Advancing Public Safety with Real-Time Life Jacket Detection and Demographic Profiling Using YOLOv8 and Age Classification
Year: 2025
Type: article
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.
DOI: 10.4108/airo.9785
Authors: Md Abu Yusuf, Nur Mohammad Chowdhury, Ponchanon Datta Rone, Partha Pratim Saha, Md Iqbal Hossan, Debabrata Sarkar, Ranjan Paul, Md Rana Hossain, Madhusodan Chakraborty
Language: English
Subfield: Computer Vision and Pattern Recognition
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4414339356