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Introduction:
The objective of this project is to develop a web application that uses surveillance cameras to detect fire and alert users in real-time. The application uses computer vision algorithms and machine learning techniques to analyze video footage from the cameras and detect the presence of fire. The project aims to improve fire safety by detecting potential fire hazards early and allowing users to take appropriate action.
Methods:
The project involved several steps, including collecting and labeling a dataset of video footage that contained both fire and non-fire events, preprocessing the video footage to extract individual frames, and training a machine learning model using the preprocessed dataset. The machine learning model was a convolutional neural network (CNN) that was trained to detect the presence of fire in an image.
Once the machine learning model was trained, a web application was developed that allowed users to upload video footage from their surveillance cameras. The uploaded footage was analyzed frame by frame using the trained machine learning model to detect the presence of fire. If fire was detected, the application triggered an alert and notified the user via email or SMS. The application also provided a live video feed from the surveillance camera and highlighted the region where the fire was detected.
Results:
The developed web application was able to accurately detect the presence of fire in video footage from surveillance cameras. The machine learning model achieved an accuracy of over 95% on the test dataset, indicating that it was able to accurately distinguish between fire and non-fire events. The web application was also able to provide real-time alerts and notifications to users when fire was detected, allowing them to take appropriate action.
Discussion:
The developed web application has several potential applications in improving fire safety in buildings. For example, it can be used in warehouses, factories, and other industrial settings where fire hazards are common. The application can also be used in homes and other residential settings, alerting residents to potential fire hazards in real-time.
The project has several limitations that should be considered. One limitation is the need for high-quality video footage from surveillance cameras. The accuracy of the machine learning model is highly dependent on the quality of the video footage. Another limitation is the need for periodic retraining of the machine learning model to ensure that it continues to accurately detect fire over time.
Conclusion:
The project has demonstrated the feasibility of using surveillance cameras and machine learning algorithms to develop a web application for fire detection. The application has the potential to improve fire safety in various settings, including industrial and residential settings. Further research is needed to optimize the accuracy of the machine learning model and to develop additional features that can enhance the functionality of the application.
Technology Used in the project :-
- We have developed this project using the below technology
- HTML : Page layout has been designed in HTML
- CSS : CSS has been used for all the desigining part
- JavaScript : All the validation task and animations has been developed by JavaScript
- Python : All the business logic has been implemented in Python
- Flask: Project has been developed over the Flask Framework
Supported Operating System :-
- We can configure this project on following operating system.
- Windows : This project can easily be configured on windows operating system. For running this project on Windows system, you will have to install
- Python 3.6.10, PIP, Django.
- Linux : We can run this project also on all versions of Linux operating systemMac : We can also easily configured this project on Mac operating system.
Installation Step : -
- python 3.6.8
- command 1 - python -m pip install --user -r requirements.txt
- command 2 - python app.py