A method for detecting surface defects in hot-rolled strip steel based on deep learning

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