Fire Detection Model using Deep Learning

By: Engineer's Planet

Lately, there have been many fire outbreaks which is becoming a growing issue and the damage caused by these types of incidents is tremendous to nature and human interests. Such incidents have highlighted the need for more effective and efficient fire detection systems. The main goal of this project is to create a fire detection system. The key objectives of the project is to identify fires from the images we can get from surveillance system or other resources

Collect data on different types of fires, including images and videos of flames, smoke, and heat. The data should be collected in a controlled environment to ensure that it is representative of real-world fires.

1. Data Collection:

Clean and preprocess the data to ensure that it is ready for use in training the deep learning model. This may include resizing images, normalizing pixel values, and splitting the data into training, validation, and test sets.

2. Data Preprocessing:

3. Model Development:

Use deep learning techniques such as convolutional neural networks (CNNs) to develop a model that can detect fires in the collected data. The model should be trained on the preprocessed data, and the accuracy of the model should be evaluated using the test set.

4. Success Metrics

The accuracy of the model on the test data set should be > 85%(Subjective in nature)

In conclusion, the development of a fire detection model using deep learning holds immense potential for enhancing safety and security measures. Leveraging cutting-edge technology, this project showcases the effectiveness of AI in swiftly identifying and alerting to fire hazards, ultimately safeguarding lives and property in various environments.

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