A Deep Learning-Based Efficient Firearms Monitoring Technique for Building Secure Smart Cities

A Deep Learning-Based Efficient Firearms Monitoring Technique for Building Secure Smart Cities

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Product Code: Python - Deep Learning
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Product Description

Aim:

           The aim of this study is to detect weapon by employing YOLO v8, focusing on better accuracy.


Abstract:

          This research introduces an advanced approach utilizing YOLO v8 for more accurate and efficient firearm and weapon detection in the context of developing secure smart cities. While the existing method, Faster RCNN, effectively detected human faces and guns, this study proposes an upgrade to YOLO v8 for detecting both knives and guns with higher accuracy scores. The proposed method aims to address the limitations of the current techniques to bolster security measures in urban areas.

Proposed Method:

        The proposed approach utilizes YOLO v8, a state-of-the-art object detection system known for its accuracy and speed. YOLO v8 is implemented to identify both knives and guns with higher precision than the existing Faster RCNN model.


Advantages:

  • Enhanced Accuracy: YOLO v8 demonstrates improved accuracy in detecting both guns and knives.
  • Increased Speed: YOLO v8 is known for its faster processing, enabling real-time detection in smart city environments.
  • Expanded Weapon Detection: Unlike the previous model, YOLO v8 has the capability to detect multiple types of weapons, not just firearms.
  • Potential for Real-Time Security Response: The quicker and more accurate identification of weapons can aid law enforcement and security measures promptly.


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Package Includes

Software Projects Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
  8. Screen Shots
  9. Software Links
  10. Reference Papers
  11. Full Project Documentation
  12. Online support


The Delivery time for software projects is 2 -3 working days. Some of the software projects will require Hardware interface. Please go through the hardware Requirements in the abstract carefully. The Hardware will take 7-8 Working Days

 

Hardware Projects Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. Datasheets
  6. Circuit Diagrams
  7. Source Code
  8. Screen Shots & Photos
  9. Software Links
  10. Reference Papers
  11. Lit survey
  12. Full Project Documentation
  13. Online support


The Delivery time for Hardware projects is 7-8 working days.

   

Mini Projects: Software Includes

  1. Demo  Video
  2. Abstract
  3. Base paper
  4. Full Project PPT
  5. UML Diagrams
  6. SRS
  7. Source Code
  8. Screen Shots
  9. Software Links
  10. Reference Papers
  11. Full Project Documentation
  12. Online support

 

The Delivery time for software Miniprojects is 2 -3 working days.

 

Mini Projects - Hardware includes

  1. Demo  Video
  2. Abstract
  3. PPT
  4. Datasheets
  5. Circuit Diagrams
  6. Source Code
  7. Screen Shots & Photos
  8. Software Links
  9. Reference Papers
  10. Full Project Documentation
  11. Online support

The Delivery time for Hardware Mini projects is 7-8 working days.