An Effective Attention-based CNN Model for Fire Detection in Adverse Weather Conditions

This paper has been accepted to ISPRS Journal of Photogrammetry and Remote Sensing

1. Paper Links

https://www.sciencedirect.com/science/article/pii/S0924271623002940

2 Datasets

The datasets can be downloaded from the following links. We follow the training and testing data similar to the previous methods.

Option 1: Download FD dataset from given link: Click here

Option 2: The proposed DFAN dataset Click here

3. Citation and Acknowledgements

Please read and cite our following papers on Fire Detection if you like our work:

@article{yar2022optimized,
  title={Optimized dual fire attention network and medium-scale fire classification benchmark},
  author={Yar, Hikmat and Hussain, Tanveer and Agarwal, Mohit and Khan, Zulfiqar Ahmad and Gupta, Suneet Kumar and Baik, Sung Wook},
  journal={IEEE Transactions on Image Processing},
  volume={31},
  pages={6331--6343},
  year={2022},
  publisher={IEEE}
}
}

@article{yar2023modified,
  title={A modified YOLOv5 architecture for efficient fire detection in smart cities},
  author={Yar, Hikmat and Khan, Zulfiqar Ahmad and Ullah, Fath U Min and Ullah, Waseem and Baik, Sung Wook},
  journal={Expert Systems with Applications},
  volume={231},
  pages={120465},
  year={2023},
  publisher={Elsevier}
}
}

  @article{yar2023effective,
  title={An Effective Attention-based CNN Model for Fire Detection in Adverse Weather Conditions},
  author={Yar, Hikmat and Ullah, Waseem and Khan, Zulfiqar Ahmad and Baik, Sung Wook},
  journal={ISPRS Journal of Photogrammetry and Remote Sensing},
  volume={206},
  pages={335--346},
  year={2023},
  publisher={Elsevier}
}
}