Real-Time Helmet Detection and Safety Compliance Analytics using Ultralytics YOLO11

Abstract

Helmet compliance is an important requirement in smart transportation and industrial safety settings because a failure in detection in time and/or errors can cause severe injuries to the head and loss of life. The performance of one stage DL detectors in real-time object detection in the challenging visual scenarios has been significantly improved recently. This study presents a real-time analytics platform based on Ultralytics YOLO that has been developed to identify and monitor helmet compliance and safety issues. The system is designed to reliably detect the occurrence of helmets and no-helmets in photos, movies, and live surveillance streams even in scenarios where there are small objects, occlusions and varying lighting conditions. Methodology contains considerable training of models, optimum preprocessing with a unified data set configuration, and preparation of datasets in the form of bounding boxes with YOLO format. Some of the detection models that are evaluated on standard performance measures that include mAP, recall, and precision are Faster R-CNN, YOLOv5s6u, YOLOv5x6u, YOLOv8, YOLOv9, and YOLOv11.Based on experimental results, YOLOv5s6u is more competitive in real-time conditions than two-stage detectors, as well as more recent versions of YOLO, with 93.0% mAP, 86.8% precision and 87.4% recall. The installed inference pipeline and the resulting computation of safety compliance statistics and live CCTV monitoring makes scalable and automated helmet enforcement in real-world settings possible.