萨尔文信天翁的自动化检测：改进用于航空野生动物调查的深度学习工具
Automated Detection of Salvin's Albatrosses: Improving Deep Learning Tools for Aerial Wildlife Surveys
AI总结：
本研究评估通用鸟类检测模型BirdDetector对萨尔文信天翁繁殖种群的检测效果，发现微调结合强图像增强可显著提升检测准确率，为偏远环境的物种监测提供可行方案。
摘要：
深度学习和航空成像的最新进展已变革了野生动物监测方式，使研究人员能够以前所未有的规模调查野生动物种群。无人机（UAVs）提供了一种具有成本效益的高分辨率图像采集方式，尤其适用于监测种群密集的海鸟栖息地。在本研究中，我们评估了通用鸟类检测模型BirdDetector在估算新西兰邦蒂群岛上萨尔文信天翁（Thalassarche salvini）繁殖种群数量时的性能。我们利用无人机采集的图像，在零样本和微调两种设置下评估该模型的有效性，过程中结合了增强的推理技术和更强的数据增强方法。我们的研究结果表明，尽管零样本设置下应用该模型已能提供不错的基线性能，但通过目标域标注数据进行微调并结合更强的图像增强，可显著提升检测准确率。这些结果凸显了利用预训练深度学习模型在偏远、恶劣环境中开展特定物种监测的潜力。
英文摘要：
Recent advancements in deep learning and aerial imaging have transformed wildlife monitoring, enabling researchers to survey wildlife populations at unprecedented scales. Unmanned Aerial Vehicles (UAVs) provide a cost-effective means of capturing high-resolution imagery, particularly for monitoring densely populated seabird colonies. In this study, we assess the performance of a general-purpose avian detection model, BirdDetector, in estimating the breeding population of Salvin's albatross (Thalassarche salvini) on the Bounty Islands, New Zealand. Using drone-derived imagery, we evaluate the model's effectiveness in both zero-shot and fine-tuned settings, incorporating enhanced inference techniques and stronger augmentation methods. Our findings indicate that while applying the model in a zero-shot setting offers a strong baseline, fine-tuning with annotations from the target domain and stronger image augmentation leads to marked improvements in detection accuracy. These results highlight the potential of leveraging pre-trained deep-learning models for species-specific monitoring in remote and challenging environments.
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