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

Topics: Applications: Image Processing and Artificial Vision, Pattern Recognition, Decision Making, Industrial and Real World Applications, Financial Applications, Neural Prostheses and Medical Applications, Neural Based Data Mining and Complex Information Process; Convolutional Neural Networks

Authors: Jurij Kuzmic and Günter Rudolph

Affiliation: Department of Computer Science, TU Dortmund University, Otto-Hahn-Str. 14, Dortmund, Germany

Keyword(s): Object Detection, Convolutional Neural Network (ConvNet), Autonomous Driving, Simulator in Unity 3D, Sim-to-Real Transfer, Training Data Generation, Computational Intelligence, Computer Vision, TensorFlow.

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 ti me 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. (More)

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Paper citation in several formats:
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) - NCTA; ISBN 978-989-758-534-0; ISSN 2184-3236, SciTePress, pages 302-309. DOI: 10.5220/0010653500003063

@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) - NCTA},
year={2021},
pages={302-309},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010653500003063},
isbn={978-989-758-534-0},
issn={2184-3236},
}

TY - CONF

JO - Proceedings of the 13th International Joint Conference on Computational Intelligence (IJCCI 2021) - NCTA
TI - Object Detection with TensorFlow on Hardware with Limited Resources for Low-power IoT Devices
SN - 978-989-758-534-0
IS - 2184-3236
AU - Kuzmic, J.
AU - Rudolph, G.
PY - 2021
SP - 302
EP - 309
DO - 10.5220/0010653500003063
PB - SciTePress