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Related Experiment Video

Updated: Aug 10, 2025

A Test Bed to Examine Helmet Fit and Retention and Biomechanical Measures of Head and Neck Injury in Simulated Impact

A Test Bed to Examine Helmet Fit and Retention and Biomechanical Measures of Head and Neck Injury in Simulated Impact

Published on: September 21, 2017

Helmet-Wearing Tracking Detection Based on StrongSORT.

Fufang Li¹, Yan Chen¹, Ming Hu¹

¹School of Computer Science and Cyber Engineering, Guangzhou University, Guangzhou 510006, China.

Sensors (Basel, Switzerland)

|February 11, 2023

Summary

This summary is machine-generated.

This study introduces an enhanced YOLOv5 and StrongSORT model for real-time safety helmet detection in construction. The system accurately tracks workers and ensures compliance with safety regulations, improving workplace safety.

Keywords:

StrongSORT YOLOv5 focal-EIOU helmet wear tracking object detection

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Area of Science:

Computer Vision
Deep Learning
Artificial Intelligence

Background:

Object detection is crucial in computer vision, with deep learning algorithms widely applied.
Real-time monitoring of safety compliance, like helmet usage in construction, is essential for worker safety.

Purpose of the Study:

To propose a framework for real-time monitoring of workers wearing safety helmets in construction sites.
To improve the accuracy and robustness of helmet detection and tracking using deep learning.

Main Methods:

Utilized the YOLOv5 detection algorithm for object detection.
Integrated the improved StrongSORT multi-object tracking algorithm to handle occlusions and motion.
Trained a YOLOv5s model on a safety helmet dataset and fine-tuned the loss function to Focal-EIOU.

Main Results:

Achieved high performance with YOLOv5s, reaching 95.1% mAP@0.5 and 62.1% mAP@0.5:0.95.
The Focal-EIOU loss function improved precision to 96.5% and mAP metrics.
StrongSORT demonstrated efficient tracking at 0.05 seconds per frame.

Conclusions:

The combined YOLOv5s and StrongSORT framework provides effective real-time safety helmet detection and tracking.
The proposed system enhances worker safety management in construction environments.
Improvements in object detection and tracking algorithms contribute to more reliable safety monitoring solutions.