Multi-Scale Semantic Fusion-Guided Fractal Convolutional Object Detection Network for Optical Remote Sensing Imagery
- The project contains the code for implementing the MSFC-Net for optiacal remote sensing object detection.
- This work has been published on IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
| Backbone | ImageSize | mAP | Model |
|---|---|---|---|
| VGG-19 | 512x512 | 77.31 | download code:sofk |
| Detnet-50 | 512x512 | 77.44 | download code:q5g6 |
| ResNet-50 | 512x512 | 77.65 | download code:k5s5 |
| Cspdarknet-53 | 512x512 | 77.84 | download code:sx23 |
| ResNet-101 | 512x512 | 77.83 | download code:wess |
| ResNeXt-101 | 512x512 | 78.07 | download code:qwer |
| ResNeSt-101 | 512x512 | 80.26 | download code:dota |
| Backbone | ImageSize | mAP | Model |
|---|---|---|---|
| ResNeSt-101 | 512x512 | 70.08 | download code:dior |
Please refer to INSTALL.md for installation.
inital data structure:
data/dota
├── train
│ ├── images
│ └── labelTxt
└── val
├── images
└── labelTxt
- Split the original images
python dota_devkit/ImgSplit_multi_process.py
(Manually enter the basepath and outpath)
Then, you should get data in following structure.
data/dota_split
├── train
│ ├── images
│ └── labelTxt
└── val
├── images
└── labelTxt
- Create COCO format json
python dota_devkit/DOTA2COCO.py
(Manually enter the srcpath and destfile)
data/dota_split
├── train
│ ├── images
│ ├── labelTxt
│ └── train.json
│
└── val
├── images
├── labelTxt
└── val.json
(The purpose of splitting val dataset is to find the optimal model, not for testing.)
For finishing the test, firstly, you only need to download the DOTA validation and DIOR test images, the ground truth have been provided in MSFC-Net_ROOT/exp/mAP/DOTA(DIOR)/input/ground-truth/. Secondly, please download above the model networks. Finally, you can test as follow:
For example, when testing on DOTA validation, run: (recommended to set CUDA_VISIBLE_DEVICES before running)
python test.py ctdet --exp_id test_dota --test_dir `your data path` --patch_size 512 --patch_overlap 128 --dataset DOTA --nms --arch msfc_101 --test_scales 1,0.4 --load_model `your model path`
--arch msfcvgg_19
--arch msfcresnet_101
--arch msfscspdarknet_53
--arch ...
when testing on DIOR test, run:
python test.py ctdet --exp_id test_dior --test_dir `your data path` --patch_size 512 --patch_overlap 112 --dataset DIOR --nms --arch msfc_101 --test_scales 1,0.4 --load_model `your model path`
python train.py ctdet --exp_id msfcnet --batch_size 10 --dataset DOTA --arch msfc_101 --num_epoches 100
After testing the images, the results would be saved in MSFC-Net_ROOT/exp/mAP/DOTA(DIOR)/input/detection-results/.
The mAP is calculated by the https://github.com/Cartucho/mAP, for convenience, the code have been downloaded into the project MSFC-Net_ROOT/exp/mAP/DOTA and MSFC-Net_ROOT/exp/mAP/DIOR.
For calculating the mAP, run:
python main.py -na
You can follow the below steps to run a quick demo:
you need to download the network model (DOTA or DIOR), then run:
python demo.py ctdet --demo `MSFC-Net_ROOT/exp/demo/images/` --load_model `your model path` --nms --test_scales 1 --dataset DOTA --arch msfc_101
Contact: bit_zhangtong@163.com. If you have any questions.
The code is modified based on CenterNet.
@ARTICLE{msfc2021zhang,
author={Zhang, Tong and Zhuang, Yin and Wang, Guanqun and Dong, Shan and Chen, He and Li, Lianlin},
journal={IEEE Transactions on Geoscience and Remote Sensing},
title={Multiscale Semantic Fusion-Guided Fractal Convolutional Object Detection Network for Optical Remote Sensing Imagery},
year={2022},
volume={60},
number={},
pages={1-20},
doi={10.1109/TGRS.2021.3108476}}