A Deep Learning-Integrated Framework for Operational Rip Current Warning

Rip currents pose a serious maritime safety hazard, as they can quickly carry swimmers away from the shore, often leading to drownings caused by panic. Traditional beach flags and signs often fall short due to the complexities involved in issuing real-time warnings. In this study, a framework for rip current warning based on deep learning was introduced and evaluated. The framework consists of automated object detection, adaptive time-averaged image generation, and expert validation protocols. The YOLOv4 deep learning model was trained and evaluated using three distinct datasets derived from two primary sources: a publicly available dataset sourced from peer-reviewed literature and a custom-built dataset compiled for this study. The results indicate that the models performed effectively, even under challenging environmental conditions, such as fluctuating lighting, camera motion, and varying wave dynamics. A significant novelty of this framework is the adaptable time-averaging feature, which filters out potential false positives generated by the deep learning model. This feature also allows for rapid detection in emergency situations while identifying persistent rip channel patterns for long-term risk assessments. Furthermore, the rip current alerts are not solely activated by automated results. Rather, they are contingent on the verification of dangerous conditions by trained personnel, such as lifeguards or beach management officers. The results of implementing a pilot version of this framework demonstrate its practical viability for real-world deployment, marking a significant advancement in transitioning deep learning-based rip current detection from controlled environments to practical, real-time warning systems.