This paper presents a machine learning approach for the automatic identification of rip currents with breaking waves. Rip currents are dangerous fast moving currents of water that result in many deaths by sweeping people out to sea. Most people do not know how to recognize rip currents in order to avoid them. Furthermore, efforts to forecast rip currents are hindered by lack of observations to help train and validate hazard models. The presence of web cams and smart phones have made video and still imagery of the coast ubiquitous and provide a potential source of rip current observations. These same devices could aid public awareness of the presence of rip currents. What is lacking is a method to detect the presence or absence of rip currents from coastal imagery. This paper provides expert labeled training and test data for rip currents. We use Faster R-CNN and a custom temporal aggregation stage to make detections from still images or videos with higher measured accuracy than both humans and other methods of rip current detection previously reported in the literature.

本文提出了一种机器学习方法，用于自动识别具有破波的裂隙电流。激流是危险的快速流动的水流，通过将人们赶出大海导致许多人死亡。大多数人不知道如何识别裂隙电流以避免它们。此外，由于缺乏用于帮助训练和验证危害模型的观测资料，阻碍了预测裂谷电流的努力。网络摄像头和智能手机的存在使得无处不在的视频和静止图像成为现实，并提供了翻录当前观测的潜在来源。这些相同的设备可以帮助公众意识到裂隙电流的存在。缺乏一种从海岸图像中检测是否存在裂谷电流的方法。本文提供了带有标签的专家针对裂隙电流的培训和测试数据。