@inproceedings{1b8d5f7d7a12487ebe7848afd60af54c,
title = "Humanoid robot detection using deep learning: A speed-accuracy tradeoff",
abstract = "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.",
keywords = "Convolutional neural networks, Deep learning, Humanoid robots, Image segmentation, Robot detection, Robot vision",
author = "Mohammad Javadi and Azar, \{Sina Mokhtarzadeh\} and Sajjad Azami and Ghidary, \{Saeed Shiry\} and Soroush Sadeghnejad and Jacky Baltes",
note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2018.; 21st RoboCup International Symposium, 2017 ; Conference date: 27-07-2017 Through 31-07-2017",
year = "2018",
doi = "10.1007/978-3-030-00308-1\_28",
language = "English",
isbn = "9783030003074",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Verlag",
pages = "338--349",
editor = "Hidehisa Akiyama and Oliver Obst and Claude Sammut and Flavio Tonidandel",
booktitle = "RoboCup 2017",
}