RareSpot: Spotting Small and Rare Wildlife in Aerial Imagery with Multi-Scale Consistency and Context-Aware Augmentation

• URL: http://arxiv.org/abs/2506.19087v1
• Date: Mon, 23 Jun 2025 20:03:43 GMT
• Title: RareSpot: Spotting Small and Rare Wildlife in Aerial Imagery with Multi-Scale Consistency and Context-Aware Augmentation
• Authors: Bowen Zhang, Jesse T. Boulerice, Nikhil Kuniyil, Charvi Mendiratta, Satish Kumar, Hila Shamon, B. S. Manjunath,
• Abstract summary: RareSpot is a robust detection framework integrating multi-scale consistency learning and context-aware augmentation.<n>Our method achieves state-of-the-art performance, improving detection accuracy by over 35% compared to baseline methods.
• Score: 6.756718879272925
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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