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Academic Journal of Computing & Information Science, 2023, 6(3); doi: 10.25236/AJCIS.2023.060302.

A Semi-Supervised Method for Steel Surface Defect Detection Based on Soft-teacher


Heng Xiao1,2, Changwei Zhao1,2, Zhiyong Zhang1,2

Corresponding Author:
Heng Xiao

1College of Information Engineering, Henan University of Science and Technology, Luoyang, China

2Henan International Joint Laboratory of Cyberspace Security Applications, Henan University of Science and Technology, Luoyang, China


Metal products are indispensable raw materials for society nowadays. Surface defects is inevitably occur during the production process, so defect detection is an important method to improve product quality. For the problem of lack of annotation of defect images in new production lines and low efficiency of manual annotation, we propose a semi-supervised defect detection method with improvement of Soft-teacher. Firstly, using mixed data enhancement to bring perturbation to the dataset and further utilize unlabeled images to enhance the effect of consistent training; then Swin-Transformer will be used as the backbone network to reduce the information loss caused by pooling, using shifted feature pyramids to do Multi-scale training, it can use the same label to supervise inputs of different sizes to obtain high-quality pseudo-labeling. Experiments on the NEU-DET dataset show that the method achieves good results in semi-supervised strip defect detection, and obtains 68.1% AP50 detection accuracy with 20% of labeled data, which is high enough to meet the needs of preliminary defect detection and labeling.


Surface defect detection; Deep learning; Semi-Supervised; Object detection

Cite This Paper

Heng Xiao, Changwei Zhao, Zhiyong Zhang. A Semi-Supervised Method for Steel Surface Defect Detection Based on Soft-teacher. Academic Journal of Computing & Information Science (2023), Vol. 6, Issue 3: 11-19. https://doi.org/10.25236/AJCIS.2023.060302.


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