Satellite-Based-Counting

Code for Drone-Based Car Counting via Density Map Learning (VCIP 2020) and Satellite-Based Object Counting via Adaptive Density Map Assisted Learning.

Pre-trained models

Google Drive

Baidu Cloud : 6svf

Environment

We are good in the environment:

python 3.6

CUDA 9.2

Pytorch 1.2.0

numpy 1.19.2

matplotlib 3.3.4

nni 2.6.1 (Optional)

Usage

We provide the test code for our model. The adml_small_vehicle.pth model is adapted on the RSOC_small-vehicle dataset. We randomly select an image from the RSOC_small-vehicle dataset and place it in the image folder. And you can either choose the other images for a test.

We are good to run:

python test.py --model ADML --mode DME --model_state ./model/adml_small_vehicle.pth --out ./out/out.png

We will release more trained models soon. The core code will be released after the journal paper is accepted. Please see the paper for more details.

Data set

We propose a Tree data set, The download link is:

Baidu Cloud : Tree

We have only shared the training and validation set images and annotations. If you are interested in this data set, please contact us (Email address at the bottom) for a test set.

Citation (We copy the information from DBLP)

@article{DBLP:journals/tgrs/DingCYWWZ22,
  author    = {Guanchen Ding and
               Mingpeng Cui and
               Daiqin Yang and
               Tao Wang and
               Sihan Wang and
               Yunfei Zhang},
  title     = {Object Counting for Remote-Sensing Images via Adaptive Density Map-Assisted
               Learning},
  journal   = {{IEEE} Trans. Geosci. Remote. Sens.},
  volume    = {60},
  pages     = {1--11},
  year      = {2022},
  url       = {https://doi.org/10.1109/TGRS.2022.3208326},
  doi       = {10.1109/TGRS.2022.3208326},
  timestamp = {Sun, 13 Nov 2022 17:52:29 +0100},
  biburl    = {https://dblp.org/rec/journals/tgrs/DingCYWWZ22.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

@inproceedings{DBLP:conf/vcip/HuangDGYWWZ20,
  author    = {Jingxian Huang and
               Guanchen Ding and
               Yujia Guo and
               Daiqin Yang and
               Sihan Wang and
               Tao Wang and
               Yunfei Zhang},
  title     = {Drone-Based Car Counting via Density Map Learning},
  booktitle = {2020 {IEEE} International Conference on Visual Communications and
               Image Processing, {VCIP} 2020, Macau, China, December 1-4, 2020},
  pages     = {239--242},
  publisher = {{IEEE}},
  year      = {2020},
  url       = {https://doi.org/10.1109/VCIP49819.2020.9301785},
  doi       = {10.1109/VCIP49819.2020.9301785},
  timestamp = {Wed, 27 Jan 2021 14:35:06 +0100},
  biburl    = {https://dblp.org/rec/conf/vcip/HuangDGYWWZ20.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}

Acknowledgement

Thanks to these repositories

If you have any question, please feel free to contact us. (gcding@whu.edu.cn and ceoilmp@whu.edu.cn)