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Steel surface defects recognition based on multi-type statistical features and enhanced twin support vector machine

  • Published:2017-12 Issue: Volume:171 Page:140-150
  • ISSN:0169-7439
  • Container-title:Chemometrics and Intelligent Laboratory Systems
  • language:en
  • Short-container-title:Chemometrics and Intelligent Laboratory Systems

Author:

Chu Maoxiang,Gong Rongfen,Gao Song,Zhao Jie

Funder

Liaoning Province

Liaoning Province Ministry of Education

State Key Laboratory of Robotics and System (HIT)

University of Science and Technology Liaoning Foundation

Publisher

Elsevier BV

Subject

Spectroscopy,Process Chemistry and Technology,Computer Science Applications,Software,Analytical Chemistry

Cited by 72 articles. 订阅此论文施引文献 订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Road Surface Defect Detection—From Image-Based to Non-Image-Based: A Survey;IEEE Transactions on Intelligent Transportation Systems;2024-09

2. High-Frequency Dual-Branch Network for Steel Small Defect Detection;Arabian Journal for Science and Engineering;2024-08-07

3. Slim-YOLOv8: A fast and accurate algorithm for surface defect detection of steel strips;Ironmaking & Steelmaking: Processes, Products and Applications;2024-07-23

4. Lightweight strip steel defect detection algorithm based on improved YOLOv7;Scientific Reports;2024-06-10

5. Advancing Robust Few-shot Surface Defect Detection through Meta-learning;2024 9th International Conference on Integrated Circuits, Design, and Verification (ICDV);2024-06-06
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