Real-time Object Detection with Deep Learning for Robot Vision on Mixed Reality Device

被引：0

作者：

Guo, Jiazhen ^{[1
]}

Chen, Peng ^{[1
]}

Jiang, Yinlai ^{[2
]}

Yokoi, Hiroshi ^{[1
]}

Togo, Shunta ^{[1
]}

机构：

[1] Univ Electrocommun, Dept Mech & Intelligent Syst Engn, Tokyo, Japan

[2] Univ Electrocommun, Ctr Neurosci & Biomed Engn, Tokyo, Japan

来源：

2021 IEEE 3RD GLOBAL CONFERENCE ON LIFE SCIENCES AND TECHNOLOGIES (IEEE LIFETECH 2021) | 2021年

关键词：

deep learning; user interface; Microsoft HoloLens; object detection; robot vision;

D O I：

10.1109/LIFETECH52111.2021.9391811

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Mixed reality device sensing capabilities are valuable for robots, for example, the inertial measurement unit (IMU) sensor and time-of-flight (TOF) depth sensor can support the robot in navigating its environment. This paper demonstrates a deep learning (YOLO model) background, real-time object detection system implemented on mixed reality device. The goal of the system is to create a real-time communication system between HoloLens and Ubuntu systems to enable real-time object detection using the YOLO model. The experimental results show that the proposed method has a fast speed to achieve real-time object detection using HoloLens. This enables Microsoft HoloLens as a device for robot vision. To enhance human-robot interaction, we will apply it to a wearable robot arm system to automatically grasp objects in the future.

引用

页码：82 / 83

页数：2

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