评估深度学习模型在非洲野生动物图像分类中的应用：从DenseNet到视觉Transformer

Evaluating Deep Learning Models for African Wildlife Image Classification: From DenseNet to Vision Transformers

July 28, 2025

作者: Lukman Jibril Aliyu, Umar Sani Muhammad, Bilqisu Ismail, Nasiru Muhammad, Almustapha A Wakili, Seid Muhie Yimam, Shamsuddeen Hassan Muhammad, Mustapha Abdullahi

cs.AI

摘要

非洲野生动物种群面临严峻威胁，过去五十年间脊椎动物数量减少了超过65%。对此，基于深度学习的图像分类技术已成为生物多样性监测与保护的有力工具。本文针对非洲野生动物图像的自动分类，开展了一项深度学习模型的对比研究，重点探讨了冻结特征提取器的迁移学习方法。利用包含水牛、大象、犀牛和斑马四种物种的公开数据集，我们评估了DenseNet-201、ResNet-152、EfficientNet-B4及Vision Transformer ViT-H/14的性能。其中，DenseNet-201在卷积神经网络中表现最佳（准确率67%），而ViT-H/14则达到了最高的总体准确率（99%），但其显著更高的计算成本引发了部署方面的顾虑。我们的实验揭示了准确率、资源需求与可部署性之间的权衡关系。表现最优的CNN模型（DenseNet-201）已集成至Hugging Face Gradio Space，实现了实时野外应用，展示了在保护场景中部署轻量化模型的可行性。本研究通过提供模型选择、数据集准备及负责任地部署深度学习工具于野生动物保护的实际见解，为扎根非洲的AI研究做出了贡献。

English

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.