Next Article in Journal
Non-Markovian Quantum Dynamics in a Squeezed Reservoir
Previous Article in Journal
Nonparametric Causal Structure Learning in High Dimensions
 
 
Search for Articles:
Title / Keyword
Author / Affiliation
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Entropy
Volume 24
Issue 3
10.3390/e24030353
entropy-logo
Submit to this Journal Review for this Journal Edit a Special Issue
► Article Menu

Article Menu

share announcement question_answer thumb_up
...
textsms
...
Article

Ungulate Detection and Species Classification from Camera Trap Images Using RetinaNet and Faster R-CNN

by
Alekss Vecvanags
1,
Kadir Aktas
2,
Ilja Pavlovs
2,
Egils Avots
2,3,*,
Jevgenijs Filipovs
1,
Agris Brauns
1,
Gundega Done
4,
Dainis Jakovels
1 and
Gholamreza Anbarjafari
1,2,5,6
1
Institute for Environmental Solutions, LV-4126 Cēsis, Latvia
2
iCV Lab, Institute of Technology, University of Tartu, 51009 Tartu, Estonia
3
Forest Owners Consulting Center LCC, LV-4101 Cēsis, Latvia
4
Latvian State Forest Research Institute “Silava”, LV-2169 Salaspils, Latvia
5
PwC Advisory, 00180 Helsinki, Finland
6
Faculty of Engineering, Hasan Kalyoncu University, Gaziantep 27410, Turkey
*
Author to whom correspondence should be addressed.
Academic Editor: Salim Lahmiri
Entropy 2022, 24(3), 353; https://doi.org/10.3390/e24030353
Received: 11 February 2022 / Revised: 23 February 2022 / Accepted: 25 February 2022 / Published: 28 February 2022
(This article belongs to the Section Multidisciplinary Applications)
View Full-Text Download PDF
Browse Figures

Abstract

Changes in the ungulate population density in the wild has impacts on both the wildlife and human society. In order to control the ungulate population movement, monitoring systems such as camera trap networks have been implemented in a non-invasive setup. However, such systems produce a large number of images as the output, hence making it very resource consuming to manually detect the animals. In this paper, we present a new dataset of wild ungulates which was collected in Latvia. Moreover, we demonstrate two methods, which use RetinaNet and Faster R-CNN as backbones, respectively, to detect the animals in the images. We discuss the optimization of training and impact of data augmentation on the performance. Finally, we show the result of aforementioned tune networks over the real world data collected in Latvia. View Full-Text
Keywords: RetinaNet; Faster R-CNN; animal detection; camera traps; ungulates RetinaNet; Faster R-CNN; animal detection; camera traps; ungulates
►▼ Show Figures

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
SciFeed

Share and Cite

MDPI and ACS Style

Vecvanags, A.; Aktas, K.; Pavlovs, I.; Avots, E.; Filipovs, J.; Brauns, A.; Done, G.; Jakovels, D.; Anbarjafari, G. Ungulate Detection and Species Classification from Camera Trap Images Using RetinaNet and Faster R-CNN. Entropy 2022, 24, 353. https://doi.org/10.3390/e24030353

AMA Style

Vecvanags A, Aktas K, Pavlovs I, Avots E, Filipovs J, Brauns A, Done G, Jakovels D, Anbarjafari G. Ungulate Detection and Species Classification from Camera Trap Images Using RetinaNet and Faster R-CNN. Entropy. 2022; 24(3):353. https://doi.org/10.3390/e24030353

Chicago/Turabian Style

Vecvanags, Alekss, Kadir Aktas, Ilja Pavlovs, Egils Avots, Jevgenijs Filipovs, Agris Brauns, Gundega Done, Dainis Jakovels, and Gholamreza Anbarjafari. 2022. "Ungulate Detection and Species Classification from Camera Trap Images Using RetinaNet and Faster R-CNN" Entropy 24, no. 3: 353. https://doi.org/10.3390/e24030353

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Metrics

Article Access Map by Country/Region

1
Back to TopTop