  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.
