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

Autor:	Peng Chen, Hiroshi Yokoi, Jiazhen Guo, Yinlai Jiang, Shunta Togo
Rok vydání:	2021
Předmět:	Computer science Inertial measurement unit business.industry Deep learning GRASP Robot Computer vision Robot vision Artificial intelligence Communications system business Mixed reality Object detection
Zdroj:	LifeTech
DOI:	10.1109/lifetech52111.2021.9391811
Popis:	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, realtime 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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::d21025a034f4b0aa89bacc8de953ca7c https://doi.org/10.1109/lifetech52111.2021.9391811 Zobrazit plný text záznamu