A Multi-Weapon Detection Using Deep Learning

Authors

  • Moahaimen Talib Mustansriya sity
  • Jamila H. Saud Mustansiriyah University

DOI:

https://doi.org/10.31987/ijict.7.1.242

Keywords:

WeaponDetection, Cnn, Yolov7, deep learning, Anti-Terrorism

Abstract

The escalating usage of light weapons in criminal and terrorist activities has necessitated the development of advanced weapon detection systems. This study centers on the application of the high-speed deep learning detection model, You Only Look Once version 7 (YOLOv7), to address the need for efficient and swift threat identification. The proposed system, trained on a self-curated dataset rich in images of various dangerous light weapons, is designed to recognize and distinguish multiple weapons simultaneously. The unique value of YOLOv7 in object and specifically weapon detection lies in its outstanding speed and accuracy, as demonstrated by certain weapon categories achieving a mean average precision (mAP) of 97%. The system’s performance can potentially be further enhanced by augmenting the image dataset for each weapon category. This study, therefore, not only validates the critical importance of YOLOv7 in advancing detection methodologies but also presents a practical solution for bolstering public safety.

 

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Published

2024-05-03

How to Cite

A Multi-Weapon Detection Using Deep Learning. (2024). Iraqi Journal of Information and Communication Technology, 7(1), 11-22. https://doi.org/10.31987/ijict.7.1.242

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