Towards benchmarking Western Bluebird detection in the wild

Key points

• Bird monitoring in natural environments is challenging due to the small size of some species of birds relative to the scene, background clutter, variability in illumination, and the observers' viewpoint.1
• The dataset features high-resolution (4K) in-the-wild images in which birds occupy only a small fraction of the image.1
• For segmentation, supervised methods significantly outperform Grounded-SAM and SAM 3: Mask R-CNN achieves the highest mask mAP, while YOLOv8-Seg provides the best precision and fastest inference.1
• Overall, our findings highlight the difficulty of zero-shot bird detection in cluttered ecological scenes and underscore the importance of domain adaptation in small-object settings.1

Sources (1)

• [1]Towards benchmarking Western Bluebird detection in the wild
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 05:56 AM

  To address this gap, we introduce a new benchmark dataset for the detection and segmentation of Western bluebirds (Sialia Mexicana), comprising over 6,000 labeled images from 41 recording sessions.

  Bird monitoring in natural environments is challenging due to the small size of some species of birds relative to the scene, background clutter, variability in illumination, and the observers' viewpoint.

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