In Industry 4.0, internet of things (IoT) technologies are expanding and advanced smart factories are currently being developed. To build an automated visual inspection (AVI) and achieve smartization of steel manufacturing, detecting defects in products in real-time and accurately diagnosing the quality of products are essential elements. As in various manufacturing industries, the steel manufacturing process presents a class imbalance problem for products. For example, fewer defect images are available than normal images. This study developed a new image synthesis methodology for the steel manufacturing industry called a latent mapping adversarial network. Inspired by the style-based generative adversarial network (StyleGAN) structure, we constructed a mapping network for the latent space, which made it possible to compose defect images of various sizes. We discovered the most suitable loss function, and optimized the proposed method in terms of convergence and computational cost. The experimental results demonstrate the competitive performance of the proposed model compared to the traditional models in terms of classification accuracy of 92.42% and F-score of 93.15%. Consequently, the problem of data imbalance is solved, and higher productivity in steel products is expected.
Bibliographical noteFunding Information:
This work was supported by the National Research Foundation of Korea (NRF) grants funded by the Korean government (MSIT) (NRF-2022R1A2C2004457, NRF-2021R1A6A3A13045200). This work was also supported by Brain Korea 21 FOUR and Samsung Electronics Co., Ltd. (IO201210-07929-01).
© 2022 by the authors.
- automated visual inspection
- defect synthesis
- generative adversarial networks
- internet of things
- latent mapping adversarial networks
ASJC Scopus subject areas
- Control and Systems Engineering
- Signal Processing
- Hardware and Architecture
- Computer Networks and Communications
- Electrical and Electronic Engineering