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

共 50 条

[1] Real-time Object Detection and Tracking in Mixed Reality using Microsoft HoloLens
Farasin, Alessandro
Peciarolo, Francesco
Grangetto, Marco
Gianaria, Elena
Garza, Paolo
VISAPP: PROCEEDINGS OF THE 15TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS, VOL 4: VISAPP, 2020, : 165 - 172
[2] Evaluating Pruned Object Detection Networks for Real-Time Robot Vision
O'Keeffe, Simon
Villing, Rudi
2018 IEEE INTERNATIONAL CONFERENCE ON AUTONOMOUS ROBOT SYSTEMS AND COMPETITIONS (ICARSC), 2018, : 91 - 96
[3] Platooning control of drones with real-time deep learning object detection
Dai, Xin
Nagahara, Masaaki
ADVANCED ROBOTICS, 2023, 37 (03) : 220 - 225
[4] Deep Learning Based, Real-Time Object Detection for Autonomous Driving
Akyol, Gamze
Kantarci, Alperen
Celik, Ali Eren
Ak, Abdullah Cihan
2020 28TH SIGNAL PROCESSING AND COMMUNICATIONS APPLICATIONS CONFERENCE (SIU), 2020,
[5] Real-Time Deep Learning-Based Object Detection Framework
Tarimo, William
Sabra, Moustafa M.
Hendre, Shonan
2020 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (SSCI), 2020, : 1829 - 1836
[6] Introduction to Computer Vision and Real Time Deep Learning-based Object Detection
Shanahan, James G.
CIKM '20: PROCEEDINGS OF THE 29TH ACM INTERNATIONAL CONFERENCE ON INFORMATION & KNOWLEDGE MANAGEMENT, 2020, : 3515 - 3516
[7] Introduction to Computer Vision and Real Time Deep Learning-based Object Detection
Shanahan, James G.
Dai, Liang
KDD '20: PROCEEDINGS OF THE 26TH ACM SIGKDD INTERNATIONAL CONFERENCE ON KNOWLEDGE DISCOVERY & DATA MINING, 2020, : 3523 - 3524
[8] Experimental Deep Learning Object Detection in Real-time Colonoscopies
Ciobanu, Adrian
Luca, Mihaela
Barbu, Tudor
Drug, Vasile
Olteanu, Andrei
Vulpoi, Radu
2021 INTERNATIONAL CONFERENCE ON E-HEALTH AND BIOENGINEERING (EHB 2021), 9TH EDITION, 2021,
[9] Real-time Quadrilateral Object Corner Detection Algorithm Based on Deep Learning
Zhang, Jinfeng
Jiao, Zhibin
An, Xiangjing
He, Yejun
2019 COMPUTING, COMMUNICATIONS AND IOT APPLICATIONS (COMCOMAP), 2019, : 70 - 75
[10] Deep Learning-Based Real-time Object Detection in Inland Navigation
Hammedi, Wided
Ramirez-Martinez, Metzli
Brunet, Philippe
Senouci, Sidi Mohammed
Messous, Mohamed Ayoub
2019 IEEE GLOBAL COMMUNICATIONS CONFERENCE (GLOBECOM), 2019,

← 1 2 3 4 5 →