Learning Calibrated-Guidance for Object Detection in Aerial Images

This codebase is created to build benchmarks for object detection in aerial images. It is modified from mmdetection. The master branch works with PyTorch 1.1 or higher. If you would like to use PyTorch 0.4.1, please checkout to the pytorch-0.4.1 branch.

Results

Visualization results for oriented object detection on the test set of DOTA.
all

Comparison to the baseline on DOTA for oriented object detection with ResNet-101. The figures with blue boxes are the results of the baseline and pink boxes are the results of our proposed CG-Net.
compare-1

Experiment

ImageNet Pretrained Model from Pytorch

The effectiveness of our proposed methods with different backbone network on the test of DOTA.

Backbone +CG Weight mAP(%)
ResNet-50 download 73.26
ResNet-50 + download 74.21
ResNet-101 download 73.06
ResNet-101 + download 74.30
ResNet-152 download 72.78
ResNet-152 + download 73.53

CG-Net Results in DOTA.

Backbone Aug Rotate Task Weight mAP(%)
ResNet-101 + Oriented download 77.89
ResNet-101 + Horizontal download 78.26

Citing

If you use our work, please consider citing:

@InProceedings{liang2021learning,
      title={Learning Calibrated-Guidance for Object Detection in Aerial Images}, 
      author={Dong, Liang and Zongqi, Wei and Dong, Zhang and Qixiang, Geng and Liyan, Zhang and Han, Sun and Huiyu, Zhou and Mingqiang, Wei and Pan, Gao},
      booktitle ={arXiv:2103.11399},
      year={2021}
}

GitHub

https://github.com/WeiZongqi/CG-Net