This code is to replicate the expeirments from the paper Improving Training Instance Quality in Aerial Image Object Detection with A Sampling-balance based Multi-stage Network
This network is built based on Pytorch 1.1 and MMdetection v1.0rc1
Please refer to INSTALL.md to install the MMdetection framework. It should be noted that a correct version of MMdetection should be download first, which can ensure the codes to be executable.
You should first prepare the used dataset as VOC format.
Three datasets NWPU-VHR10, DIOR, HRRSD are implemented in the branch. If you use one of them, you can set
dataset_type = 'VOCDataset' # for NWPU10
dataset_type = 'HRRSD_Dataset' # for HRRSD dataset_type = 'DOIR_Dataset' # for DIOR
The codes can repreduce the expeiremnts in the paper.
3. Some prediction examples of the proposed method on the NWPU VHR-10 data set (Green boxes are the correct predictions. Blue boxes are the false predictions. Red boxes are the missing predictions).
If you use this method in your research, please cite this paper.
@article{SBNet,
title = {Improving Training Instance Quality in Aerial Image Object
Detection With a Sampling-Balance-Based Multistage Network},
author = {Wei Han, Runyu Fan, Lizhe Wang, Ruyi Feng, Fengpeng Li,
Ze Deng, and Xiaodao Chen},
journal = {{IEEE} Trans. Geosci. Remote. Sens.},
doi = {10.1109/TGRS.2020.3038803},
year = {2020},
pages = {1-15}
}