Abstract
Rip currents present a significant safety risk to beach tourists and coastal communities, resulting in hundreds of annual drownings all over the world. A key contributing factor to this danger is the lack of awareness among beachgoers about recognizing and avoiding these rip currents. In response to this issue, we introduce RipFinder, a mobile app equipped with machine learning (ML) models trained to detect two types of rip currents. Users can leverage the app’s computer vision capabilities to use their phone’s camera to identify these hazardous rip currents in real time. The amorphous and ephemeral nature of rip currents makes it challenging to detect them with high accuracy using object detection models. To address this, we propose a client-server ML model-based computer vision system designed specifically to improve rip current detection accuracy. This novel approach enables the app to function with or without internet connectivity, proving particularly beneficial in regions without lifeguards or internet access. Additionally, the app serves as an educational resource, offering in-app information about rip currents. It also promotes citizen science involvement by encouraging users to contribute valuable information on detected rip currents. This paper presents the app’s overall design and discusses the challenges inherent to the rip current detection system.