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YOLO-AFL: A Novel Lightweight Algorithm for Real-Time Safety Helmet Detection in Factory Workshops.

Hao Wang^1,2, Xianying Feng^1,2, Peigang Li^1,2

¹School of Mechanical Engineering, Shandong University, Jinan 250061, China.

Sensors (Basel, Switzerland)

|May 27, 2026

Summary

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A new lightweight model, YOLO-AFL, efficiently detects safety helmet wearing in factories. It reduces model size and computation while maintaining high accuracy, improving worker safety in resource-limited environments.

Area of Science:

Computer Vision
Artificial Intelligence
Industrial Safety

Background:

Worker safety in factories necessitates effective safety helmet detection.
Current deep learning models for safety helmet detection are computationally intensive and have large parameters, limiting their use in resource-constrained settings.

Purpose of the Study:

To introduce YOLO-AFL, a novel lightweight model for efficient safety helmet detection.
To optimize deep learning models for industrial safety applications with limited computational resources.

Main Methods:

Implemented K-Means++ algorithm with a novel (1 - AIoU) distance metric for enhanced anchor box estimation.
Introduced lightweight PConv operation into the C3 module to reduce model complexity.
Designed a dual attention network (LDA-GC) to maintain accuracy after model simplification.

Keywords:

YOLO lightweight model safety helmet target detection

Related Experiment Videos

Main Results:

Achieved an mAP50 of 94.1% on a custom dataset for safety helmet detection.
Reduced model parameters by 19.1% compared to the baseline model.
Decreased computational complexity by 16.9%.

Conclusions:

YOLO-AFL demonstrates superior performance and efficiency for safety helmet wearing detection.
The proposed optimizations enable effective deployment of safety detection systems in resource-constrained industrial environments.
The model significantly enhances worker safety through efficient and accurate detection.