Metalurgija (Jan 2024)

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

  • H. Ren,
  • Y. J. Zhang,
  • J. T. Chen,
  • X. N. Wei,
  • H. K. Chen,
  • P. Liu

Journal volume & issue
Vol. 63, no. 3-4
pp. 423 – 426

Abstract

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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.

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