You can find the preprint from the ... .

• The dataset is uploaded on IEEE dataport. You can find the dataset here at IEEE Dataport or DOI.

• This table shows all available data for the dataset.

• This project uses items 7, 8, 9, and 10 from the dataset. Items 7 and 8 are being used for the "Fire_vs_NoFire" image classification. Items 9 and 10 are for the fire segmentation.

• If you clone this repository on your local drive, please download item 7 from the dataset and unzip in directory /frames/Training/... for the Training phase of the "Fire_vs_NoFire" image classification. The direcotry looks like this:

Repository/frames/Training
                    ├── Fire/*.jpg
                    ├── No_Fire/*.jpg

• For testing your trained model, please use item 8 and unzip it in direcotry /frame/Test/... . The direcotry looks like this:

Repository/frames/Test
                    ├── Fire/*.jpg
                    ├── No_Fire/*.jpg

• Items 9 and 10 should be unzipped in these directories frames/Segmentation/Data/Image/... and frames/Segmentation/Data/Masks/... accordingly. The direcotry looks like this:

Repository/frames/Segmentation/Data
                                ├── Images/*.jpg
                                ├── Masks/*.png

• Please remove other README files from those directories and make sure that only images are there.

• The binary fire classifcation model of this project is based on the Xception Network:

• The fire segmentation model of this project is based on the U-NET:

• A short sample video of the dataset is available on YouTube:

• os
• re
• cv2
• copy
• tqdm
• scipy
• numpy
• pickle
• random
• itertools
• Keras 2.4.0
• Tensorflow 2.3.0
• matplotlib.pyplot

This code is run and tested on Python 3.6 on linux (Ubuntu 18.04) machine with no issues. There is a config.py file in this directoy which shows all the configuration parameters such as Mode, image target size, Epochs, batch size, train_validation ratio, etc. All dependency files are available in the root directory of this repository.

• To run the training phase for the "Fire_vs_NoFire" image classification, change the mode value to 'Training' in the config.py file. Like This

Mode = 'Training'

Make sure that you have copied and unzipped the data in correct direcotry.

• To run the test phase for the "Fire_vs_NoFire" image classification, change the mode value to 'Classification' in the config.py file. Change This

Mode = 'Classification'

Make sure that you have copied and unzipped the data in correct direcotry.

• To run the test phase for the Fire segmentation, change the mode value to 'Classification' in the config.py file. Change This

Mode = 'Segmentation'

Make sure that you have copied and unzipped the data in correct direcotry.

Then after setting your parameters, just run the main.py file.

python main.py

• Fire classification accuracy:

• Fire classification Confusion Matrix:

• Fire segmentation metrics and evaluation:

• Comparison between generated masks and grount truth mask:

If you find it useful, please cite our paper as follows:

For academtic and non-commercial usage