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ArticlePeer ReviewedOpen AccessPlagiarism Passed
Paper ID:IJSREM47814

Accident Detection System

Stream/Area:

-

Subject:

-

Paper ID:

IJSREM47814

Volume:

9 |

5 |

May 2025

Pages:

Publication Frequency:

Monthly (12 issues per year)

DOI
Digital Object Identifier
10.55041/IJSREM47814

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

AC

Ajeet Chaurasiya

PM

Padmini Mishra

AY

Adarsh Yadav

AH

Ali Husain

SG

Shivam Gupta

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Abstract

This research paper presents an advanced accident detection system utilizing the cutting-edge YOLOv11 algorithm. The system aims to detect and classify vehicular accidents in real-time through traffic surveillance cameras. YOLOv11, the latest iteration in the YOLO family, offers significant improvements in detection accuracy and processing speed compared to its predecessors and other single-stage detectors like SSD. Our implementation achieved a remarkable 96.7% accuracy in accident detection with a false positive rate of only 1.8% and an average detection time of 0.4 seconds. The system demonstrates robust performance across various environmental conditions and traffic scenarios. This research contributes to intelligent transportation systems by providing a reliable, efficient solution for rapid accident detection, potentially reducing emergency response times by up to 8 minutes and improving overall road safety infrastructure.

KEYWORDS

Accident detection YOLOv11 Traffic surveillance Real-time detection Road safety Detection accuracy.

Cite This Article

Ajeet Chaurasiya, Padmini Mishra, Adarsh Yadav, Ali Husain, Shivam Gupta, "Accident Detection System," International Journal of Scientific Research in Engineering and Management, vol. 9, no. 5, May 2025. https://doi.org/10.55041/IJSREM47814

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Publication Date

5/13/2025

Peer Review Type

Double-Blind Peer Review

Review Rounds: 2

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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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No funding information available for this research.

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