TODbox (Tiny Object Detection Box)

We have now released the full sets (trainval, test) of AI-TOD-v2! [Download]

This is a repository of the official implementation of the following paper:

[Paper][Code] Detecting tiny Objects in aerial images: A normalized Wasserstein distance and A new benchmark (ISPRS J P & RS, 2022)
[Paper][Code] Dot distance for tiny object detection in aerial images (CVPRW, 2021)

Introduction

The Normalized Wasserstein Distance and the RanKing-based Assigning strategy (NWD-RKA) for tiny object detection.

A comparison between AI-TOD and AI-TOD-v2.

Supported Data

Notes: The images of the AI-TOD-v2 are the same of the AI-TOD. In this stage, we only release the train, val annotations of the AI-TOD-v2, the test annotations will be used to hold further competitions.

Supported Methods

Supported baselines for tiny object detection:

Baselines

Supported horizontal tiny object detection methods:

Supported rotated tiny object detection methods:

DCFL

Installation and Get Started

Required environments:

Linux
Python 3.6+
PyTorch 1.3+
CUDA 9.2+
GCC 5+
MMCV
cocoapi-aitod

Install TODbox:

Note that our TODbox is based on the MMDetection 2.24.1. Assume that your environment has satisfied the above requirements, please follow the following steps for installation.

git clone https://github.com/Chasel-Tsui/mmdet-aitod.git
cd mmdet-nwdrka
pip install -r requirements/build.txt
python setup.py develop

Citation

If you use this repo in your research, please consider citing these papers.

@inproceedings{xu2021dot,
  title={Dot Distance for Tiny Object Detection in Aerial Images},
  author={Xu, Chang and Wang, Jinwang and Yang, Wen and Yu, Lei},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops},
  pages={1192--1201},
  year={2021}
}

@inproceedings{NWDRKA_2022_ISPRS,
    title={Detecting Tiny Objects in Aerial Images: A Normalized Wasserstein Distance and A New Benchmark},
    author={Xu, Chang and Wang, Jinwang and Yang, Wen and Yu, Huai and Yu, Lei and Xia, Gui-Song},
    booktitle={ISPRS Journal of Photogrammetry and Remote Sensing},
    volume={190},
    pages={79--93},
    year={2022},
}