Exploring Advance Approaches for Drowning Detection: A Review

Abstract

This research mainly explores the existing drowning detection methodologies, focusing primarily on the roles carried out by Machine Learning (ML) and Deep Learning (DL) algorithms. It directly emphasizes the dominance of ML in the analysis of raw sensor data along with the contribution of DL to computer vision, which also reveals the present gap between advanced vision along detection models. The holistic approaches are mainly advocated, potentially integrating wearable devices, vision-based systems, as well as sensors while also balancing their performance, regional applicability, and cost-effectiveness. The challenges aligned to enabling real-time detection and reduced latency are important for the time-sensitive realm of incidents related to drowning. Future directions necessarily include the exploration of advanced forms of vision models and segmentation techniques for innovative detection algorithms. Integration of wearable devices and sensors with the inclusion of vision-based systems is important for the required adaptability. The upcoming proposal aims to integrate robotics into rescue operations bringing revolution to response times. The study also covers the requirement for a compact combination of ML and DL algorithms and a generalized solution for the equilibrium maintenance between cost-effectiveness, sophistication, and regional applicability.