Bird detection methods compared for western bluebird monitoring
Towards benchmarking Western Bluebird detection in the wild
Computer Vision and Pattern Recognition
Summary
Detecting small birds like Western bluebirds in natural settings is hard because they are tiny in big scenes with complex backgrounds and changing light. The authors collected over 6,000 high-resolution images to create a new dataset for testing bird detection and segmentation methods. They tested different computer vision models and found that training models specifically on this task works better than using general models without training. Their analysis also showed that bird size alone doesn't explain detection failures, but factors like brightness and clutter also matter.
What this means in practice
- •For wildlife monitoring teams: Use the benchmark dataset and evaluated detectors to improve automated tracking of Western bluebirds in diverse natural environments.
- •For ecological survey teams: Incorporate fine-tuned open-vocabulary models like YOLO-World for accurate bird detection in field surveys with limited labeled data.
Authors
Estela Monserrat Arriaga Santana, Julian Rosas Scull, Ibeth P. Alarcón, Bibiana Montoya, Aylin Sosa Mejía, Hugo Jair Escalante
Abstract
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. Progress is further limited by the scarcity of large-scale, realistic datasets, which are essential for understanding behavioral patterns. 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. The dataset features high-resolution (4K) in-the-wild images in which birds occupy only a small fraction of the image. We evaluated supervised detectors, open-vocabulary models under zero-shot and fine-tuned settings, and segmentation approaches. Supervised detectors remain the most reliable overall, with Faster R-CNN achieving the highest detection mAP and RT-DETR offering the best precision-recall trade-off. Open-vocabulary models perform poorly in zero-shot settings; however, fine-tuning substantially improves their performance, with YOLO-World becoming competitive with supervised methods and achieving the highest precision, F1-score, and mAP@0.5. 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. A diagnostic analysis further shows that failures are not explained by object size alone, but by a combination of apparent scale, brightness, contrast, clutter, blur, crowding, and recording-session variation. 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.