Enhancing Fisheye Lens Object Detection with Generative Data Augmentation

Overhead fisheye cameras offer broad spatial coverage, making them suitable for surveillance in public spaces such as libraries. However, their severe image distortion and the scarcity of publicly available, privacy-compliant datasets hinder effective object detection. This study addresses these challenges through a dual strategy: augmenting data using text-to-image generative models and correcting fisheye distortion via calibrated intrinsic camera parameters. This approach enables robust training on enriched datasets while mitigating geometric artifacts. Experimental results show substantial performance gains over the YOLOv8 baseline, with [email protected] improving from 0.246 to 0.688 and [email protected]:0.95 from 0.122 to 0.518. Detection of small objects—such as beverages—improved markedly, with [email protected] rising from 0.507 to 0.795. Furthermore, combining synthetic and real data in training not only enhances generalization but also improves model robustness under challenging visual conditions.