R2CNN: Rotational Region CNN for Orientation Robust Scene Detection

Recommend improved code： https://github.com/DetectionTeamUCAS

A Tensorflow implementation of FPN or R2CNN detection framework based on FPN.
You can refer to the papers R2CNN Rotational Region CNN for Orientation Robust Scene Text Detection or Feature Pyramid Networks for Object Detection
Other rotation detection method reference R-DFPN, RRPN and R2CNN_HEAD
If useful to you, please star to support my work. Thanks.

Citation

Some relevant achievements based on this code.

@article{[yang2018position](https://ieeexplore.ieee.org/document/8464244),
	title={Position Detection and Direction Prediction for Arbitrary-Oriented Ships via Multitask Rotation Region Convolutional Neural Network},
	author={Yang, Xue and Sun, Hao and Sun, Xian and  Yan, Menglong and Guo, Zhi and Fu, Kun},
	journal={IEEE Access},
	volume={6},
	pages={50839-50849},
	year={2018},
	publisher={IEEE}
}

@article{[yang2018r-dfpn](http://www.mdpi.com/2072-4292/10/1/132),
	title={Automatic ship detection in remote sensing images from google earth of complex scenes based on multiscale rotation dense feature pyramid networks},
	author={Yang, Xue and Sun, Hao and Fu, Kun and Yang, Jirui and Sun, Xian and Yan, Menglong and Guo, Zhi},
	journal={Remote Sensing},
	volume={10},
	number={1},
	pages={132},
	year={2018},
	publisher={Multidisciplinary Digital Publishing Institute}
} 

Configuration Environment

ubuntu(Encoding problems may occur on windows) + python2 + tensorflow1.2 + cv2 + cuda8.0 + GeForce GTX 1080
If you want to use cpu, you need to modify the parameters of NMS and IOU functions use_gpu = False in cfgs.py
You can also use docker environment, command: docker pull yangxue2docker/tensorflow3_gpu_cv2_sshd:v1.0

Installation

Clone the repository

git clone https://github.com/yangxue0827/R2CNN_FPN_Tensorflow.git    

Make tfrecord

The data is VOC format, reference here
Data path format ($R2CNN_ROOT/data/io/divide_data.py)

├── VOCdevkit
│   ├── VOCdevkit_train
│       ├── Annotation
│       ├── JPEGImages
│    ├── VOCdevkit_test
│       ├── Annotation
│       ├── JPEGImages

Clone the repository

cd $R2CNN_ROOT/data/io/  
python convert_data_to_tfrecord.py --VOC_dir='***/VOCdevkit/VOCdevkit_train/' --save_name='train' --img_format='.jpg' --dataset='ship'
     

Compile

cd $PATH_ROOT/libs/box_utils/
python setup.py build_ext --inplace

##Demo
1、Unzip the weight $R2CNN_ROOT/output/res101_trained_weights/*.rar
2、put images in $R2CNN_ROOT/tools/inference_image
3、Configure parameters in $R2CNN_ROOT/libs/configs/cfgs.py and modify the project's root directory
4、

cd $R2CNN_ROOT/tools      

5、image slice

python inference1.py   

6、large image

cd $FPN_ROOT/tools
python demo1.py --src_folder=.\demo_src --des_folder=.\demo_des         

Train

1、Modify $R2CNN_ROOT/libs/lable_name_dict/***_dict.py, corresponding to the number of categories in the configuration file
2、download pretrain weight(resnet_v1_101_2016_08_28.tar.gz or resnet_v1_50_2016_08_28.tar.gz) from here, then extract to folder $R2CNN_ROOT/data/pretrained_weights
3、

cd $R2CNN_ROOT/tools      

4、Choose a model(FPN or R2CNN))
If you want to train FPN :

python train.py   

elif you want to train R2CNN:

python train1.py   

Test tfrecord

cd $R2CNN_ROOT/tools   
python test.py(test1.py)   

eval(Not recommended, Please refer here

cd $R2CNN_ROOT/tools   
python eval.py(eval1.py)  

Summary

tensorboard --logdir=$R2CNN_ROOT/output/res101_summary/ 

Graph

icdar2015 test results

Test results