This is the official Pytorch implementation for How to Reduce Change Detection to Semantic Segmentation.
Overall, we present a new paradigm that reduces change detection to semantic segmentation which means tailoring an existing and powerful semantic segmentation network to solve change detection.
Our analysis suggests that there are three possible change types within the change detection task and they should be learned separately. Therefore, we propose a MTF (Merge Temporal Features ) module to learn these changes.
We propose a simple but effective network, called C-3PO (Combine 3 POssible change types), detects changes in pixel-level, and can be considered as a new baseline network in this field.
- This project aims at providing a benchmark of change detection. We implement and include several previous methods in this project, such as
FC-EF,FC-Siam-diff,FC-Siam-cone,DR-TANet,CSCDNet, etc. Please checksrc/modelfor more details. - We mainly use the models in torchvision, such as
FCN,DeepLab, etc. This project does not use any other 3rd libraries, such as mmsegmentation. Hence, this project provides a guidance of how to utilize the torchvision models to efficiently solve other problems.
- Python3
- PyTorch
- Torchvision
- pycocotools
- timm
Python3, Pytorch and Torchvision are necessary. pycocotools is required for the COCO dataset. timm is required for the Swin Transformer backbone.
If you want to use CSCDNet in our project, please follow their instructions to install the correlation module.
If you do not want to use other models or install unnecessary packages, please checkout the mini branch, which only contains codes for our C-3PO model.
There are three datasets needed in this projects:
- COCO
- PCD
- VL-CMU-CD
- ChangeSim
Please refer to src/dataset/path_config.py to understand the folder structure of each dataset. And edit data_path according to your system.
Please follow this site to download the PCD dataset. You may need to send e-mails to Takayuki Okatani.
You can download VL-CMU-CD by this link.
Please follow this page to prepare the ChangeSim dataset.
training
python3 -m torch.distributed.launch --nproc_per_node=4 --use_env src/train.py --train-dataset VL_CMU_CD --test-dataset VL_CMU_CD --input-size 512 --model resnet18_mtf_msf_fcn --mtf id --msf 4 --warmup --loss-weight
testing
python3 src/train.py --test-only --model resnet18_mtf_msf_fcn --mtf id --msf 4 --train-dataset VL_CMU_CD --test-dataset VL_CMU_CD --input-size 512 --resume [checkpoint.pth]
We provide all shells to reproduce the results in our paper. Please check files in the exp folder. You can use below commands to run experiments.
source exp/sota/resnet18_mtf_id_msf4_deeplabv3_cmu.sh
train
Please download the weights by yourself. Check the test command in the shell, and modify --resume according to your download path. Then you can run the test command to evaluate the performance.
| Shell | Weights | Model | Dataset | Performance |
|---|---|---|---|---|
| resnet18_mtf_id_msf4_deeplabv3_cmu | resnet18_id_4_deeplabv3_VL_CMU_CD.pth | resnet18_mtf_msf_deeplabv3 | VL-CMU-CD | 79 |
| vgg16bn_mtf_id_msf4_deeplabv3_cmu | vgg16bn_id_4_deeplabv3_VL_CMU_CD.pth | vgg16bn_mtf_msf_deeplabv3 | VL-CMU-CD | 80 |
| resnet18_mtf_iade_msf4_deeplabv3_pcd | resnet18_iade_4_deeplabv3_PCD.pth | resnet18_mtf_msf_deeplabv3 | GSV/TSUNAMI set 0 | 80/88 |
| vgg16bn_mtf_iade_msf4_deeplabv3_pcd | vgg16bn_iade_4_deeplabv3_PCD.pth | vgg16bn_mtf_msf_deeplabv3 | GSV/TSUNAMI set 0 | 79/88 |
Note that the PCD dataset has 5 subsets, and we release our models for the first subset. Please train models on other subsets by yourself if you want to obtain them.
As stated in our paper, VL-CMU-CD and PCD suffer from noisy labels. We present the last and the best performance for reference. Overall, it is easy to achieve the performance between these two scores.
If you find the work useful for your research, please cite:
@article{wang2022c3po,
title={How to Reduce Change Detection to Semantic Segmentation},
author={Wang, Guo-Hua and Gao, Bin-Bin and Wang, Chengjie},
journal={Pattern Recognition},
year={2023}
}