Research Article
Advancing Public Safety with Real-Time Life Jacket Detection and Demographic Profiling Using YOLOv8 and Age Classification
@ARTICLE{10.4108/airo.9785, author={Md Abu Yusuf and Nur Mohammad Chowdhury and Ponchanon Datta Rone and Partha Pratim Saha and Md Iqbal Hossan and Debabrata Sarkar and Ranjan Paul and Md Rana Hossain and Madhusodan Chakraborty}, title={Advancing Public Safety with Real-Time Life Jacket Detection and Demographic Profiling Using YOLOv8 and Age Classification}, journal={EAI Endorsed Transactions on AI and Robotics}, volume={4}, number={1}, publisher={EAI}, journal_a={AIRO}, year={2025}, month={9}, keywords={Life Jacket, Object detection, Age Classification, Yolov8, Safety Protocol}, doi={10.4108/airo.9785} }- Md Abu Yusuf
Nur Mohammad Chowdhury
Ponchanon Datta Rone
Partha Pratim Saha
Md Iqbal Hossan
Debabrata Sarkar
Ranjan Paul
Md Rana Hossain
Madhusodan Chakraborty
Year: 2025
Advancing Public Safety with Real-Time Life Jacket Detection and Demographic Profiling Using YOLOv8 and Age Classification
AIRO
EAI
DOI: 10.4108/airo.9785
Abstract
This study introduces a robust life jacket identification system that incorporates YOLOv8, FaceNet, and AgeNet for real-time safety surveillance in settings such as beaches, swimming pools, and maritime activities. The YOLOv8 model is applied for detecting life jackets, while FaceNet and AgeNet do face recognition and age classification, respectively, dividing persons into age groupings like "Teenager" or "Adult." The technology proficiently recognizes life jackets, detects faces, and evaluates risk by analyzing demographic factors, such as age, to generate safety alerts. The model attained a remarkable precision of 0.9934, a recall of 0.9818, and mAP50 of 0.9948, therefore validating its efficacy in recognizing life jackets and identifying individuals at risk. In high-risk aquatic situations, real-time life jacket detection, age classification, and facial recognition make the system resilient and reliable, improving public safety and risk management.
Copyright © 2025 Md Abu Yusuf et al., licensed to EAI. This is an open access article distributed under the terms of the CC BY-NCSA 4.0, which permits copying, redistributing, remixing, transformation, and building upon the material in any medium so long as the original work is properly cited.
