  Resonant Beam Charging (RBC) is a wireless charging technology which supports
multi-watt power transfer over meter-level distance. The features of safety,
mobility and simultaneous charging capability enable RBC to charge multiple
mobile devices safely at the same time. To detect the devices that need to be
charged, a Mask R-CNN based dection model is proposed in previous work.
However, considering the constraints of the RBC system, it's not easy to apply
Mask R-CNN in lightweight hardware-embedded devices because of its heavy model
and huge computation. Thus, we propose a machine learning detection approach
which provides a lighter and faster model based on traditional Mask R-CNN. The
proposed approach makes the object detection much easier to be transplanted on
mobile devices and reduce the burden of hardware computation. By adjusting the
structure of the backbone and the head part of Mask R-CNN, we reduce the
average detection time from $1.02\mbox{s}$ per image to $0.6132\mbox{s}$, and
reduce the model size from $245\mbox{MB}$ to $47.1\mbox{MB}$. The improved
model is much more suitable for the application in the RBC system.
