Enhancing Metal Surface Defect Recognition Through Image Patching and Synthetic Defect Generation-Reference-Cited by-同舟云学术

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Enhancing Metal Surface Defect Recognition Through Image Patching and Synthetic Defect Generation

Published:2023 Issue: Volume:11 Page:113339-113359
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Mustafaev Bekhzod¹^ORCID,Kim Sungwon²,Kim Eungsoo³

Affiliation:

1. Department of Artificial Intelligence Convergence, Graduate School, Busan University of Foreign Studies, Busan, South Korea

2. Vazil Company Co., Ltd., Busan, South Korea

3. Division of Electronic Robotics and Information Security Engineering, Busan University of Foreign Studies, Busan, South Korea

Funder

Research and Development Project for the Process Quality Technology Development of Vazil Company Ltd

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/ielx7/6287639/10005208/10274090.pdf?arnumber=10274090

Reference37 articles.

1. A random forest based machine learning approach for mild steel defect diagnosis

2. EfficientNetV2: Smaller models and faster training;tan;Proc Int Conf Mach Learn,2021

3. Binary Gabor pattern (BGP) descriptor and principal component analysis (PCA) for steel surface defects classification

4. MobileNetV2: Inverted Residuals and Linear Bottlenecks

5. Surface Defect Classification for Hot-Rolled Steel Strips by Selectively Dominant Local Binary Patterns

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