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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM48619

Road Accident Detection Using AI and ML.

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM48619

Volume:

9 |

5 |

May 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM48619

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

AS1

Aryan Salunke 1

AK

Aryan Kuchankar2

HM3

Hitesh Marathe 3

MJRC4

Mr. Jitendra R. Chavan 4

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Abstract

Road accidents have emerged as a critical global concern, resulting in significant loss of life, injuries, and property damage. Timely detection and response to such incidents can significantly reduce fatalities and improve emergency services. In this research, we propose an intelligent road accident detection system utilizing Artificial Intelligence (AI) and Machine Learning (ML) approaches. The system employs advanced computer vision techniques integrated with real-time video surveillance for accurate accident detection. Deep Learning models, particularly Convolutional Neural Networks (CNN), are used for object detection, motion analysis, and abnormal behavior identification on roads. This paper presents a detailed methodology, a literature review of existing systems, and a proposed architecture for efficient accident detection and notification systems. The findings suggest that AI-driven solutions can revolutionize road safety management systems.

KEYWORDS

Road Accident Detection Artificial Intelligence Machine Learning Deep Learning Convolutional Neural Network Real-time Monitoring Smart Transportation.

Cite This Article

Aryan Salunke 1, Aryan Kuchankar2, Hitesh Marathe 3, Mr. Jitendra R. Chavan 4, "Road Accident Detection Using AI and ML.," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 5, May 2025. https://doi.org/10.55041/IJSREM48619

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Publication Date

5/26/2025

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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2

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4

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5

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Conflict of Interest

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

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