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.
- 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.
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
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}
}