Ingaleshwar, SubodhSubodhIngaleshwarThasharofi, FaridFaridThasharofiAvila Pava, MateoMateoAvila PavaVaishya, HarshitHarshitVaishyaTabak, YazanYazanTabakErnst, JürgenJürgenErnstPortas, RubenRubenPortasRast, WanjaWanjaRastMelzheimer, JoergJoergMelzheimerAschenborn, OrtwinOrtwinAschenbornGötz, TheresaTheresaGötzGöb, StephanStephanGöb2024-05-212024-05-212024https://publica.fraunhofer.de/handle/publica/46852510.5220/00123767000036362-s2.0-85190821897Accurate 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.enAnimal SpeciesApplied ConservationArtificial Intelligence (AI)Deep Neural NetworksEmbedded SystemsEnergy EfficientImage Classificationconference paper