lu Yang¹^ORCID,Wang Ke¹^ORCID,Guo Shuai¹^ORCID,Zhang Tianzhu²^ORCID,Pang Yanwei³^ORCID,Niu Jianwei⁴^ORCID,Xu Mingliang¹^ORCID

Affiliation:

1. School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou, China

2. School of Information Science and Technology, University of Science and Technology of China, Hefei, China

3. School of Electrical Automation and Information Engineering, Tianjin University, Tianjin, China

4. State Key Laboratory of Virtual Reality Technology and Systems, School of Computer Science and Engineering, Beihang University, Beijing, China

Funder

National Key Research and Development Program of China

National Natural Science Foundation of China

China Postdoctoral Science Foundation

Foundation for University Key Research of Henan Province

Hangzhou Innovation Institute, Beihang University

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Instrumentation

Link

http://xplorestaging.ieee.org/ielx7/19/9717300/09933904.pdf?arnumber=9933904

Reference50 articles.

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3. Saliency detection for strip steel surface defects using multiple constraints and improved texture features

4. Multiscale structural similarity for image quality assessment

5. Optimizing intersection-over-union in deep neural networks for image segmentation;rahman;Proc Int Symp Vis Comput,2016

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