Weapon Detection System Using  Deep Learning

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P Murugan; A Merry Ida; S.Amshiga Brillian; M.Seals Ancy; R Aashika; S.Sneha

Weapon Detection System Using Deep Learning

Authors: P Murugan, A Merry Ida, S.Amshiga Brillian, M.Seals Ancy, R Aashika, S.Sneha

Unique Paper ID: 160929
Volume: 10
Issue: 2
PageNo: 104-109

Keywords: Computer vision weapon detection Faster RCNN CCTV Machine learning (MI)

Abstract:
Weapon detection is a crucial task in ensuring public safety and preventing violent incidents. In recent years, computer vision technology has been used for real- time weapon detection, and the YOLO (You Only Look Once) object detection algorithm has emerged as a popular and efficient technique for this purpose. In this report, we discuss the use of YOLO for weapon detection, and its performance in identifying firearms, knives, and other weapons. We trained a YOLOv8 model using a dataset of annotated images, and evaluated its performance on a test dataset. The results indicate that YOLO is an accurate and efficient technique for weapon detection, achieving a high map and mA it improves the performance of the model in different scenarios.

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Cite This Article

ISSN: 2349-6002
Volume: 10
Issue: 2
PageNo: 104-109

Weapon Detection System Using Deep Learning

Available:https://ijirt.org/article?manuscript=160929

Impact Factor
8.01 (Year 2024)

UGC Approved
Journal no 47859

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