This study evaluates the performance of various deep learning models, specifically DenseNet, ResNet, VGGNet, and YOLOv8, for wildlife species classification on a custom dataset. The dataset comprises 575 images of 23 endangered species sourced from reputable online repositories. The study utilizes transfer learning to fine-tune pre-trained models on the dataset, focusing on reducing training time and enhancing classification accuracy. The results demonstrate that YOLOv8 outperforms other models, achieving a training accuracy of 97.39% and a validation F1-score of 96.50%. These findings suggest that YOLOv8, with its advanced architecture and efficient feature extraction capabilities, holds great promise for automating wildlife monitoring and conservation efforts.

翻译：本研究评估了多种深度学习模型（特别是DenseNet、ResNet、VGGNet和YOLOv8）在自定义数据集上进行野生动物物种分类的性能。该数据集包含从权威在线资源库获取的23个濒危物种共575张图像。研究采用迁移学习方法对预训练模型进行数据集微调，重点关注减少训练时间与提升分类准确率。结果表明，YOLOv8凭借其先进的架构和高效的特征提取能力，以97.39%的训练准确率和96.50%的验证F1分数优于其他模型。这些发现表明YOLOv8在自动化野生动物监测与保护工作中具有巨大应用潜力。