András Biricz, ELTE (2024.11.01 - 2025.03.31)

Abstract: In this study, we developed a highly automated, AI-assisted pipeline for airborne pollen detection using microscopy images digitized from Hirst-type samplers. Our approach significantly reduces the need for manual annotation by using open-world object detection and transformer-based models for training dataset generation. The project used multi-regional datasets from Hungary, Sweden, France, and the Mediterranean region to train and evaluate models under both controlled and real-world conditions. Deep learning models were trained and benchmarked on GPU infrastructure, enabling efficient training of transformer-based object detection models. The final system demonstrated relatively high performance in cross-regional generalization and real-world applicability, contributing to modern, scalable solutions for allergen monitoring and ecological analysis.