Defect detection algorithm for image reconstruction based on the self-attentive mechanism
DOI:
https://doi.org/10.54097/fcis.v3i2.6485Keywords:
Defect detection, Image reconstruction, Loss function, Self-attentive mechanismAbstract
In this paper, we propose an image reconstruction method for defect detection, which introduces a self-attentive mechanism into the generative adversarial neural network to make it capable of extracting global features in response to the weak ability of the generative adversarial neural network to establish image remote dependencies. To address the problem that the adversarial loss function is not suitable for network training, a pixel-by-pixel loss function is introduced to constrain the image generated by the generator so that the output does not deviate too much from the true value during training. The experimental results show that the trained GAN network can effectively reconstruct the defective areas to separate the defects from the background.
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Jia, L., et al. "Fabric defect inspection based on lattice segmentation and Gabor filtering. "Neurocomputing 238.2017:84-102.
Ismail, N., J. M. Zain, and T. Hai. "Fabric authenticity method using fast Fourier transformation detection." Electrical, Control and Computer Engineering (INECCE), 2011 International Conference on IEEE, 2011.
Cohen, F. S., and Z. Fan. "Automated inspection of textile fabrics using textural models." IEEE Transactions on Pattern Analysis and Machine Intelligence 13.8(1991):803-808.
Song, S., et al. "EfficientDet for fabric defect detection based on edge computing." Journal of Engineered Fibers and Fabrics 16.3(2021):1014-1026.
Qin Runtian, Li Yu, and Fan Yujie."Research on Fabric Defect Detection Based on Multi-branch Residual Network." Journal of Physics: Conference Series 1907.1(2021).
He Yuan, Huang Xin Yue,and Hock Tay Francis Eng."Fabric Defect Detection based on Improved Object as Point." International Journal of Computer Science and Information Technology 13.3(2021).
Jing, J., et al. "Fabric defect detection using the improved YOLOv3 model." Journal of Engineered Fibers and Fabrics 15.1(2020):155892502090826.
Li, Y, W. Zhao , and J. Pan . "Deformable Patterned Fabric Defect Detection With Fisher Criterion-Based Deep Learning." IEEE Transactions on Automation Science and Engineering (2017).
Mei, Hyz . "An Unsupervised-Learning-Based Approach for Automated Defect Inspection on Textured Surfaces." Fortschritte der Physik 67.6(2018).
Huang, R., et al. "Prior-guided GAN-based interactive airplane engine damage image augmentation method." Chinese Journal of Aeronautics 35.10(2022):11.
Guanghua Hu, et al. "Unsupervised fabric defect detection based on a deep convolutional generative adversarial network." Textile Research Journal 90.3-4(2020).
Liu Juhua, et al. "Multistage GAN for Fabric Defect Detection." IEEE transactions on image processing: a publication of the IEEE Signal Processing Society 29.(2019).
Ronneberger, Olaf, P. Fischer, and T. Brox. "U-Net: Convolutional Networks for Biomedical Image Segmentation." International Conference on Medical Image Computing and Computer-Assisted Intervention Springer International Publishing, 2015.
Phillip Isola, et al. "Image-to-Image Translation with Conditional Adversarial Networks." CoRR abs/1611.07004. (2016).
Zhao, H, et al. "Loss Functions for Image Restoration With Neural Networks." IEEE Transactions on Computational Imaging (2017).
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