Rip Current Data augmentation

Rip currents are a common beach hazard, and their visual characteristics are inconsistent and highly variable. Automated detection approaches are limited by lacking large scale annotated datasets. We use generative adversarial networks to synthesize rip currents based on input object detection labels, enriching the dataset to improve detector performance without requiring re-annotation of the generated data.