This project provides the code and results for 'SGFNet: Semantic-Guided Fusion Network for RGB-Thermal Semantic Segmentation ', IEEE TCSVT, 2023. IEEE link

python 3.7/3.8 + pytorch 1.9.0 (built on EGFNet)

• Download the following pre-trained model.
• SGFNet.pth (code: 466s)
• SGFNet_pst.pth (code: lpte)
• test on MFNet dataset:

  python test.py --logdir [run logdir] --data-root [path to dataset] --pth [path to pre-trained model]

• test on PST900 dataset:

  python test_pst.py --logdir [run logdir] --data-root [path to dataset] --pth [path to pre-trained model]

We provide segmentation maps on MFNet dataset(SGFNet_MF.zip) and PST900 dataset(SGFNet_pst.zip).

   @ARTICLE{Wang_2023_SGFNet,
            author={Wang, Yike and Li, Gongyang and Liu, Zhi},
            journal={IEEE Transactions on Circuits and Systems for Video Technology}, 
            title={SGFNet: Semantic-Guided Fusion Network for RGB-Thermal Semantic Segmentation}, 
            year={2023},
            doi={10.1109/TCSVT.2023.3281419}}

If you encounter any problems with the code, want to report bugs, etc.

Please contact me at kevinwangyike@qq.com or shuwangyike@shu.edu.cn.