| Autor: |
Xiafei Ma, Zongliang Xie, Haotong Ma, Xu Yangjie, Dong He, Ge Ren |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
IEEE Photonics Journal, Vol 14, Iss 4, Pp 1-5 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
1943-0655 |
| DOI: |
10.1109/JPHOT.2022.3194509 |
| Popis: |
The image-based piston sensing method using the convolutional neural network (CNN) is an advanced technique which has good applicability. However, acquiring a large amount of the training dataset required to train a network is difficult to handle in practice. In this letter, we demonstrate the possibility of using a neural network trained by the simulation dataset to accurately sense pistons directly from experimental images. As a demonstration of the proposed scheme, a single CNN developed by computer-generated images is applied for piston measurement of an experimental setup with three sub-apertures. This is particularly helpful for the sparse aperture system with more sub-apertures. We believe that the study in this letter will contribute to the applications of the CNN-based technique for piston sensing. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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