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

Published on Dec 1, 2018 in Global Communications Conference
· DOI :10.1109/glocomw.2018.8644387
Aozhou Wu (Tongji University), Qingqing Zhang2
Estimated H-index: 2
(Tongji University)
+ 4 AuthorsPengfei Xia2
Estimated H-index: 2
(Tongji University)
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 learning detection approach provides an intelligent way to improve the user experience in wireless power transfer for mobile and Internet of Things (IoT) devices.
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