Rip Current Segmentation: A Novel Benchmark and YOLOv8 Baseline Results
This work addresses a safety-critical issue for beachgoers and coastal management by providing a baseline for rip current segmentation, though it is incremental as it applies existing methods to a new domain.
The paper tackles the problem of automatically detecting rip currents, a leading cause of beach accidents, by introducing a new dataset and training YOLOv8 models for instance segmentation, achieving an mAP50 of 88.94% on validation and 81.21% on test data.
Rip currents are the leading cause of fatal accidents and injuries on many beaches worldwide, emphasizing the importance of automatically detecting these hazardous surface water currents. In this paper, we address a novel task: rip current instance segmentation. We introduce a comprehensive dataset containing $2,466$ images with newly created polygonal annotations for instance segmentation, used for training and validation. Additionally, we present a novel dataset comprising $17$ drone videos (comprising about $24K$ frames) captured at $30 FPS$, annotated with both polygons for instance segmentation and bounding boxes for object detection, employed for testing purposes. We train various versions of YOLOv8 for instance segmentation on static images and assess their performance on the test dataset (videos). The best results were achieved by the YOLOv8-nano model (runnable on a portable device), with an mAP50 of $88.94%$ on the validation dataset and $81.21%$ macro average on the test dataset. The results provide a baseline for future research in rip current segmentation. Our work contributes to the existing literature by introducing a detailed, annotated dataset, and training a deep learning model for instance segmentation of rip currents. The code, training details and the annotated dataset are made publicly available at https://github.com/Irikos/rip_currents.