DENet, a dynamic equilibrium approach for cross-domain fire detection, effectively balancing learning across heterogeneous sensor data and outperforming classical incremental methods.
Ming Wang*, Dayu Yu*, Wanting He, Peng Yue*, Zheheng Liang
[Paper] [Project] [Dataset] [BibTeX]
The code requires python>=3.7, as well as pytorch>=1.7 and torchvision>=0.8. Please follow the instructions here to install both PyTorch and TorchVision dependencies. Installing both PyTorch and TorchVision with CUDA support is strongly recommended.
The following optional dependencies are necessary for mask post-processing, saving masks in COCO format, the example notebooks, and exporting the model in ONNX format. jupyter is also required to run the example notebooks.
pip install opencv-python pycocotools matplotlib
If you use DENet in your research, please use the following BibTeX entry.
@article{DENet,
title={Domain-incremental Learning for Fire Detection in Space-air-ground Integrated Observation Network},
author={Ming Wang, Dayu Yu, Wanting He, Peng Yue, Zheheng Liang},
journal={International Journal of Applied Earth Observation and Geoinformation},
year={2023}
}