RipScout: Realtime ML-Assisted Rip Current Detection and Automated Data Collection Using UAVs

Abstract

This article presents RipScout, a system for realtime rip current detection and data collection using drones equipped with machine learning (ML). No internet connection is required. RipScout achieves realtime performance by using lightweight ML models that fit the constraints of limited mobile computing resources with the drone controller. We compared different ML models trained to detect either one or two types of rip currents. The best model was then selected for RipScout. The system was evaluated with three ML models, with the EfficientDet D2 model achieving the highest accuracy of 93.1% for multiclass detection while maintaining realtime processing at an average speed of 17 frames per second. When a rip is detected along a flight path, the drone hovers in place and collects a video clip of a predefined length, followed by circling around the detected rip using prespecified radii and heights to collect video samples from different vantage points and elevations. An important benefit of RipScout is that the collection of rip current data can be performed by drone operators who are not familiar with rip currents. We conducted field tests and found that the proposed system allows data to be collected four times faster than without it while improving accuracy. As a by-product of the field experiments, we also provide a new rip current dataset. Such a multiviewpoint dataset can be used to improve rip current detection, especially from lower elevations and different orientations.