RipDet: A Fast and Lightweight Deep Neural Network for Rip Currents Detection

Abstract

Rip currents are intense and localized seaward-flowing water from shore through the surf zone, cutting through the lines of breaking ocean waves. It generally pulls a person out to sea very fast, with speeds up to eight feet per second. It is one of the most important reasons for an average of 21 confirmed fatalities each year. Fully automated Rip current detection using state-of-the-art deep learning techniques can help to monitor the coast. However, there are several challenges involved, like dealing with a small training sample size and unavailability of pretrained models in the domain of Rip current. In this work, we address these issues and propose a novel fast and lightweight RipDet framework for an efficient and accurate automated Rip current detection. The proposed model is aided by careful data augmentation, fine-tuning, and a custom learning rate schedule that helps the model adapt to Rip currents' distribution with the low number of training samples. Extensive experiments on benchmark data show that the proposed model outperforms other state-of-the-art methods for Rip current detection with mean average precision (mAP) score of 98.131 %.