An End-to-End Steel Surface Classification Approach Based on EDCGAN and MobileNet V2.

ABSTRACT: In the production process of steel products, it is very important to find defects, which can not only reduce the failure rate of industrial production but also can reduce economic losses. All deep learning-based methods need many labeled samples for training. However, in the industrial field, there is a lack of sufficient training samples, especially in steel surface defects. It is almost impossible to collect enough samples that can be used for training. To solve this kind of problem, different from traditional data enhancement methods, this paper constructed a data enhancement model dependent on GAN, using our designed EDCGAN to generate abundant samples that can be used for training. Finally, we mixed different proportions of the generated samples with the original samples and tested th

SUBMITTER: Jin G 

PROVIDER: S-EPMC9966273 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature