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

Published on Dec 1, 2018 in GLOBECOM (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)
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.
  • References (0)
  • Citations (0)
Joseph Redmon6
Estimated H-index: 6
Ali Farhadi35
Estimated H-index: 35
We present some updates to YOLO! We made a bunch of little design changes to make it better. We also trained this new network that's pretty swell. It's a little bigger than last time but more accurate. It's still fast though, don't worry. At 320x320 YOLOv3 runs in 22 ms at 28.2 mAP, as accurate as SSD but three times faster. When we look at the old .5 IOU mAP detection metric YOLOv3 is quite good. It achieves 57.9 mAP@50 in 51 ms on a Titan X, compared to 57.5 mAP@50 in 198 ms by RetinaNet, simi...
Kaiming He42
Estimated H-index: 42
Georgia Gkioxari18
Estimated H-index: 18
+ 1 AuthorsRoss B. Girshick60
Estimated H-index: 60
We present a conceptually simple, flexible, and general framework for object instance segmentation. Our approach efficiently detects objects in an image while simultaneously generating a high-quality segmentation mask for each instance. The method, called Mask R-CNN, extends Faster R-CNN by adding a branch for predicting an object mask in parallel with the existing branch for bounding box recognition. Mask R-CNN is simple to train and adds only a small overhead to Faster R-CNN, running at 5 fps....
Cheng-Yang Fu6
Estimated H-index: 6
Wei Liu11
Estimated H-index: 11
+ 2 AuthorsAlexander C. Berg41
Estimated H-index: 41
The main contribution of this paper is an approach for introducing additional context into state-of-the-art general object detection. To achieve this we first combine a state-of-the-art classifier (Residual-101[14]) with a fast detection framework (SSD[18]). We then augment SSD+Residual-101 with deconvolution layers to introduce additional large-scale context in object detection and improve accuracy, especially for small objects, calling our resulting system DSSD for deconvolutional single shot ...
Published on Dec 1, 2016in IEEE Vehicular Technology Magazine6.14
Qingwen Liu11
Estimated H-index: 11
Jun Wu10
Estimated H-index: 10
(Tongji University)
+ 4 AuthorsLajos Hanzo65
Estimated H-index: 65
(University of Southampton)
Increasing the battery-recharge period of smartphones is becoming a challenge since their power consumption is increased as a result of enhanced functions that require sophisticated multimedia signal processing. An attractive solution is constituted by wireless charging, which is capable of replenishing the battery over the ether. Given this motivation, we present the fundamental physics and the related system structure of a promising wireless charging technique, i.e., distributed laser charging...
Published on Jan 1, 2016 in ECCV (European Conference on Computer Vision)
Wei Liu11
Estimated H-index: 11
(UNC: University of North Carolina at Chapel Hill),
Dragomir Anguelov20
Estimated H-index: 20
+ 4 AuthorsAlexander C. Berg41
Estimated H-index: 41
(UNC: University of North Carolina at Chapel Hill)
We present a method for detecting objects in images using a single deep neural network. Our approach, named SSD, discretizes the output space of bounding boxes into a set of default boxes over different aspect ratios and scales per feature map location. At prediction time, the network generates scores for the presence of each object category in each default box and produces adjustments to the box to better match the object shape. Additionally, the network combines predictions from multiple featu...
Published on Sep 6, 2014 in ECCV (European Conference on Computer Vision)
Tsung-Yi Lin12
Estimated H-index: 12
(Cornell University),
Michael Maire18
Estimated H-index: 18
(California Institute of Technology)
+ 5 AuthorsC. Lawrence Zitnick34
Estimated H-index: 34
We present a new dataset with the goal of advancing the state-of-the-art in object recognition by placing the question of object recognition in the context of the broader question of scene understanding. This is achieved by gathering images of complex everyday scenes containing common objects in their natural context. Objects are labeled using per-instance segmentations to aid in precise object localization. Our dataset contains photos of 91 objects types that would be easily recognizable by a 4...
Published on Jan 1, 2014in IEEE Communications Surveys and Tutorials22.97
Sancheng Peng10
Estimated H-index: 10
(Zhaoqing University),
Shui Yu30
Estimated H-index: 30
(Deakin University),
Aimin Yang2
Estimated H-index: 2
(Guangdong University of Foreign Studies)
Smartphones are pervasively used in society, and have been both the target and victim of malware writers. Motivated by the significant threat that presents to legitimate users, we survey the current smartphone malware status and their propagation models. The content of this paper is presented in two parts. In the first part, we review the short history of mobile malware evolution since 2004, and then list the classes of mobile malware and their infection vectors. At the end of the first part, we...
Published on Feb 1, 2013in IEEE Transactions on Wireless Communications6.39
Qihui Wu22
Estimated H-index: 22
Guoru Ding16
Estimated H-index: 16
+ 1 AuthorsYu-Dong Yao29
Estimated H-index: 29
(Stevens Institute of Technology)
This paper investigates the issue of spatial-temporal opportunity detection for spectrum-heterogeneous cognitive radio networks, where at a given time secondary users (SUs) at different locations may experience different spectrum access opportunities. Most prior studies address either spatial or temporal sensing in isolation and explicitly or implicitly assume that all SUs share the same spectrum opportunity. However, this assumption is not realistic and the traditional non-cooperative sensing (...
Cited By0
View next paperSystem architecture of intelligent personal communication node for body sensor network