<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Safety helmets, as crucial protective equipment, significantly contribute to the head safety of workers. Adherence to safety helmet regulations is integral to construction site safety management. Recognizing the limitations inherent in manual supervision methods, we have developed a vision-based framework for the detection of workers and their safety helmets. This framework features enhancements to the YOLOv5s model, resulting in the advanced YOLOv5-Pro. The enhanced YOLOv5-Pro model achieved a mean Average Precision (mAP) of 95.4% on the validation set, marking an improvement of 3.6% over the original model. Furthermore, we expanded the utility of the YOLOv5-Pro model by incorporating nighttime data augmentation. The augmented YOLOv5-Pro model demonstrated robust performance in both daytime and nighttime conditions, as evidenced by our experimental results.</p>
</abstract>

Safety helmets, as crucial protective equipment, significantly contribute to the head safety of workers. Adherence to safety helmet regulations is integral to construction site safety management. Recognizing the limitations inherent in manual supervision methods, we have developed a vision-based framework for the detection of workers and their safety helmets. This framework features enhancements to the YOLOv5s model, resulting in the advanced YOLOv5-Pro. The enhanced YOLOv5-Pro model achieved a mean Average Precision (mAP) of 95.4% on the validation set, marking an improvement of 3.6% over the original model. Furthermore, we expanded the utility of the YOLOv5-Pro model by incorporating nighttime data augmentation. The augmented YOLOv5-Pro model demonstrated robust performance in both daytime and nighttime conditions, as evidenced by our experimental results.

Workers and Safety Helmets Detection in Day and Night Scenes based on improved YOLOv5

Economics and Management School of Tongji University

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

Safety helmets, as crucial protective equipment, significantly contribute to the head safety of workers. Adherence to safety helmet regulations is integral to...

Workers and Safety Helmets Detection in Day and Night Scenes based on improved YOLOv5

Online veröffentlicht: 02. Juli 2024

Seitenbereich: -

Eingereicht: 03. März 2024

Akzeptiert: 28. Mai 2024

DOI: https://doi.org/10.2478/amns-2024-1542

Schlüsselwörter
Deep Learning, Object detection, Safety Helmet, Construction Management

© 2024 Guofeng Ma et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Workers and Safety Helmets Detection in Day and Night Scenes based on improved YOLOv5

Online veröffentlicht: 02. Juli 2024

Seitenbereich: -

Eingereicht: 03. März 2024

Akzeptiert: 28. Mai 2024

DOI: https://doi.org/10.2478/amns-2024-1542

SchlüsselwörterDeep Learning, Object detection, Safety Helmet, Construction Management

© 2024 Guofeng Ma et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
Deep Learning, Object detection, Safety Helmet, Construction Management