Object Detection with TensorFlow on Hardware with Limited Resources for Low-power IoT Devices

Jurij Kuzmic, Günter Rudolph

2021

Abstract

This paper presents several models for individual object detection with TensorFlow in a 2D image with Convolution Neural Networks (ConvNet). Here, we focus on an approach for hardware with limited resources in the field of the Internet of Things (IoT). Additionally, our selected models are trained and evaluated using image data from a Unity 3D simulator as well as real data from model making area. In the beginning, related work of this paper is discussed. As well known, a large amount of annotated training data for supervised learning of ConvNet is required. These annotated training data are automatically generated with the Unity 3D environment. The procedure for generating annotated training data is also presented in this paper. Furthermore, the different object detection models are compared to find a better and faster system for object detection on hardware with limited resources for low-power IoT devices. Through the experiments described in this paper the comparison of the run time of the trained models is presented. Also, a transfer learning approach in object detection is carried out in this paper. Finally, future research and work in this area are discussed.

Paper Citation

in Harvard Style

Kuzmic J. and Rudolph G. (2021). Object Detection with TensorFlow on Hardware with Limited Resources for Low-power IoT Devices. In Proceedings of the 13th International Joint Conference on Computational Intelligence (IJCCI 2021) - Volume 1: NCTA; ISBN 978-989-758-534-0, SciTePress, pages 302-309. DOI: 10.5220/0010653500003063

in Bibtex Style

@conference{ncta21,
author={Jurij Kuzmic and Günter Rudolph},
title={Object Detection with TensorFlow on Hardware with Limited Resources for Low-power IoT Devices},
booktitle={Proceedings of the 13th International Joint Conference on Computational Intelligence (IJCCI 2021) - Volume 1: NCTA},
year={2021},
pages={302-309},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010653500003063},
isbn={978-989-758-534-0},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computational Intelligence (IJCCI 2021) - Volume 1: NCTA
TI - Object Detection with TensorFlow on Hardware with Limited Resources for Low-power IoT Devices
SN - 978-989-758-534-0
AU - Kuzmic J.
AU - Rudolph G.
PY - 2021
SP - 302
EP - 309
DO - 10.5220/0010653500003063
PB - SciTePress