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Article

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

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Institute for Environmental Solutions, LV-4126 Cēsis, Latvia
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iCV Lab, Institute of Technology, University of Tartu, 51009 Tartu, Estonia
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Forest Owners Consulting Center LCC, LV-4101 Cēsis, Latvia
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Latvian State Forest Research Institute “Silava”, LV-2169 Salaspils, Latvia
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PwC Advisory, 00180 Helsinki, Finland
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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)
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
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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

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