Flow-Based Rip Current Detection and Visualization

Abstract

Rip currents are like rivers of fast-moving water that can quickly carry the unwary out to sea. They are not easy to recognize, especially to the untrained observer. Other than in-situ current measurements, there exists a number of methods that analyzes images and videos to detect rip currents. Most of these techniques base their detection on the appearance of rip currents such as foamy pattern, discoloration of water, and locations of breaking waves. Leading methods use either image processing or machine learning of images and/or video input. In this paper, we analyze the behavior of water movement rather than simply its appearance to detect rip currents. Specifically, we investigated several flow visualization methods and tune them to detect rip currents. Based on our study, we recommend two methods that allowed us to detect rip currents where other methods have failed. And because the methods originated as visualization techniques, any presence of rip currents are automatically highlighted. We also evaluated these two methods against previously annotated results by rip current experts, and found that our detections were sufficiently sensitive that some expert annotations were relabeled.

RipViz: Finding Rip Currents by Learning Pathline Behavior

“…Object Detector [11] yes 0.43 Timelines [30] no 0.72 Filtered color map [30] yes 0. parts of the beach or miss parts of the rip especially with long elongated rips. We also noticed that the information such as the curvature of the rip current cannot be gleaned from bounding boxes alone.…”

“…They grouped vectors based on the direction and magnitude to visualize the rip current. Mori et al [30] used a similar approach to group vectors and improved the visualization of the rip current by mapping direction to color and magnitude to hue. In the same work, Mori et al [30] also used timelines to visualize rip currents.…”

“…The second approach relies on flow analysis, where the flow behavior of ocean waves is analyzed to detect rip currents. Direction-based clustering of flow vectors [30,34] and timelines [30] placed parallel to the beach are used in this approach. While able to detect weaker rip currents or those where the appearance is not obvious, timelines require user input to specify their initial placement.…”

Rip Current: A Potential Hazard Zones Detection in Saint Martin’s Island using Machine Learning Approach

“…They use Faster R-CNN (F-RCNN) and a custom temporally agglomeration step to identify rip currents from images or videos, with significantly higher verified correctness than humans and other previously described approaches. Mori et al (2022) [2] offered two strategies that enabled them to identify rip currents while all the other methods flopped. Also, since these methods were made to help people see things, the fact that rip currents exist is naturally emphasized.…”

YOLO-Rip: A modified lightweight network for Rip currents detection

“…Because of these limitations, the applicability of the above methods for both public warning and model validation needed to be improved. Mori et al (2022) investigated the use of optical flow analysis techniques to detect rip currents in videos. By improving the flow field visualization method, a substantial improvement was achieved both in detecting fine rip currents and in the clarity of the visualization.…”