用无人机影像检测信天翁，优化模型提升野外监测精度。
Automated Detection of Salvin's Albatrosses: Improving Deep Learning Tools for Aerial Wildlife Surveys
- 用预训练模型在无标注数据下直接检测，快速建立基准
- 针对目标区域微调并增强图像处理，检测准确率显著提升
- 适合在偏远地区开展物种监测的研究者参考
深度学习与航拍技术的进步推动了野生动物监测的规模化发展。无人机可低成本获取高分辨率影像，尤其适用于密集分布的海鸟巢群调查。本研究评估通用鸟类检测模型 BirdDetector 在新西兰博奈特群岛萨尔文信天翁（Thalassarche salvini）繁殖种群估算中的表现。基于无人机影像，我们在零样本和微调两种设置下评估模型效果，结合改进的推理方法与更强的数据增强策略。结果表明，尽管零样本设置已提供良好基线，但使用目标域标注数据进行微调并采用更强增强方法，能显著提升检测准确性。研究凸显了预训练深度学习模型在偏远、复杂环境中实现物种专项监测的巨大潜力。
原文摘要 · Abstract (English)
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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