https://github.com/ushariranasinghe/fire-detection

CNN for fire detection using OpenCV techniques to enhance image features, achieving robust performance with TensorFlow and Keras.
https://github.com/ushariranasinghe/fire-detection

cnn-classification keras opencv pandas tensorflow2

Last synced: 26 days ago
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CNN for fire detection using OpenCV techniques to enhance image features, achieving robust performance with TensorFlow and Keras.

• Host: GitHub
• URL: https://github.com/ushariranasinghe/fire-detection
• Owner: ushariRanasinghe
• Created: 2024-08-18T07:51:33.000Z (3 months ago)
• Default Branch: main
• Last Pushed: 2024-09-20T09:55:21.000Z (about 2 months ago)
• Last Synced: 2024-10-13T00:41:45.380Z (26 days ago)
• Topics: cnn-classification, keras, opencv, pandas, tensorflow2
• Language: Jupyter Notebook
• Homepage:
• Size: 5.92 MB
• Stars: 1
• Watchers: 1
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.md

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README

        
# Fire Detection with Custom CNN

## Overview

This project aims to detect fire in images using a Convolutional Neural Network (CNN). To improve the accuracy and reduce the training time of the model, different features of the images are extracted using cv2 library, and fed into a multi-input CNN.

## Components

### Data Preparation

- **Dataset**: The dataset is divided into two directories: `fire` and `non_fire`, each containing images for respective classes.

- **Data Loading**: Images are loaded into a Pandas DataFrame along with their labels (`fire` or `non_fire`).

### Image Preprocessing

Images are preprocessed using three types of masks:

1. **Edge Mask**: Highlights edges in the image using Canny edge detection.

2. **Heatmap Mask**: Identifies bright regions that may indicate fire.

3. **Color Mask**: Isolates fire-like colors (reds, oranges, yellows) to highlight potential fire areas.

### Custom Data Generator

A custom data generator is implemented to:

- Load and preprocess images.

- Apply the three types of masks.

- Normalize the images.

- Yield batches of three masked images and their corresponding labels for model training.

### Model Architecture

A CNN model with three input branches is designed to process the three masked images. The model includes:

- **Convolutional Layers**: To extract features from images.

- **Pooling Layers**: To reduce spatial dimensions.

- **Dense Layers**: To classify images into fire or non-fire.

The model is compiled using the Adam optimizer with a learning rate of 0.0001 and binary crossentropy loss.

### Training and Evaluation

- **Training**: The model is trained with a training set and validated with a validation set using custom data generators. Callbacks are used to manage learning rate adjustments and save the best model.

- **Evaluation**: The model is evaluated on a test set to measure loss and accuracy.

### Prediction and Visualization

- **Prediction**: The model predicts the class of images (fire or non-fire) based on the preprocessed inputs.

- **Visualization**: A set of test images with predictions is displayed to assess the model's performance visually.

## Requirements

- Python 3.x

- TensorFlow 2.x

- OpenCV

- NumPy

- Pandas

- Matplotlib

- PIL

- tqdm

## Usage

1. **Prepare the Dataset**: Ensure the dataset directories are properly set up and contain images in the `fire` and `non_fire` categories.

2. **Run the Code**: Execute the provided script to load data, preprocess images, train the model, and evaluate performance.

3. **Visualize Results**: View the predictions on test images to understand the model's performance.

## Notes

- **Image Size**: The images are resized to 150x150 pixels.

- **Masks**: Custom masks are designed to enhance features relevant to fire detection.