DFireDataset

:fire: D-Fire: an image dataset for fire and smoke detection

Authors: Researchers from Gaia, solutions on demand (GAIA)

:bulb: About

D-Fire is an image dataset of fire and smoke occurrences designed for machine learning and object detection algorithms with more than 21,000 images. The dataset summary is detailed in the table below.

<div align="center"> <table> <tr> <th>Number of images</th> <th>Number of bounding boxes</th> </tr> <tr><td>

| Category | # Images | | ------------- | ------------- | | Only fire | 1,164 | | Only smoke | 5,867 | | Fire and smoke | 4,658 | | None | 9,838 |

</td><td>

| Class | # Bounding boxes | | ------------- | ------------- | | Fire | 14,692 | | Smoke | 11,865 |

</td></tr> </table> </div>

Annotations follow the YOLO format, with normalized coordinates between 0 and 1. To facilitate usage, we provide a utils.yolo2pixel function to convert these normalized coordinates into pixel coordinates.

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:high_brightness: Example images

Below are some representative samples from the D-Fire dataset showcasing different scenarios of fire and smoke detection.

<p align="center"> <img width="500" src="./figures/dfire_examples.png" alt="D-Fire examples"> <p>

:computer: Download links

Access the D-Fire dataset, including images, annotations, and pre-split training, validation, and test sets via the following links. Additional resources such as surveillance videos and trained models are also available to support your research.

• D-Fire dataset (only images and labels).
• Training, validation and test sets.
• Kaggle version ready to use.
• Some surveillance videos.
• Some deep learning models trained with the D-Fire dataset.
• For more surveillance videos, request your registration on our environmental monitoring platform "Apaga o Fogo!" (Put out the Fire!).

:scroll: Citation

Please cite the following paper if you use our image database:

• <p align="justify">Pedro Vinícius Almeida Borges de Venâncio, Adriano Chaves Lisboa, Adriano Vilela Barbosa: <a href="https://link.springer.com/article/10.1007/s00521-022-07467-z"> An automatic fire detection system based on deep convolutional neural networks for low-power, resource-constrained devices. </a> In: Neural Computing and Applications, 2022.</p>

If you use our surveillance videos, please cite the following paper:

• <p align="justify"><b>Pedro Vinícius Almeida Borges de Venâncio</b>, Roger Júnio Campos, Tamires Martins Rezende, Adriano Chaves Lisboa, Adriano Vilela Barbosa: <a href="https://link.springer.com/article/10.1007/s00521-023-08260-2"> A hybrid method for fire detection based on spatial and temporal patterns. </a> In: Neural Computing and Applications, 2023.</p>