Research on surface defect classification method of hot rolled strip steel based on comparative learning

Xingshuai Zang; Shengnan Zhang; Yu He

doi:10.1117/12.3014479

9 January 2024 Research on surface defect classification method of hot rolled strip steel based on comparative learning

Xingshuai Zang, Shengnan Zhang, Yu He

Proceedings Volume 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023); 129691K (2024) https://doi.org/10.1117/12.3014479
Event: International Conference on Algorithm, Imaging Processing and Machine Vision (AIPMV 2023), 2023, Qingdao, China

Abstract

In response to the thin nature of hot rolled steel plates and strips, the vast majority of which are surface defects that can easily lead to production accidents, and limited by the challenges of insufficient datasets and a large amount of unlabeled data, this paper proposes a comparative learning method to solve the above problems. In terms of methods, a dual data augmentation strategy is adopted. Firstly, the original image is data enhanced through manual processing, and CycleGAN is introduced for style transfer to enrich the dataset. Then, ResNet152 network is used for feature extraction, and several comparative learning methods are applied to observe the accuracy of hot rolled strip defect detection. In the end, the improved comparative learning method in this article successfully improved the accuracy of surface defect classification for hot rolled strip steel. Through this research, we are committed to providing more reliable quality control methods for industrial production and reducing the risk of production accidents.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Xingshuai Zang, Shengnan Zhang, and Yu He "Research on surface defect classification method of hot rolled strip steel based on comparative learning", Proc. SPIE 12969, International Conference on Algorithm, Imaging Processing, and Machine Vision (AIPMV 2023), 129691K (9 January 2024); https://doi.org/10.1117/12.3014479

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available