Cyclegan Network for Sheet Metal Welding Drawing Translation
- URL: http://arxiv.org/abs/2209.14106v1
- Date: Wed, 28 Sep 2022 13:55:36 GMT
- Title: Cyclegan Network for Sheet Metal Welding Drawing Translation
- Authors: Zhiwei Song, Hui Yao, Dan Tian, Gaohui Zhan
- Abstract summary: 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.
After training with our model, the PSNR, SSIM and MSE of welding engineering drawings reach about 44.89%, 99.58% and 2.11, respectively.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In intelligent manufacturing, the quality of machine translation engineering
drawings will directly affect its manufacturing accuracy. Currently, most of
the work is manually translated, greatly reducing 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
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.
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