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Surface Defect Segmentation Method for Thin Strip Cast Rolled Steel Plates Based on Improved TransUNet
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Zhi-hua MA1, Bo CHEN1, Kai ZENG1, 2, 3, 4, *, Jun-lei QIAN1, 4, Peng-cheng XIAO2, 3, Li-guang ZHU3, 5
Science Technology and Engineering | 2025, 25(10) : 4239 - 4245
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Science Technology and Engineering | 2025, 25(10): 4239-4245
• Papers·Automation and Computational Technology •
Surface Defect Segmentation Method for Thin Strip Cast Rolled Steel Plates Based on Improved TransUNet
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Zhi-hua MA1, Bo CHEN1, Kai ZENG1, 2, 3, 4, *, Jun-lei QIAN1, 4, Peng-cheng XIAO2, 3, Li-guang ZHU3, 5
Affiliations
  • 1 College of Electrical Engineering, North China University of Science and Technology, Tangshan 063210, China
  • 2 College of Metallurgy and Energy, North China University of Science and Technology, Tangshan 063210, China
  • 3 Hebei Collaborative Innovation Center of High Quality Steel Continuous Casting Engineering Technology, Tangshan 063000, China
  • 4 Tangshan Iron and Steel Enterprise Process Control and Optimization Technology Innovation Center, Tangshan 063000, China
  • 5 College of Materials Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China
Published: 2025-04-08 doi: 10.12404/j.issn.1671-1815.2403371
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A semantic segmentation-based method for defect segmentation on thin strip cast and rolled steel plates was proposed to accurately and quickly identify surface defects. Firstly, defect images from the production line were annotated using Labeling software to create a defect segmentation dataset. Secondly, a TransUNet network model was established to recognize and segment surface defects, integrating an optimized DANet dual-attention fusion network to enhance model segmentation performance. Finally, comparative experiments between the improved model and other segmentation models were designed. The feasibility and effectiveness of the proposed method are verified through analysis of experimental results and evaluation metrics. The experiments demonstrate that the improved network achieves a segmentation accuracy of 96.85%, an average intersection over union of 96.99%, and a similarity coefficient of 92.98% for foreign object defects on thin strip cast and rolled steel plates, respectively increasing by 1.19%, 0.61%, and 0.63% compared to the TransUNet network. Additionally, the improved network achieves a segmentation accuracy of 92.86% on the publicly available hot-rolled strip steel defect dataset, indicating its versatility and providing technical guidance for intelligent detection of surface defects on steel plates.

thin strip casting and rolling  /  semantic segmentation  /  defect identification  /  dual attention
Zhi-hua MA, Bo CHEN, Kai ZENG, Jun-lei QIAN, Peng-cheng XIAO, Li-guang ZHU. Surface Defect Segmentation Method for Thin Strip Cast Rolled Steel Plates Based on Improved TransUNet[J]. Science Technology and Engineering, 2025 , 25 (10) : 4239 -4245 . DOI: 10.12404/j.issn.1671-1815.2403371
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Year 2025 volume 25 Issue 10
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Article Info
doi: 10.12404/j.issn.1671-1815.2403371
  • Receive Date：2024-05-08
  • Online Date：2025-07-09
  • Published：2025-04-08
Article Data
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History
  • Received：2024-05-08
  • Revised：2025-01-03
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Affiliations
    1 College of Electrical Engineering, North China University of Science and Technology, Tangshan 063210, China
    2 College of Metallurgy and Energy, North China University of Science and Technology, Tangshan 063210, China
    3 Hebei Collaborative Innovation Center of High Quality Steel Continuous Casting Engineering Technology, Tangshan 063000, China
    4 Tangshan Iron and Steel Enterprise Process Control and Optimization Technology Innovation Center, Tangshan 063000, China
    5 College of Materials Science and Engineering, Hebei University of Science and Technology, Shijiazhuang 050018, China
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