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Weapons Detection system build with YoloV4-Darknet and deployed Locally with Flask

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Nayrouzzz/Gradution-Project---ITI---YoloV4-Weapons-Detection

 
 

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YoloV4 weapons Detection

automatic weapon detection software using a convolutional neural network (CNN) based on pre-trained You Only Look Once (YOLO) model.
The software detect three types of weapons : knives, pistols and rifles
deployed locally using Flask

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Table of Contents
  1. About The Project
  2. Getting Started
  3. Acknowledgments

About The Project

Product Name Screen Shot

The first and supreme goal of science is to serve man-kind, and since the security and safety of man-kind is one of the first and most important priorities, we worked on “Weapon Dectection Sysyem” depending on artificial intelligence and deep learning technologies can detect weapons such as: pistols, knifes and rifles then sending alert mails with the detected weapon for the authorized department such as: police department, security man, ... etc.

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Built With

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Getting Started

To get a local copy up and running follow these simple steps.

Prerequisites

To be able to run the system you have to install the following Prerequisites

Installation

  1. Clone the repo
    git clone https://github.com/MahmoudMTaha/YoloV4-Weapons-Detection.git
  2. Install Darknet Tutorial in a new folder "darknet-master"
  3. delete "data" and "cfg" folders from darknet-master
  4. copy "data" and "cfg" folders from the cloned darknet folder to "darknet-master"
  5. rename darknet.py in "darknet-master" to darknet2.py
  6. download "yolov4-custom_best.weights" from here into "darknet-master"
  7. delete the cloned darknet folder
  8. rename "darknet-master" to darknet and move it to the project folder
  9. edit Yolo_Test_and_Deployment.ipynb by putting sender email and password

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Acknowledgments

This is The Graduation Project of the Information Technology institute ITI AI program Powered by EPITA

Team

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Weapons Detection system build with YoloV4-Darknet and deployed Locally with Flask

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  • C 58.7%
  • Cuda 14.0%
  • C++ 13.8%
  • Python 4.0%
  • CMake 3.8%
  • PowerShell 1.4%
  • Other 4.3%