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Related Experiment Video

Updated: Jun 25, 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

Lightweight helmet target detection algorithm combined with Effici-Bi-Level Routing Attention.

Yanguo Huang¹, Minjie Fang¹, Jian Peng¹

¹College of Electrical and Automation, Jiangxi University of Science and Technology, GanZhou, China.

|May 29, 2024

Summary

FB-YOLOv7 enhances helmet detection accuracy by improving small target identification and reducing errors. This advanced network offers high efficiency and superior performance compared to existing models for two-wheeler safety.

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

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Area of Science:

Computer Vision
Artificial Intelligence
Road Safety

Background:

Helmet use is crucial for reducing two-wheeler accident injuries.
Existing helmet detection systems struggle with small targets and equipment limitations, leading to missed or false detections.

Purpose of the Study:

To develop an improved helmet detection network, FB-YOLOv7, addressing limitations of existing models.
Enhance the accuracy and efficiency of detecting helmets in real-world traffic scenarios.

Main Methods:

Utilized YOLOv7-tiny as a base and introduced an enhanced Bi-Level Routing Attention mechanism.
Implemented the AFPN framework with asymptotic adaptive feature fusion and EfficiCIoU loss for improved accuracy.

Main Results:

FB-YOLOv7 achieved 87.2% and 94.6% mean average precision (mAP@.5).
Demonstrated high efficiency with frame rates of 129 and 126 frames per second (FPS).
Outperformed six other detection networks in accuracy, small target sensitivity, and efficiency.

Conclusions:

FB-YOLOv7 significantly improves helmet detection performance, particularly for small targets.
The network offers a practical and efficient solution for enhancing road safety through better helmet compliance monitoring.