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A Highly Efficient and Lightweight Detection Method for Steel Surface Defect.

Authors :
Xu, Changyu
Li, Jie
Li, Xianguo
Source :
Journal of Nondestructive Evaluation. Sep2024, Vol. 43 Issue 3, p1-13. 13p.
Publication Year :
2024

Abstract

The detection of steel surface defects is of great significance to steel production. In order to better meet the requirements of accuracy, real-time, and lightweight model, this paper proposes a highly efficient and lightweight steel surface defect detection method based on YOLOv5n. Firstly, ODMobileNetV2 composed of MobileNetV2 and ODConv is used as the backbone to improve the defect feature extraction capability. Secondly, GSConv is utilized in the neck to achieve deep information fusion through channel concatenation and shuffling, enhancing the ability of feature fusion. Finally, this paper proposes a spatial-channel reconstruction block (SCRB) designed to suppress redundant features and improve the representation ability of defect features through feature separation and reconstruction. Experimental results show that this method achieves 84.1% mAP and 109 FPS on the NEU-DET dataset, and 72.9% mAP and 110.1 FPS on the GC10-DET dataset, enabling accurate and efficient detection. Furthermore, the number of parameters is only 5.04M, which has a significant lightweight advantage. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
01959298
Volume :
43
Issue :
3
Database :
Academic Search Index
Journal :
Journal of Nondestructive Evaluation
Publication Type :
Academic Journal
Accession number :
178560060
Full Text :
https://doi.org/10.1007/s10921-024-01084-7