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https://github.com/jwwangchn/AI-TOD
Official code for "Tiny Object Detection in Aerial Images".
https://github.com/jwwangchn/AI-TOD
aerial-images computer-vision dataset object-detection
Last synced: about 2 months ago
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Official code for "Tiny Object Detection in Aerial Images".
- Host: GitHub
- URL: https://github.com/jwwangchn/AI-TOD
- Owner: jwwangchn
- License: mit
- Created: 2020-10-13T07:19:58.000Z (about 4 years ago)
- Default Branch: master
- Last Pushed: 2024-05-16T13:41:42.000Z (7 months ago)
- Last Synced: 2024-08-01T13:16:59.642Z (5 months ago)
- Topics: aerial-images, computer-vision, dataset, object-detection
- Language: Python
- Homepage:
- Size: 1.95 MB
- Stars: 178
- Watchers: 3
- Forks: 18
- Open Issues: 26
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
# AI-TOD
[[Paper]](https://drive.google.com/file/d/1IiTp7gilwDCGr8QR_H9Covz8aVK7LXiI/view?usp=sharing) AI-TOD is a dataset for tiny object detection in aerial images.
[[Dataset]](https://github.com/jwwangchn/AI-TOD) Please download the [xView trainig set](http://xviewdataset.org/#dataset) and [AI-TOD_wo_xview](https://1drv.ms/u/s!Ao5UiAkIbGJ7xHCGhQe2tsU1Ut5i?e=SrUdYp) to [construct](aitodtoolkit) the complete AI-TOD dataset!
## Description
AI-TOD comes with 700,621 object instances for eight categories across 28,036 aerial images. Compared to existing object detection datasets in aerial images, the mean size of objects in AI-TOD is about 12.8 pixels, which is much smaller than others.
## Download
You need to download the following two parts (Part1: xView training set, Part2: part of AI-TOD) and use our end-to-end synthesis tool to generate the complete AI-TOD dataset. (Note the we have released the **complete annotations** of AI-TOD, you only need to **generate images**)
* xView training set. [[Website]](http://xviewdataset.org/#dataset)
* Part of AI-TOD. [[OneDrive]](https://1drv.ms/u/s!Ao5UiAkIbGJ7xHCGhQe2tsU1Ut5i?e=SrUdYp)
* E2E aitodtoolkit. [[Folder]](aitodtoolkit)
## A Guide on Generating AI-TOD
**Step 1:** Download the xView training set, AI-TOD without xview, and clone the aitodtoolkit.
```
git clone https://github.com/jwwangchn/AI-TOD.git
```
**Step 2:** Organize the downloaded files in the following way.
```
├─aitod
│ ├─annotations ## put the downloaded annotations of AI-TOD_wo_xview (.json)
│ └─images ## unzip the downloaded AI-TOD_wo_xview image sets, put them (.png) in the corresponding folder
│ ├─test ## directly put the images in it without extra folder
│ ├─train
│ ├─trainval
│ └─val
├─aitod_xview ## here are six files (.txt)
├─xview
│ ├─ori
│ │ └─train_images ## unzip the downloaded xView training set images, put them (.tif) here
│ └─xView_train.geojson ## the annotation file of xView training set
└─generate_aitod_imgs.py ## end-to-end tool
```
**Step 3:** Install required packages.
* Required environment
1. Python 3.7
2. [mmcv](https://github.com/open-mmlab/mmcv)
* Install [wwtool](https://github.com/jwwangchn/wwtool)
```
git clone https://github.com/jwwangchn/wwtool.git
cd wwtool
python setup.py develop
```
* Install other required packages
```
cd ..
cd aitodtoolkit
pip install -r requirements.txt
```
**Step 4:** Run the E2E aitodtoolkit and get AI-TOD, it might take around an hour, then the full image sets of AI-TOD can be found in the **aitod** folder. And you can delete other files in other folders to avoid taking up too much space.
```
python generate_aitod_imgs.py
```
## Evaluation
Training, Validation and Testing sets are both publicly available now. We report the COCO style performance in the original paper, you can use the [cocoapi-aitod](https://github.com/jwwangchn/cocoapi-aitod) to evaluate the model performance.
## Citation
If you use this dataset in your research, please consider citing these papers.
```
@inproceedings{AI-TOD_2020_ICPR,
title={Tiny Object Detection in Aerial Images},
author={Wang, Jinwang and Yang, Wen and Guo, Haowen and Zhang, Ruixiang and Xia, Gui-Song},
booktitle=ICPR,
pages={3791--3798},
year={2021},
}
```
```
@article{NWD_2021_arXiv,
title={A Normalized Gaussian Wasserstein Distance for Tiny Object Detection},
author={Wang, Jinwang and Xu, Chang and Yang, Wen and Yu, Lei},
journal={arXiv preprint arXiv:2110.13389},
year={2021}
}
```
## Reference
[xView Dataset](http://xviewdataset.org/)
## License
The AI-TOD dataset is licensed under the Attribution-NonCommercial-ShareAlike 4.0 International ([CC BY-NC-SA 4.0](https://creativecommons.org/licenses/by-nc-sa/4.0/)). Thus AI-TOD dataset are freely available for academic purpose or individual reserach, but restricted for commercial use. Besides, the underlying codes are licensed under the MIT license.