论文标题
智能无线电源传输的基于蒙版R-CNN的对象检测
Mask R-CNN Based Object Detection for Intelligent Wireless Power Transfer
论文作者
论文摘要
谐振光束充电（RBC）是具有安全性，移动性和同时充电能力的有前途的多瓦和多米无线功率传输方法。但是，RBC系统操作依赖于信息可用性，包括电源接收器位置，类标签和接收器号。由于智能手机是最广泛使用的移动设备，因此我们在RBC系统中提出了一个基于蒙版R-CNN的智能手机检测模型。实验表明，我们的模型将智能手机扫描时间减少到三分之一。因此，这款机器LearningDetectionDetectionDectectionAppRaceProvidesanIntelligentwayWay将移动和物联网（IoT）设备无线电源传输的用户体验。
Resonant Beam Charging (RBC) is a promising multi-Watt and multi-meter wireless power transfer method with safety, mobility and simultaneously-charging capability. However, RBC system operation relies on information availability including power receiver location, class label and the receiver number. Since smartphone is the most widely-used mobile device, we propose a Mask R-CNN based smartphone detection model in the RBC system. Experiments illustrate that our model reduces the smartphone scanning time to one third. Thus, this machine learningdetectionapproachprovidesanintelligentwaytoimprove the user experience in wireless power transfer for mobile and Internet of Things (IoT) devices.
