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

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.

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.

• 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!).

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:

• 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.