Vision-Based UAV Self-Positioning in Low-Altitude Urban Environments

This repository contains code and dataset for the paper titled Vision-Based UAV Self-Positioning in Low-Altitude Urban Environments. In this paper, we propose a method for accurately self-positioning unmanned aerial vehicles (UAVs) in challenging low-altitude urban environments using vision-based techniques. We provide the DenseUAV dataset and a Baseline model implementation to facilitate research in this task. Thank you for your kind attention.

News

2023/12/18: Our paper is accepted by IEEE Trans on Image Process.
2023/8/14: Our dataset and code are released.

News
Table of contents
About Dataset
Prerequisites
Installation
Dataset & Preparation
Train & Evaluation
- Training and Testing
- Evaluation
Supported Methods
License
Citation
Related Work

About Dataset

The dataset split is as follows:

Subset	UAV-view	Satellite-view	Classes	universities
Training	6,768	13,536	2,256	10
Query	2,331	4,662	777	4
Gallery	9099	18198	3033	14

More detailed file structure:

├── DenseUAV/
│   ├── Dense_GPS_ALL.txt           /* format as: path latitude longitude height
│   ├── Dense_GPS_test.txt
│   ├── Dense_GPS_train.txt
│   ├── train/
│       ├── drone/                   /* drone-view training images
│           ├── 000001
│               ├── H100.JPG
│               ├── H90.JPG
│               ├── H80.JPG
|           ...
│       ├── satellite/               /* satellite-view training images
│           ├── 000001
│               ├── H100_old.tif
│               ├── H90_old.tif
│               ├── H80_old.tif
│               ├── H100.tif
│               ├── H90.tif
│               ├── H80.tif
|           ...
│   ├── test/
│       ├── query_drone/             /* UAV-view testing images
│       ├── query_satellite/         /* satellite-view testing images

Prerequisites

Python 3.7+
GPU Memory >= 8G
Numpy 1.21.2
Pytorch 1.10.0+cu113
Torchvision 0.11.1+cu113

Installation

It is best to use cuda version 11.3 and pytorch version 1.10.0. You can download the corresponding version from this website and install it through pip install. Then you can execute the following command to install all dependencies.

pip install -r requirments.txt

Create the directory for saving the training log and ckpts.

mkdir checkpoints

Dataset & Preparation

Download DenseUAV upon request. You may use the request Template.

Train & Evaluation

Training and Testing

You could execute the following command to implement the entire process of training and testing.

bash train_test_local.sh

The setting of parameters in train_test_local.sh can refer to Get Started.

Evaluation

The following commands are required to evaluate Recall and SDM separately.

cd checkpoints/<name>
python test.py --name <name> --test_dir <dir/to/testdir/of/dataset> --gpu_ids 0 --num_worker 4

the <name> is the dir name in your training setting, you can find in the checkpoints/.

For Recall

python evaluate_gpu.py

For SDM

python evaluateDistance.py --root_dir <dir/to/root/of/dataset>

We also provide the baseline checkpoints, quark one-drive.

unzip <file.zip> -d checkpoints
cd checkpoints/baseline
python test.py --test_dir <dataset_root>/test
python evaluate_gpu.py
python evaluateDistance.py --root_dir <dataset_root>

Supported Methods

Augment	Backbone	Head	Loss
Random Rotate	ResNet	MaxPool	CrossEntropy Loss.
Random Affine	EfficientNet	AvgPool	Focal Loss
Random Brightness	ConvNext	MaxAvgPool	Triplet Loss
Random Erasing	DeiT	GlobalPool	Hard-Mining Triplet Loss
	PvT	GemPool	Same-Domain Triplet Loss
	SwinTransformer	LPN	Soft-Weighted Triplet Loss
	ViT	FSRA	KL Loss

License

This project is licensed under the Apache 2.0 license.

Citation

The following paper uses and reports the result of the baseline model. You may cite it in your paper.

@ARTICLE{DenseUAV,
  author={Dai, Ming and Zheng, Enhui and Feng, Zhenhua and Qi, Lei and Zhuang, Jiedong and Yang, Wankou},
  journal={IEEE Transactions on Image Processing},
  title={Vision-Based UAV Self-Positioning in Low-Altitude Urban Environments},
  year={2024},
  volume={33},
  number={},
  pages={493-508},
  doi={10.1109/TIP.2023.3346279}}

Related Work

University-1652 https://github.com/layumi/University1652-Baseline
FSRA https://github.com/Dmmm1997/FSRA

Dmmm1997 / DenseUAV