Advance Surveillance System Integrated with Weapon Detection and Accident Detection

Year : 2025 | Volume : 13 | Issue : 01 | Page : 47 54
    By

    Amisha Vartak,

  • Himanshu Ghode,

  • Aniket Kashid,

  • Prajakta Jadhav,

  1. Student, Department of Computer Engineering Vishwaniketan’s Institute of Management Entrepreneurship and Engineering Technology (VIMEET), Khalapur, Maharashtra, India
  2. Student, Department of Computer Engineering Vishwaniketan’s Institute of Management Entrepreneurship and Engineering Technology (VIMEET), Khalapur, Maharashtra, India
  3. Student, Department of Computer Engineering Vishwaniketan’s Institute of Management Entrepreneurship and Engineering Technology (VIMEET), Khalapur, Maharashtra, India
  4. Assistant Professor, Department of Computer Engineering Vishwaniketan’s Institute of Management Entrepreneurship and Engineering Technology (VIMEET), Khalapur, Maharashtra, India

Abstract

Security concerns have become paramount as there is rise in crime rates in crowded events and isolated areas. Abnormal event detection and monitoring system, utilizing computer vision, are crucial for tackling these challenges. In parallel, reducing mortality rates from accidents by ensuring timely emergency response in essential. This study presents the implementation of automatic weapon detection and accident detection. In weapon detection, YOLO v4, Convolutional Neural Networks (CNN), and Faster RCNN algorithms were used. Results indicate both algorithms provide good accuracy, though their real-world application depends on speed and precision. In Accident detection, Keras model, CNN, and RCNN were used. This study also explores the development of Weapon Detection and Accident Detection and Alert System using software to detect weapons and vehicle accidents. In a camera prototype, GPS modules provide the accident location, and GSM sends email notifications. This system ensures instant alerts to emergency services in case of an accident, delivering real-time updates on the vehicle’s location. By reducing response time, it enhances the chances of timely medical intervention and increases survival rates.

Keywords: Convolutional neural network, single shot detection, YOLOv4, region convolutional neural networks (R-CNN), object detection

[This article belongs to Research & Reviews: A Journal of Embedded System & Applications ]

How to cite this article:
Amisha Vartak, Himanshu Ghode, Aniket Kashid, Prajakta Jadhav. Advance Surveillance System Integrated with Weapon Detection and Accident Detection. Research & Reviews: A Journal of Embedded System & Applications. 2025; 13(01):47-54.
How to cite this URL:
Amisha Vartak, Himanshu Ghode, Aniket Kashid, Prajakta Jadhav. Advance Surveillance System Integrated with Weapon Detection and Accident Detection. Research & Reviews: A Journal of Embedded System & Applications. 2025; 13(01):47-54. Available from: https://journals.stmjournals.com/rrjoesa/article=2025/view=203811


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Regular Issue Subscription Review Article
Volume 13
Issue 01
Received 02/02/2025
Accepted 17/02/2025
Published 18/03/2025
Publication Time 44 Days
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