Publication Details

Vehicle Fine-grained Recognition Based on Convolutional Neural Networks for Real-world Applications

ŠPAŇHEL, J.; SOCHOR, J.; MAKAROV, A. Vehicle Fine-grained Recognition Based on Convolutional Neural Networks for Real-world Applications. In 2018 14th Symposium on Neural Networks and Applications (NEUREL). Belgrade: IEEE Signal Processing Society, 2018. p. 1-5. ISBN: 978-1-5386-6974-7.

Czech title

Rozpoznání modelů vozidel založené na neuronových sítí pro aplikaci v reálných podmínkách

Type

conference paper

Language

English

Authors

Špaňhel Jakub, Ing., Ph.D. (DCGM)
Sochor Jakub, Ing., Ph.D.
MAKAROV, A.

Keywords

convolutional neural networks, similar vehicle type search, vehicle fine-grained recognition, vehicle reidentification

Abstract

We explore the implementation of vehicle
fine-grained type and color recognition based on neural
networks in a real-world application. We suggest changes to
the previously published method with respect to capabilities
of low-powered devices, such as Nvidia Jetson. Experimental
evaluation shows that the accuracy of MobileNet net slightly
decreases compared to ResNet-50 from 89.55% to 86.13%
while inference is 2.4× faster on Jetson.

Published

2018

Pages

1–5

Proceedings

2018 14th Symposium on Neural Networks and Applications (NEUREL)

Conference

2018 14th Symposium on Neural Networks and Applications (NEUREL), SAVA Center Milentija Popovića 9 11070, Belgrade, Serbia, RS

ISBN

978-1-5386-6974-7

Publisher

IEEE Signal Processing Society

Place

Belgrade

DOI

10.1109/NEUREL.2018.8587012

UT WoS

000457745100031

EID Scopus

2-s2.0-85060947815

BibTeX

@inproceedings{BUT155107,
  author="ŠPAŇHEL, J. and SOCHOR, J. and MAKAROV, A.",
  title="Vehicle Fine-grained Recognition Based on Convolutional Neural Networks for Real-world Applications",
  booktitle="2018 14th Symposium on Neural Networks and Applications (NEUREL)",
  year="2018",
  pages="1--5",
  publisher="IEEE Signal Processing Society",
  address="Belgrade",
  doi="10.1109/NEUREL.2018.8587012",
  isbn="978-1-5386-6974-7"
}