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.