Lightweight Mask R-CNN for Long-Range Wireless Power Transfer Systems

TL;DR

This paper presents a lightweight and faster version of Mask R-CNN tailored for device detection in wireless power transfer systems, enabling deployment on mobile hardware with reduced computational load.

Contribution

A novel lightweight Mask R-CNN model with structural modifications that significantly reduces detection time and model size for wireless power transfer applications.

Findings

Detection time reduced from 1.02s to 0.6132s per image.

Model size decreased from 245MB to 47.1MB.

Enhanced suitability for mobile device deployment.

Abstract

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.