Advancing Public Safety with Real-Time Life Jacket Detection and Demographic Profiling Using YOLOv8 and Age Classification

Md Abu Yusuf; Nur Mohammad Chowdhury; Ponchanon Datta Rone; Partha Pratim Saha; Md Iqbal Hossan; Debabrata Sarkar; Ranjan Paul; Md Rana Hossain; Madhusodan Chakraborty

doi:10.4108/airo.9785

Authors

Md Abu Yusuf Maharishi International University
Nur Mohammad Chowdhury Louisiana Tech University
Ponchanon Datta Rone Maharishi International University
Partha Pratim Saha Maharishi International University
Md Iqbal Hossan Maharishi International University
Debabrata Sarkar Chittagong University of Engineering & Technology
Ranjan Paul Maharishi International University
Md Rana Hossain Maharishi International University
Madhusodan Chakraborty Maharishi International University

DOI:

https://doi.org/10.4108/airo.9785

Keywords:

Life Jacket, Object detection, Age Classification, Yolov8, Safety Protocol

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.

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