Authors:
Subodh Ingaleshwar
1
;
Farid Thasharofi
1
;
Mateo Pava
1
;
Harshit Vaishya
1
;
Yazan Tabak
1
;
Juergen Ernst
1
;
Ruben Portas
2
;
Wanja Rast
2
;
Joerg Melzheimer
2
;
Ortwin Aschenborn
2
;
Theresa Goetz
1
;
3
and
Stephan Goeb
1
Affiliations:
1
Fraunhofer- Institute for Integrated Circuits IIS, Erlangen, Germany
;
2
Leibniz Institute for Zoo and Wildlife Research, Berlin, Germany
;
3
Department of Industrial Engineering and Health, University of Applied Sciences Amberg-Weiden, Germany
Keyword(s):
Artificial Intelligence (AI), Animal Species, Applied Conservation, Deep Neural Networks, Embedded Systems, Energy Efficient, Image Classification.
Abstract:
Accurate and timely recognition of wild animal species is very important for various management processes in nature conservation. In this article, we propose an energy-efficient way of classifying animal species in real-time. Specifically, we present an image classification system on a low power Edge-AI device, which embeds a deep neural network (DNN) in a microcontroller that accurately recognizes different animal species. We evaluate the performance of the proposed system using a real-world dataset collected via a small handheld camera from remote conservation regions of Africa. We implement different DNN models and deploy them on the embedded device to perform real-time classification of animal species. The experimental results show that the proposed animal species classification system is able to obtain a remarkable accuracy of 84.30% with an energy efficiency of 0.885 𝑚J on an edge device. This work provides a new perspective toward low power, energy-efficient, fast and accurat
e edge-AI technology to help in inhibiting wildlife-human conflicts.
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