Near Real-Time Object Recognition for Pepper Based on Deep Neural Networks Running on a Backpack

被引：2

作者：

Reyes, Esteban ^{[1
]}

Gomez, Cristopher ^{[1
]}

Norambuena, Esteban ^{[1
]}

Ruiz-del-Solar, Javier ^{[1
,2
]}

机构：

[1] Univ Chile, Dept Elect Engn, Santiago, Chile

[2] Univ Chile, Adv Min Technol Ctr, Santiago, Chile

来源：

ROBOT WORLD CUP XXII, ROBOCUP 2018 | 2019年 / 11374卷

关键词：

Pepper robot; YOLO; Jetson TK1; ROS;

D O I：

10.1007/978-3-030-27544-0_24

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The main goal of this work is to provide Pepper with a near real-time object recognition system based on deep neural networks. The proposed system is based on YOLO (You Only Look Once), a deep neural network that is able to detect and recognize objects robustly and at a high speed. In addition, considering that YOLO cannot be run in the Pepper's internal computer in near real-time, we propose to use a Backpack for Pepper, which holds a Jetson TK1 card and a battery. By using this card, Pepper is able to robustly detect and recognize objects in images of 320 x 320 pixels at about 5 frames per second.

引用

页码：287 / 298

页数：12

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