A Generative Adversarial Network Based Deep Learning Method for Low-Quality Defect Image Reconstruction and Recognition
Yiping Gao, Liang Gao, Xinyu Li
Abstract
In vision-based defect recognition, deep learning (DL) is a research hotspot. However, DL is sensitive to image quality, and it is hard to collect enough high-quality defect images. The low-quality images usually lose some useful information and may mislead the DL methods into poor results. To overcome this problem, this article proposes a generative adversarial network (GAN)-based DL method for low-quality defect image recognition. A GAN is used to reconstruct the low-quality defect images, and a VGG16 network is built to recognize the reconstructed images. The experimental results under low-quality defect images show that the proposed method achieves very good performances, which has accuracies of 95.53-99.62% with different masks and noises, and they are improved greatly compared with the other methods. Furthermore, the results on PSNR, SSIM, cosine, and mutual information indicate that the quality of the reconstructed image is improved greatly, which is very helpful for defect analysis.