Reducing rip current drowning: An improved residual based lightweight deep architecture for rip detection

Abstract

Rip Currents are contributing around 25 fatal drownings each year in Australia. Previous research has indicated that most of beachgoers cannot correctly identify a rip current, leaving them at risk of experiencing a drowning incident. Automated detection of rip currents could help to reduce drownings and assist lifeguards in supervision planning; however, varying beach conditions have made this challenging. This work presents the effectiveness of an improved lightweight framework for detecting rip currents: RipDet+11This work is an extended work of our earlier work accepted in IJCNN (Rashid et al., 2020) and ICONIP (Rashid et al., 2021). This work presents a deep learning framework aided with residual mapping to predict based on a limited number of samples. Unlike our previous work, we present lightweight framework aided with residual mapping to predict based on a limited number of samples., aided with residual mapping to boost the generalization performance. We have used Yolo-V3 architecture to build RipDet+ framework and utilize pretrained weight by fully exploiting the detection training set from some base classes which in result quickly adapt the detection prediction to the available rip data. Extensive experiments are reported which show the effectiveness of RipDet+ architecture in achieving a detection accuracy of 98.55%, which is significantly greater compared to other state-of-the-art methods for Rip currents detection.