Preprint Open access
Bayesian Data Augmentation for DNN Retraining with Binomial Outcomes in Vision-Based UAV Landing
In GPS-denied or cluttered urban environments, vision-based landing is essential for reliable UAV missions. Real-world landing sites are often unstructured and highly variable, requiring strong generalization by the perception system. Deep Neural Networks (DNNs) trained with synthetic data augmentation offer a scalable …