Humanoid robot detection using deep learning: A speed-accuracy tradeoff

Mohammad Javadi, Sina Mokhtarzadeh Azar, Sajjad Azami, Saeed Shiry Ghidary, Soroush Sadeghnejad*, Jacky Baltes

*此作品的通信作者

研究成果: 書貢獻/報告類型會議論文篇章

13 引文 斯高帕斯(Scopus)

摘要

Recent advances in computer vision have made the detection of landmarks on the soccer field easier for teams. However, the detection of other robots is also a critical capability that has not garnered much attention in the RoboCup community so far. This problem is well represented in different RoboCup Soccer and Rescue Robot Leagues. In this paper, we compare several two-stage detection systems based on various Convolutional Neural Networks (CNN) and highlight their speed-accuracy trade off. The approach performs edge based image segmentation in order to reduce the search space and then a CNN validates the detection in the second stage. We use images of different humanoid robots to train and test three different CNN architectures. A part of these images was gathered by our team and will be publicly available. Our experiments demonstrate the strong adaptability of deeper CNNs. These models, trained on a limited set of robots, are able to successfully distinguish an unseen kind of humanoid robot from non-robot regions.

原文英語
主出版物標題RoboCup 2017
主出版物子標題Robot World Cup XXI
編輯Hidehisa Akiyama, Oliver Obst, Claude Sammut, Flavio Tonidandel
發行者Springer Verlag
頁面338-349
頁數12
ISBN(列印)9783030003074
DOIs
出版狀態已發佈 - 2018
事件21st RoboCup International Symposium, 2017 - Nagoya, 日本
持續時間: 2017 七月 272017 七月 31

出版系列

名字Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
11175 LNAI
ISSN(列印)0302-9743
ISSN(電子)1611-3349

會議

會議21st RoboCup International Symposium, 2017
國家/地區日本
城市Nagoya
期間2017/07/272017/07/31

ASJC Scopus subject areas

  • 理論電腦科學
  • 電腦科學(全部)

指紋

深入研究「Humanoid robot detection using deep learning: A speed-accuracy tradeoff」主題。共同形成了獨特的指紋。

引用此