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.
| Number of images | Number of bounding boxes |
|---|---|
| | 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
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:
-
Pedro Vinícius Almeida Borges de Venâncio, Adriano Chaves Lisboa, Adriano Vilela Barbosa: An automatic fire detection system based on deep convolutional neural networks for low-power, resource-constrained devices. In: Neural Computing and Applications, 2022.
If you use our surveillance videos, please cite the following paper:
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Pedro Vinícius Almeida Borges de Venâncio, Roger Júnio Campos, Tamires Martins Rezende, Adriano Chaves Lisboa, Adriano Vilela Barbosa: A hybrid method for fire detection based on spatial and temporal patterns. In: Neural Computing and Applications, 2023.