用轻量级AI模型实现复杂背景下的太阳能电池精准识别，提升无线光能传输效率。
Deep Learning Based Solar Cell Recognition for Optical Wireless Power Transfer
- 基于Yolov5-Lite的深度学习方法，兼顾速度与部署便利性
- 最高F1得分91%，mAP达94.8%，识别精度高
- 适合嵌入式设备，适用于需快速对准的光能传输系统
光无线能量传输（OWPT）通过光学发射器向光电接收器（通常为太阳能电池）无线传输光能。为实现最高传输效率，接收端太阳能电池需精确对准发射器。目前，复杂背景下的太阳能电池识别研究仍较少。本文采用基于Yolov5-Lite的深度学习方法，因其轻量化、快速且易于硬件部署。实验表明，该模型在测试中达到最高F1分数91%和mAP 94.8%。结果表明，该深度学习模型在OWPT系统中用于精确对准发射器与太阳能电池接收器方面具有高度前景。
原文摘要 · Abstract (English)
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.
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