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Related Experiment Video

Updated: Jul 16, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

A novel semi-supervised strip steel surface defect classification model based on CycleGAN and ResNet-50.

Liqiang Zhang1, Weilin Cao2, Youming Li2

  • 1School of Artificial Intelligence, Neijiang Normal University, Neijiang, 641100, Sichuan, China. zhangxiaosuan_ai@163.com.

Scientific Reports
|July 10, 2026
PubMed
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This study introduces an improved semi-supervised learning model for steel strip surface defect classification. It enhances data diversity using CycleGAN and improves fine-grained feature recognition with ResNet-50, achieving high accuracy on the NEU-CLS dataset.

Area of Science:

  • Materials Science
  • Computer Science
  • Artificial Intelligence

Background:

  • Steel strip quality is vital for product reliability, necessitating accurate surface defect identification.
  • Existing deep learning models struggle with limited, unlabeled industrial data and GAN-generated samples lacking diversity.
  • Fine-grained texture similarities in defect data further degrade classification performance.

Purpose of the Study:

  • To develop a novel semi-supervised learning model for enhanced steel strip surface defect classification.
  • To address limitations in data diversity and fine-grained feature extraction in existing methods.
  • To improve the generalization ability and accuracy of defect detection models in industrial settings.

Main Methods:

  • A data augmentation strategy using Cycle Generative Adversarial Network (CycleGAN) to generate diverse, realistic unlabeled samples.

Related Experiment Videos

Last Updated: Jul 16, 2026

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine
08:27

Image Recognition and Parameter Analysis of Concrete Vibration State Based on Support Vector Machine

Published on: January 5, 2024

  • Implementation of a ResNet-50 based network for robust feature extraction and classification of fine-grained defects.
  • Training and evaluation using a combination of limited labeled and extensive unlabeled data from the NEU-CLS dataset.

    • Main Results:

    • The proposed model achieved high performance metrics on the NEU-CLS dataset, including 92.91% accuracy, 89.64% precision, and 84.81% recall.
    • The CycleGAN augmentation improved sample diversity and better simulated real industrial defect distributions.
    • The ResNet-50 backbone effectively captured subtle, fine-grained features crucial for distinguishing similar defect types.

    Conclusions:

    • The developed semi-supervised model offers a competitive and effective solution for steel strip surface defect classification, particularly with limited labeled data.
    • CycleGAN-based data augmentation and ResNet-50 feature extraction significantly enhance model performance and generalization.
    • This approach demonstrates a promising direction for improving automated quality control in steel manufacturing.