Mask R-CNN Based Object Detection for Intelligent Wireless Power Transfer

TL;DR

This paper introduces a Mask R-CNN based detection model to identify smartphones in Resonant Beam Charging systems, significantly reducing scanning time and enhancing wireless power transfer efficiency for mobile and IoT devices.

Contribution

The paper presents a novel application of Mask R-CNN for smartphone detection in RBC systems, improving detection speed and system intelligence.

Findings

Smartphone detection time reduced to one third

Enhanced efficiency in wireless power transfer

Improved user experience in IoT device charging

Abstract

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.