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D-Fire: an image dataset for fire and smoke detection
Authors: Researchers from Gaia, solutions on demand (GAIA)
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.
<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 |
| Class | # Bounding boxes |
|---|---|
| Fire | 14,692 |
| Smoke | 11,865 |
All images were annotated according to the YOLO format (normalized coordinates between 0 and 1). However, we provide the yolo2pixel function that converts coordinates in YOLO format to coordinates in pixels.
Examples
<div align="center"> <img src="https://lh3.googleusercontent.com/pw/AL9nZEUAI1XO1nuK0XmTSxd01nma6VZkZJ5Jrnj_qIvhqe1uxziYXmTnO5GLAFEdyric37YHGLersFbnZOZ1UQ5nOX057Kgze4d8d-fdX34O9972BnUI4n4zLt8_Lw0nm03cp8qqLX-72VRUHzMf01j-8XvtYg=s721-no" width="600"</img> </div>Download
- D-Fire dataset (only images and labels).
- Training, validation and test sets.
- Some surveillance videos.
- Some models trained with the D-Fire dataset.
- For more surveillance videos, request your registration on our environmental monitoring website "Apaga o Fogo!" (Put out the Fire!).
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>