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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM51444

VISIONGUARD- Object Detection in Adverse Weather Conditions with Health Monitoring

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM51444

Volume:

9 |

7 |

Jul 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM51444

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

DAS

Dr. Ashwani Sharma

RJS

Rohit J Sangan

NL

Nitinkumar Loni

YP

Yashwanth P

TKB

Thoshith Kumar B

ANM

Amruth N Murthy

KNM

Kavan N Murthy

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Abstract

Driving in bad weather like rain, fog, or low light often makes it hard for traditional vehicle detection systems to work effectively. To tackle this, we introduce VisionGuard—a smart solution that combines advanced object detection with real-time driver health and drowsiness monitoring. Powered by YOLOv8 and trained with the ACDC dataset, it’s built to handle tough visual conditions. It also uses ultrasonic and infrared sensors to improve how it sees the environment. On the inside, it tracks vital signs like heart rate, oxygen levels, and body temperature through wearable sensors, while keeping an eye on signs of fatigue using facial landmarks and blink detection. With over 95% detection accuracy, VisionGuard brings together deep learning, sensor fusion, and health monitoring into one system—making roads safer by looking out for both what’s outside the vehicle and how the driver is doing.

KEYWORDS

Autonomous Vehicles Object Detection YOLOv8 Deep Learning Health Monitoring Drowsiness Detection Sensor Fusion Adverse Weather.

Cite This Article

Dr. Ashwani Sharma, Rohit J Sangan, Nitinkumar Loni, Yashwanth P, Thoshith Kumar B, Amruth N Murthy, Kavan N Murthy, "VISIONGUARD- Object Detection in Adverse Weather Conditions with Health Monitoring," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 7, Jul 2025. https://doi.org/10.55041/IJSREM51444

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Publication Date

7/19/2025

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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1

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4

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5

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

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No funding information available for this research.

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