Metallurgy, Vol. 63 No. 3-4, 2024.
Original scientific paper
A method for detecting surface defects in hot-rolled strip steel based on deep learning
H. Ren
; School of Computer Science and Software Engineering, University of Science and Technology Liaoning, China
Y. J. Zhang
; School of Computer Science and Software Engineering, University of Science and Technology Liaoning, China
*
J. T. Chen
; School of Computer Science and Software Engineering, University of Science and Technology Liaoning, China
X. N. Wei
; School of Computer Science and Software Engineering, University of Science and Technology Liaoning, China
H. K. Chen
; School of Computer Science and Software Engineering, University of Science and Technology Liaoning, China
P. Liu
; School of Computer Science and Software Engineering, University of Science and Technology Liaoning, China
* Corresponding author.
Abstract
Hot-rolled strip steel is a material widely used in production activities and daily life. However, the appearance of surface defects during its production process is inevitable. To address this issue, we introduce a new detection method using Gold-Yolo to detect surface defects on hot-rolled strip steel. Our method effectively balances accuracy and real-time performance while detecting four common types of surface defects, achieving an average accuracy rate of 82,2 % for detecting individual types of surface defects. Experimental data prove that our method excels in classifying and locating surface defects on hot-rolled steel strip, demonstrating broad application prospects and promotional value.
Keywords
steel strip; hot-rolled; surface defect; object detection; Gold-Yolo
Hrčak ID:
315692
URI
Publication date:
1.7.2024.
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