RareSpot：利用多尺度一致性与上下文感知增强在航空影像中识别小型稀有野生动物
RareSpot: Spotting Small and Rare Wildlife in Aerial Imagery with Multi-Scale Consistency and Context-Aware Augmentation
AI总结：
针对航空影像中小型稀有野生动物检测难题，提出RareSpot框架，利用多尺度一致性与上下文感知增强，在草原犬鼠基准上精度提升超35%，并泛化至其他数据集。
摘要：
在航空影像中自动检测小型稀有野生动物对于有效保护至关重要，但仍是一项重大的技术挑战。草原犬鼠是这一问题的典型代表：它们作为关键物种的生态重要性与其难以捉摸的存在形成鲜明对比——体型小、分布稀疏、视觉特征细微——这削弱了现有的检测方法。为应对这些挑战，我们提出了RareSpot，一个鲁棒的检测框架，整合了多尺度一致性学习和上下文感知增强。我们的多尺度一致性方法利用特征金字塔间的结构化对齐，增强细粒度对象表示并缓解与尺度相关的特征损失。互补地，上下文感知增强通过将难以检测的样本嵌入真实环境背景中，策略性地合成具有挑战性的训练实例，显著提升模型的精确率和召回率。在专家标注的草原犬鼠无人机影像基准上评估，我们的方法达到了最先进的性能，相比基线方法检测精度提升了超过35%。重要的是，它还能有效泛化到额外的野生动物数据集，展示了广泛的适用性。RareSpot基准和方法不仅支持关键的生态监测，也为在复杂航空场景中检测小型稀有物种建立了新基础。
英文摘要：
Automated detection of small and rare wildlife in aerial imagery is crucial for effective conservation, yet remains a significant technical challenge. Prairie dogs exemplify this issue: their ecological importance as keystone species contrasts sharply with their elusive presence--marked by small size, sparse distribution, and subtle visual features--which undermines existing detection approaches. To address these challenges, we propose RareSpot, a robust detection framework integrating multi-scale consistency learning and context-aware augmentation. Our multi-scale consistency approach leverages structured alignment across feature pyramids, enhancing fine-grained object representation and mitigating scale-related feature loss. Complementarily, context-aware augmentation strategically synthesizes challenging training instances by embedding difficult-to-detect samples into realistic environmental contexts, significantly boosting model precision and recall. Evaluated on an expert-annotated prairie dog drone imagery benchmark, our method achieves state-of-the-art performance, improving detection accuracy by over 35% compared to baseline methods. Importantly, it generalizes effectively across additional wildlife datasets, demonstrating broad applicability. The RareSpot benchmark and approach not only support critical ecological monitoring but also establish a new foundation for detecting small, rare species in complex aerial scenes.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
