Ceramic Bearing Surface Defect Detection Method Based on Transformer
A Transformer based super-resolution residual network is proposed for the problem of low accu-racy of subsequent defect detection due to blurred surface imaging caused by large curvature and low con-trast of ceramic bearing roller surface in traditional machine vision inspection methods.Firstly,the network uses a residual learning strategy to achieve the super-resolution task by predicting the difference between blurred and clear images;Secondly,a channel attention module and a spatial attention module are inserted in the front end of the network and the L2 multi-head self-attention module is improved to enhance the image texture and improve the gradient explosion problem;Finally,a two-stage training strategy is proposed to op-timize the training process for the super-resolution reconstruction task.The extensive experimental results on the self-built ceramic bearing surface defect dataset show that the proposed network model outperforms su-per-resolution algorithms such as MSESRGAN and VSDR in both objective metrics and subjective evalua-tion,with a reconstructed image SSIM of 0.939 and PSNR of 36.51 dB.