Deep Learning Based Solar Cell Recognition for Optical Wireless Power Transfer
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866913552956129280 |
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| author | Huang, Sida Wu, Yuanting Nguyen, Dinh Hoa |
| author_facet | Huang, Sida Wu, Yuanting Nguyen, Dinh Hoa |
| contents | Optical wireless power transfer (OWPT) is a technology that wirelessly transmit light energy from an optical transmitter to an optical receiver, usually a solar cell. In order to achieve the highest transmission efficiency, the solar cell receiver should be accurately aligned with the optical transmitter. Hitherto, only a few works have been existed for solar cell recognition in presence of complex backgrounds. In this paper, we employ a deep learning approach based on Yolov5-Lite for the solar cell recognition purpose, due to its lightweight, fast and easy to deploy on hardware characteristics. Our tests show a high accuracy of the employed deep learning model with the highest F1 score of 91% and mAP of 94.8%. Therefore, this deep learning model is highly promising for use in OWPT systems to precisely align optical transmitters and solar cell receivers. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_14096 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Deep Learning Based Solar Cell Recognition for Optical Wireless Power Transfer Huang, Sida Wu, Yuanting Nguyen, Dinh Hoa Image and Video Processing Systems and Control Optical wireless power transfer (OWPT) is a technology that wirelessly transmit light energy from an optical transmitter to an optical receiver, usually a solar cell. In order to achieve the highest transmission efficiency, the solar cell receiver should be accurately aligned with the optical transmitter. Hitherto, only a few works have been existed for solar cell recognition in presence of complex backgrounds. In this paper, we employ a deep learning approach based on Yolov5-Lite for the solar cell recognition purpose, due to its lightweight, fast and easy to deploy on hardware characteristics. Our tests show a high accuracy of the employed deep learning model with the highest F1 score of 91% and mAP of 94.8%. Therefore, this deep learning model is highly promising for use in OWPT systems to precisely align optical transmitters and solar cell receivers. |
| title | Deep Learning Based Solar Cell Recognition for Optical Wireless Power Transfer |
| topic | Image and Video Processing Systems and Control |
| url | https://arxiv.org/abs/2410.14096 |