Evaluation of Training Strategies for Ambrosia Pollen Detection on Outdoor Images

Automatic pollen detection has recently gained significant research attention due to its importance for public health. Numerous computer vision methods have been proposed for this task, with the majority relying on two-stage object detection models applied to optical microscopy images. However, twostage approaches generally suffer from higher inference times, making them less suitable for real-time pollen monitoring tasks. Moreover, model performance on measurements from outdoor data samples remains largely unexplored because they introduce additional challenges, including visually complex backgrounds and the presence of other non-pollen particles such as dust, spores, and debris. In this work, we investigate the limits of the one-stage detection model YOLOv11 on Ambrosia pollen detection in images of outdoor and laboratory-prepared samples, acquired with the AeroTape device. We systematically evaluate training strategies, analyzing the effects of using either laboratory or outdoor images, their combinations, and also the use of data augmentation. The results show that incorporating augmented lab-prepared pollen images into training achieves the best mAP@50-95, highlighting the benefits of controlled laboratory data and one-stage detectors for improved generalization.