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https://github.com/gaiasd/DFireDataset
D-Fire: an image data set for fire and smoke detection.
https://github.com/gaiasd/DFireDataset
computer-vision dataset fire-detection smoke-detection yolo
Last synced: 3 months ago
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D-Fire: an image data set for fire and smoke detection.
- Host: GitHub
- URL: https://github.com/gaiasd/DFireDataset
- Owner: gaiasd
- License: other
- Created: 2019-08-14T20:08:10.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2023-02-13T16:12:26.000Z (over 1 year ago)
- Last Synced: 2024-06-16T17:07:20.875Z (5 months ago)
- Topics: computer-vision, dataset, fire-detection, smoke-detection, yolo
- Language: Python
- Homepage:
- Size: 63.5 KB
- Stars: 145
- Watchers: 5
- Forks: 25
- Open Issues: 0
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Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
- awesome-yolo-object-detection - gaiasd/DFireDataset - Fire: an image data set for fire and smoke detection. (Applications)
- awesome-yolo-object-detection - gaiasd/DFireDataset - Fire: an image data set for fire and smoke detection. (Applications)
README
# D-Fire: an image dataset for fire and smoke detection
**Authors:** Researchers from Gaia, solutions on demand ([GAIA](https://www.gaiasd.com/))
## 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)](https://drive.google.com/drive/folders/1DWgsQLVgkkLM8m-VcugHNpD5WYDbjYp5?usp=sharing).
* [Training, validation and test sets](https://drive.google.com/drive/folders/1Np_FC3MuuFJgV-z0FmZwS9YzsTKdyRGJ?usp=sharing).
* [Some surveillance videos](https://drive.google.com/drive/folders/1P5TNDP7ZrWpIZ4v_Aav5hf3S9UII2ZKA?usp=sharing).
* [Some models trained with the D-Fire dataset](https://github.com/pedbrgs/Fire-Detection).
* For more surveillance videos, request your registration on our environmental monitoring website ["Apaga o Fogo!" (Put out the Fire!)](https://apagaofogo.eco.br/).
## Citation
Please cite the following paper if you use our image database:
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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.