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

PubDate: April 2021

Teams: The University of Electro-Communications

Writers: Jiazhen Guo; Peng Chen; Yinlai Jiang; Hiroshi Yokoi; Shunta Togo

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

Abstract

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.