評估深度學習模型在非洲野生動物影像分類中的應用：從DenseNet到視覺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

非洲野生動物種群面臨嚴重威脅，過去五十年間脊椎動物數量減少了65%以上。為應對這一挑戰，基於深度學習的圖像分類技術已成為生物多樣性監測和保護的有力工具。本文對用於自動分類非洲野生動物圖像的深度學習模型進行了比較研究，重點探討了特徵提取器凍結情況下的遷移學習。利用包含水牛、大象、犀牛和斑馬四種物種的公開數據集，我們評估了DenseNet-201、ResNet-152、EfficientNet-B4和Vision Transformer ViT-H/14的性能。其中，DenseNet-201在卷積網絡中表現最佳（準確率67%），而ViT-H/14則達到了最高的總體準確率（99%），但其計算成本顯著更高，引發了部署方面的顧慮。我們的實驗揭示了準確性、資源需求和可部署性之間的權衡。性能最佳的CNN模型（DenseNet-201）已被集成到Hugging Face Gradio Space中，用於實地實時應用，展示了在保護環境中部署輕量級模型的可行性。本研究通過提供模型選擇、數據集準備以及深度學習工具在野生動物保護中的負責任部署的實踐見解，為紮根非洲的AI研究做出了貢獻。

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.