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Conference Paper
Magnifier Network for Ceramic Z-directional Height Estimation with a Single Image
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- Authors
- Hye-Jin S. Kim, Yoonsoo Han, Suyoung Chi
- Issue Date
- 2020-10
- Citation
- International Conference on Information and Communication Technology Convergence (ICTC) 2020, pp.1199-1202
- Publisher
- IEEE
- Language
- English
- Type
- Conference Paper
- Abstract
- Due to the AI development, productivity of manufactures has been increased. This paper introduces a method that detects three dimensional size defects in ceramic products using a monocular depth estimation method. Conventional depth estimation is weak to deal with two-sided biased data. This paper addresses a brand new magnifier network in order to magnify important parts among an input with various ranges. Deep neural networks have considered that they are weak to learn partial area. However, the proposed method overcomes this weak point by applying to a proposed magnifier method. In this paper, we present that the proposed method increases the performance in terms of qualitative and quantitative aspects.
- KSP Keywords
- Ceramic products, Deep neural network(DNN), Estimation method, Monocular depth estimation, Partial area, Single image, Three dimensional(3D), Three dimensional size, height estimation