CONFERENCE PROCEEDINGS
Advanced Search
Home > Proceedings > Volume 13416 > Article
Paper
8 November 2024 Research on multi-scale steel surface defects identification based on YOLO model
Shixi Li, Benchen Yang, Jie Kang, Xuzhao Liu, Shuai Li, Guangbo Yi
Author Affiliations +
Proceedings Volume 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024); 1341623 (2024) https://doi.org/10.1117/12.3049599
Event: 2024 4th International Conference on Advanced Algorithms and Neural Networks, 2024, Qingdao, China
Abstract
The number of surface defects in steel has consistently been a pivotal criterion for evaluation in the context of steel production. Conventional detection techniques, such as the strobe method, are deficient in several respects, including a slow response time and a lack of precision. Nevertheless, object detection technology based on deep learning can effectively address these issues due to its robust real-time performance and high accuracy. The proposed method capitalizes on the rapid and efficient characteristics of the YOLO model, integrating a multi-scale feature extraction module to fuse feature maps of varying scales, thereby enhancing the detection capabilities for a spectrum of steel surface defects. Specifically, the convolutional neural network layer in the YOLO model is employed to extract multi-scale features in the image in a stepwise manner. These features are then integrated together through a feature fusion strategy, thereby facilitating the accurate identification and location of steel surface defects. Experimental analysis demonstrates that the proposed method is markedly superior to the traditional detection method in terms of detection speed and accuracy, and can effectively enhance the performance of steel surface defect identification.
(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Citation Download Citation
Shixi Li, Benchen Yang, Jie Kang, Xuzhao Liu, Shuai Li, and Guangbo Yi "Research on multi-scale steel surface defects identification based on YOLO model", Proc. SPIE 13416, Fourth International Conference on Advanced Algorithms and Neural Networks (AANN 2024), 1341623 (8 November 2024); https://doi.org/10.1117/12.3049599
ACCESS THE FULL ARTICLE
ORGANIZATIONAL
Sign in with credentials provided by your organization.
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
Members: $17.00
Non-members: $21.00 ADD TO CART
PROCEEDINGS
 6 PAGES
DOWNLOAD PAPER SAVE TO MY LIBRARY
GET CITATION
Advertisement
Advertisement
RIGHTS & PERMISSIONS
Get copyright permission  Get copyright permission on Copyright Marketplace
KEYWORDS
Feature extraction
Object detection
Deep learning
Defect detection
Detection and tracking algorithms
Evolutionary algorithms
Education and training
RELATED CONTENT
Subscribe to Digital Library
Receive Erratum Email Alert
Back to Top