Real-Time Human Age Estimation Using an Optimized YOLOv11 Framework

Abstract

Object detection, which finds and identifies objects in an image or video, is a crucial component of computer vision. YOLO (You Only Look Once) models are real-time object identification algorithms that identify and classify objects in an image. Because YOLO processes the entire image in a single pass, it is faster and more efficient. This study estimates a person's age from facial pictures using the latest real-time object identification model, YOLO v11.YOLOv11 boasts exceptional speed, precision, efficiency, and enhanced feature extraction. The suggested approach recognizes faces and classifies them into four age groups: adolescents (ages 13 to 19), young adults (ages 20 to 35), adults (ages 36 to 55) and seniors (above 55). Precision, Recall, F1-Score are increased by 1%. Real-time object detection AI, surveillance and security hidden object puzzles (games), transportation and autonomous driving (booking services), agriculture and environmental monitoring, healthcare and specialized fields make use of YOLO models.