Improved CycleGAN for super-resolution of engineering drawings

Abstract

In intelligent manufacturing, the quality of machine translation engineering drawings will directly affect its manufacturing accuracy. Currently, most of the engineering drawing translation work is done manually, which greatly reduces production efficiency. This paper proposes an automatic translation method for welded structural engineering drawings based on Cyclic Generative Adversarial Networks (Cyclegan). The Cyclegan network model of unpaired transfer learning is used to learn the feature mapping of real welding engineering drawings to realize the automatic translation of engineering drawings. U-Net and Patchgan are the main network for the generator and discriminator, respectively. Based on removing the identity mapping function, a high-dimensional sparse network is proposed to replace the traditional dense network for the Cyclegan generator to improve noise robustness. Increase the residual block hidden layer to increase the resolution of the generated graph. The improved and fine-tuned network models are experimentally validated, computing the gap between real and generated data. It meets the welding engineering precision standard and solves the main problem of low drawing recognition efficiency in the welding manufacturing process. The results show. After training with our model, the PSNR, SSIM, and MSE of welding engineering drawings reach about 44.89%, 99.58%, and 2.11, respectively, which are superior to traditional networks in both training speed and accuracy.