This is the official pytorch implementation of FEANet: FEANet: Feature-Enhanced Attention Network for RGB-Thermal Real-time Semantic Segmentation (IEEE IROS). Some of the codes are borrowed from MFNet and RTFNet.
The current version supports Python>=3.8.10, CUDA>=11.3.0 and PyTorch>=1.11.0, but it should work fine with lower versions of CUDA and PyTorch.
Extensive experiments on the urban scene dataset demonstrate that our FEANet outperforms other state-of-the-art (SOTA) RGB-T methods in terms of objective metrics and subjective visual comparison (+2.6% in global mAcc and +0.8% in global mIoU). For the 480 × 640 RGB-T test images, our FEANet can run with a real-time speed on an NVIDIA GeForce RTX 2080 Ti card. Please take a look at thepaper.
The original dataset can be downloaded from the MFNet project page, but you are encouraged to download our preprocessed dataset from here.
The weights used in the paper:
FEANet : https://gofile.io/d/LAmzOE
python run_own_pth.py -dr [data_dir] -d [test] -f best.pth
python train.py -dr [data_dir] -ls 0.03 -b 5 -em 100
If you use FEANet in an academic work, please cite:
@inproceedings{DBLP:conf/iros/DengFLWYGCHGL21,
author = {Fuqin Deng and
Hua Feng and
Mingjian Liang and
Hongmin Wang and
Yong Yang and
Yuan Gao and
Junfeng Chen and
Junjie Hu and
Xiyue Guo and
Tin Lun Lam},
title = {FEANet: Feature-Enhanced Attention Network for RGB-Thermal Real-time
Semantic Segmentation},
booktitle = {{IEEE/RSJ} International Conference on Intelligent Robots and Systems,
{IROS} 2021, Prague, Czech Republic, September 27 - Oct. 1, 2021},
pages = {4467--4473},
publisher = {{IEEE}},
year = {2021},
url = {https://doi.org/10.1109/IROS51168.2021.9636084},
doi = {10.1109/IROS51168.2021.9636084},
timestamp = {Wed, 22 Dec 2021 12:37:50 +0100},
biburl = {https://dblp.org/rec/conf/iros/DengFLWYGCHGL21.bib},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
Hua Feng：1030001866@qq.com
Mingjian Liang: 2443434059@qq.com