Wildlife Species Classification on the Edge: A Deep Learning Perspective

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 accurate edge-AI technology to help in inhibiting wildlife-human conflicts.