Deep Convolutional Neural Network for Fire Detection

Deep Convolutional Neural Network for Fire Detection

₹5,500.00
Product Code: Python - Deep Learning
Availability: In Stock
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Product Description


Aim:

            To efficient RCNN based system for fire detection in videos captured in uncertain surveillance scenarios


Synopsis:

            Vision based fire detection framework has lately picked up popularity when contrasted with customary fire  recognition framework dependent on sensors. The need of  video perception at private, Modern, business regions and  woods areas has expanded the use of vision based fire  acknowledgment system Recently lots of fire related accidents  has occurred due to improper Surveillance or unable to cover  those uncertain regions like restricted areas in forest or any  factory buildings. In order to overcome such accidents , we  propose a new method using Convolutional neural networks (RCNN).

                     

Proposed System

            We propose an efficient RCNN based system for fire detection in videos captured in uncertain surveillance scenarios. Our approach uses light-weight deep neural networks with no dense fully connected layers, making it computationally inexpensive. Once detect a fire the information will pass through the firebase. Firebase is a one type of database. Then the firebase to send  a notification in android smartphone.             

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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.