Data-Driven Semantic Segmentation Method for Detecting Metal Surface Defects-Reference-Cited by-同舟云学术

Data-Driven Semantic Segmentation Method for Detecting Metal Surface Defects

Published:2024-05-01 Issue:9 Volume:24 Page:15676-15689
ISSN:1530-437X
Container-title:IEEE Sensors Journal
language:
Short-container-title:IEEE Sensors J.

Author:

Zhang Zhao¹^ORCID,Wang Weibo¹^ORCID,Tian Xiaoyan²^ORCID,Tan Jiubin³^ORCID

Affiliation:

1. Advanced Nuclear and New Energy Research Institute, the Center of Ultra-precision Optoelectronic Instrument Engineering, and the Key Laboratory of Ultra-precision Intelligent Instrumentation, Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin, China

2. Faculty of Computing, Harbin Institute of Technology, Harbin, China

3. Center of Ultra-precision Optoelectronic Instrument Engineering and the Key Laboratory of Ultra-precision Intelligent Instrumentation, Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin, China

Funder

National Natural Science Foundation of China

Key Research and Development Program of Heilongjiang

CGN-HIT Advanced Nuclear and New Energy Research Institute

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/7361/10516472/10487792.pdf?arnumber=10487792

Reference77 articles.

1. Semantic Segmentation of Metal Surface Defects and Corresponding Strategies

2. A Multiscale Attention Mechanism Super-Resolution Confocal Microscopy for Wafer Defect Detection

3. LF-YOLO: A Lighter and Faster YOLO for Weld Defect Detection of X-Ray Image

4. CASDD: Automatic Surface Defect Detection Using a Complementary Adversarial Network

5. Automated Visual Defect Detection for Flat Steel Surface: A Survey