This project hosts the official implementation for the paper:
Gradient Calibration Loss for Fast and Accurate Oriented Bounding Box Regression [URL][PDF][BibTex]
( accepted by IEEE Transactions on Geoscience and Remote Sensing).
In this paper**,** we demonstrate several drawbacks of rotated IoU loss through both experiments and theoretical derivation. And then, a Gradient Calibration Loss (GCL**)** is propoosed to optimize the rotated IoU loss via gradient analysis and correction**.** GCL can be easily introduced into the existing rotation detectors to achieve performance gains without extra inference overhead**.**
conda create -n gcl python=3.7 -y
pip install torch==1.9.0+cu111 torchvision==0.10.0+cu111 torchaudio==0.9.0 -f https://download.pytorch.org/whl/torch_stable.html
cd BboxToolkit
pip install -v -e .
cd ..
pip install mmcv-full==1.3.9 -f https://download.openmmlab.com/mmcv/dist/cu111/torch1.9.0/index.html
cd mmdet/ops/point_justify
python setup.py develop
cd ../../..
pip install -r requirements/build.txt
pip install mmpycocotools
python setup.py develop
-
Creat config files.
-
Data preparation viia
cd BboxToolkit/tools/andpython img_split.py --base_json split_configs/dota1_0/ss_trainval.json -
Run
sh train.shorsh night.sh.
- Run
sh demo.shandsh test.sh.
- Results on DOTA-v1.0
- Results on DOTA-v1.5
If you find our work or code useful in your research, please consider citing:
@ARTICLE{ming2024gradient,
author={Ming, Qi and Miao, Lingjuan and Zhou, Zhiqiang and Song, Junjie and Pizurica, Aleksandra},
journal={IEEE Transactions on Geoscience and Remote Sensing},
title={Gradient Calibration Loss for Fast and Accurate Oriented Bounding Box Regression},
year={2024},
volume={62},
number={},
pages={1-15},
keywords={Object detection;Convergence;Remote sensing;Detectors;Training;Feature extraction;Proposals;Convolutional neural network;gradient analysis;loss function;oriented object detection},
doi={10.1109/TGRS.2024.3367294}}
Feel free to contact me if there are any questions.