# Deep Learning Based Solar Cell Recognition for Optical Wireless Power Transfer

arXiv: https://arxiv.org/abs/2410.14096 · HTML: https://mlsanity.com/paper/2410.14096/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Oct 2024.

## Built on by (most cited first, 1 of 1)

- [Comparing Object Detection Models for Electrical Substation Component Mapping](https://mlsanity.com/paper/2512.22454/) (2025)

## Abstract (arXiv)

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.