Metal surface defects segmentation method using cycle generative adversarial networks on small datasets

Chuxin Yang; Zhenglin Li; Longping Liu

doi:10.1117/12.2680469

27 June 2023 Metal surface defects segmentation method using cycle generative adversarial networks on small datasets

Chuxin Yang, Zhenglin Li, Longping Liu

Proceedings Volume 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022); 127051K (2023) https://doi.org/10.1117/12.2680469
Event: Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 2022, Nanjing, China

Abstract

Aiming at the problems of small metal surface defect samples in industrial production and the difficulty of data annotation in supervised segmentation algorithms, a background reconstruction method based on Cycle generative adversarial networks is proposed to realize metal surface defect segmentation in combination with the traditional threshold segmentation algorithm. Firstly, the corresponding defect-free template is reconstructed from the defect image using Cycle generative adversarial networks, and the defect image and the reconstructed template are subjected to the differential subjected to eliminate the influence of the background texture of the defect sample. Finally, the segmentation process is performed using the adaptive thresholding segmentation method. In order to adapt to the small sample training as well as to improve the performance of background reconstruction of the generative network, the U-Net network structure is used as the generator, and the attention mechanism is also introduced. Meanwhile, the L1 loss and Multi-scale SSIM loss are combined to design the cycle consistency loss for training. The experimental results show that the method in this paper can accomplish good defect segmentation results using a small number of defective samples.

Citation Download Citation

Chuxin Yang, Zhenglin Li, and Longping Liu "Metal surface defects segmentation method using cycle generative adversarial networks on small datasets", Proc. SPIE 12705, Fourteenth International Conference on Graphics and Image Processing (ICGIP 2022), 127051K (27 June 2023); https://doi.org/10.1117/12.2680469

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KEYWORDS

Image segmentation

Deep learning

Metals

Image restoration

Machine learning

Defect detection

Image processing algorithms and systems

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