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© 2026 IJSREM – International Journal of Scientific Research in Engineering and Management. This is an open-access journal. Articles are published under the Creative Commons Attribution (CC BY 4.0) License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM28483

Helmet Detection System

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM28483

Volume:

8 |

2 |

Feb 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM28483

License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.

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Authors

1A

1Sayyed Arish

2R

2Khan Rohaan

3M

3Zaid Mulla

4z

4Khan zaka

5S

5Zuber Shaikh

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Abstract

This paper presents a real-time helmet detection system designed to enhance safety in workplaces and traffic environments. Utilizing deep learning models like YOLO (You Only Look Once), the system detects whether individuals are wearing helmets in live video feeds or images. Trained on a diverse dataset, the system achieves high accuracy and efficiency, making it suitable for deployment in safetycritical areas. The paper highlights its potential applications, challenges, and future improvements, such as IoT integration and edge computing, to further enhance performance and scalability. This work contributes to leveraging AI for safety enforcement and accident prevention.

KEYWORDS

Helmet Detection Computer Vision DeepLearning Convolutional Neural Networks (CNNs) YOLO (You Only Look Once) Real-Time Object Detection

Cite This Article

1Sayyed Arish, 2Khan Rohaan, 3Zaid Mulla, 4Khan zaka, 5Zuber Shaikh, "Helmet Detection System," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 2, Feb 2024. https://doi.org/10.55041/IJSREM28483

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1/1/45699

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Double-Blind Peer Review

Review Rounds: 2

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