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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:10.55041/IJSREM36415

DESIGNING OF COMPUTER VISION BASED SAFETY GEAR DETECTION USING YOLO V8 AND JETSON NANO

Stream/Area:

-

Subject:

-

Paper ID:

10.55041/IJSREM36415

Volume:

8 |

7 |

Jul 2024

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
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10.55041/IJSREM36415

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

K

K.Gurucharan

SK

S.S. Kiran

MRB

M. Rajan Babu

K

K.DHARMATEJA

KB

K.AVINASH BALAJI

KK

K.UDAY KUMAR

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Abstract

Enhancing worker safety on construction sites is pivotal, as lapses in wearing safety gear such as helmets, masks, and vests can lead to potential hazards. To address this issue, we introduce a novel solution leveraging the YOLO V8 device to detect helmet usage among workers. However, challenges arise due to security unawareness and discomfort, causing helmet removal. To mitigate this risk, we propose an advanced system employing the Jetson Nano board and . This system not only detects helmet adherence but also monitors the use of safety masks and vests. Unlike the existing YOLO V5-based approach, our proposed system adopts YOLO V8, ensuring heightened accuracy. The integration of a CSI Camera Module further enhances real-time monitoring capabilities. Through the synergy of Jetson Nano, YOLO V8 and CSI Camera Module, we pioneer an innovative solution to enforce safety compliance by preventing unauthorized entry without proper safety measures.

KEYWORDS

NVIDIA Jetson Nano Object detection Deep Learning YOLO V8 CSI Camera module.

Cite This Article

K.Gurucharan, S.S. Kiran, M. Rajan Babu, K.DHARMATEJA, K.AVINASH BALAJI, K.UDAY KUMAR, "DESIGNING OF COMPUTER VISION BASED SAFETY GEAR DETECTION USING YOLO V8 AND JETSON NANO," International Journal of Scientific Research in Engineering and Management, vol. 8, no. 7, Jul 2024. https://doi.org/10.55041/IJSREM36415

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Publication Date

7/14/2024

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Double-Blind Peer Review

Review Rounds: 2

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The authors declare no conflict of interest related to this research work.

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