Abstract
Wildlife populations in Africa face severe threats, with vertebrate numbers declining by over 65% in the past five decades. In response, image classification using deep learning has emerged as a promising tool for biodiversity monitoring and conservation. This paper presents a comparative study of deep learning models for automatically classifying African wildlife images, focusing on transfer learning with frozen feature extractors. Using a public dataset of four species: buffalo, elephant, rhinoceros, and zebra; we evaluate the performance of DenseNet-201, ResNet-152, EfficientNet-B4, and Vision Transformer ViT-H/14. DenseNet-201 achieved the best performance among convolutional networks (67% accuracy), while ViT-H/14 achieved the highest overall accuracy (99%), but with significantly higher computational cost, raising deployment concerns. Our experiments highlight the trade-offs between accuracy, resource requirements, and deployability. The best-performing CNN (DenseNet-201) was integrated into a Hugging Face Gradio Space for real-time field use, demonstrating the feasibility of deploying lightweight models in conservation settings. This work contributes to African-grounded AI research by offering practical insights into model selection, dataset preparation, and responsible deployment of deep learning tools for wildlife conservation.
Abstract (translated)
非洲野生动物种群正面临严重威胁，过去五十年间脊椎动物的数量下降了超过65%。为应对这一挑战，基于深度学习的图像分类技术已成为生物多样性监测和保护的一项有前景的工具。本文通过使用一个包含四种物种（水牛、大象、犀牛和斑马）的公开数据集，对比研究了几种深度学习模型在自动分类非洲野生动物图像方面的性能表现，重点关注冻结特征提取器的迁移学习方法。 实验中评估了DenseNet-201、ResNet-152、EfficientNet-B4以及Vision Transformer ViT-H/14四种模型的表现。其中，DenseNet-201在卷积神经网络（CNN）中的表现最佳，达到了67%的准确率；而ViT-H/14则实现了最高的总体准确率99%，但其计算成本显著更高，引发了部署方面的担忧。我们的实验突出了精度、资源需求与可部署性之间的权衡。 性能最好的卷积神经网络模型DenseNet-201被集成到了Hugging Face Gradio Space中，以支持实时现场应用，展示了在保护环境中部署轻量级模型的可行性。这项工作通过提供有关模型选择、数据集准备以及深度学习工具负责任部署的实际见解，为非洲本土人工智能研究做出了贡献。
URL
https://arxiv.org/abs/2507.21364
