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PSGCNet: A Pyramidal Scale and Global Context Guided Network for Dense Object Counting in Remote Sensing Image

The overal framework architecture

The visualization on RSOC

The visualziation on CARPK

The visualization on crowd counting datasets

The quantitative result on RSOC

The quantitative result on CARPK and PUBCR+

The quantitative result on DroneCrowd

The quantitative result on crowd counting dataset

Code

Install dependencies

torch >= 1.0 torchvision opencv numpy scipy, all the dependencies can be easily installed by pip or conda

This code was tested with python 3.6

Train and Test

1、 Dowload Dataset

2、 Pre-Process Data (resize image and split train/validation)

python preprocess_dataset.py --origin_dir <directory of original data> --data_dir <directory of processed data>

3、 Train model (validate on single GTX Titan X)

python train.py --data_dir <directory of processed data> --save_dir <directory of log and model>

4、 Test Model

python test.py --data_dir <directory of processed data> --save_dir <directory of log and model>

The result is slightly influenced by the random seed, but fixing the random seed (have to set cuda_benchmark to False) will make training time extrodinary long, so sometimes you can get a slightly worse result than the reported result, but most of time you can get a better result than the reported one. If you find this code is useful, please give us a star and cite our paper, have fun.

5、 Training on ShanghaiTech Dataset

Change dataloader to crowd_sh.py

For shanghaitech a, you should set learning rate to 1e-6, and bg_ratio to 0.1

Paper: https://arxiv.org/abs/2012.03597v3

RSOC Dataset：https://pan.baidu.com/s/19hL7O1sP_u2r9LNRsFSjdA code：nwcx

or at the website https://drive.google.com/drive/my-drive but only including building subsets. Other three can be download at https://captain-whu.github.io/DOTA/ according to our provided filenames

CARPK dataset, PUCPR+ dataset: https://lafi.github.io/LPN/

DroneCrowd dataset: https://github.com/VisDrone/DroneCrowd

UCF-QNRF dataset: https://www.crcv.ucf.edu/data/ucf-qnrf/

ShanghaiTech dataset: http://pan.baidu.com/s/1nuAYslz

UCF_CC_50 dataset: https://www.crcv.ucf.edu/data/ucf-cc-50/

References

If you find the PSGCNet useful, please cite our paper. Thank you!

@article{gao2022psgcnet,
title={PSGCNet: A Pyramidal Scale and Global Context Guided Network for Dense Object Counting in Remote-Sensing Images},
author={Gao, Guangshuai and Liu, Qingjie and Hu, Zhenghui and Li, Lu and Wen, Qi and Wang, Yunhong},
journal={IEEE Transactions on Geoscience and Remote Sensing},
volume={60},
pages={1--12},
year={2022},
publisher={IEEE}
}