R2CNN_Faster-RCNN_Tensorflow

Abstract

This is a tensorflow re-implementation of R²CNN: Rotational Region CNN for Orientation Robust Scene Text Detection.
It should be noted that we did not re-implementate exactly as the paper and just adopted its idea.

This project is based on Faster-RCNN, and completed by YangXue and YangJirui.

Status

Demo available !

Citation

Some relevant achievements based on this code.

@article{https://arxiv.org/abs/1806.04828
    Author = {Xue Yang, Hao Sun, Xian Sun, Menglong Yan, Zhi Guo, Kun Fu},
    Title = {Position Detection and Direction Prediction for Arbitrary-Oriented Ships via Multiscale Rotation Region Convolutional Neural Network},
    Year = {2018}
} 

@article{yangxue_r-dfpn:http://www.mdpi.com/2072-4292/10/1/132 or https://arxiv.org/abs/1806.04331
    Author = {Xue Yang, Hao Sun, Kun Fu, Jirui Yang, Xian Sun, Menglong Yan and Zhi Guo},
    Title = {{R-DFPN}: Automatic Ship Detection in Remote Sensing Images from Google Earth of Complex Scenes Based on Multiscale Rotation Dense Feature Pyramid Networks},
    Journal = {Published in remote sensing},
    Year = {2018}
}

DOTA test results

Comparison

Approaches	mAP	PL	BD	BR	GTF	SV	LV	SH	TC	BC	ST	SBF	RA	HA	SP	HC
SSD inception-v2	17.84	41.06	24.31	4.55	17.1	15.93	7.72	13.21	39.96	12.05	46.88	9.09	30.82	1.36	3.5	0.0
YOLOv2	25.492	52.75	24.24	10.6	35.5	14.36	2.41	7.37	51.79	43.98	31.35	22.3	36.68	14.61	22.55	11.89
R-FCN	30.84	39.57	46.13	3.03	38.46	9.1	3.66	7.45	41.97	50.43	66.98	40.34	51.28	11.14	35.59	17.45
FR-H	39.95	49.74	64.22	9.38	56.66	19.18	14.17	9.51	61.61	65.47	57.52	51.36	49.41	20.8	45.84	24.38
FR-O	54.13	79.42	77.13	17.7	64.05	35.3	38.02	37.16	89.41	69.64	59.28	50.3	52.91	47.89	47.4	46.3
Ours	60.67	80.94	65.75	35.34	67.44	59.92	50.91	55.81	90.67	66.92	72.39	55.06	52.23	55.14	53.35	48.22

Face Detection

Environment: NVIDIA GeForce GTX 1060

Requirements

1、tensorflow >= 1.2
2、cuda8.0
3、python2.7 (anaconda2 recommend)
4、opencv(cv2)

Download Model

1、please download resnet50_v1、resnet101_v1 pre-trained models on Imagenet, put it to data/pretrained_weights.
2、please download mobilenet_v2 pre-trained model on Imagenet, put it to data/pretrained_weights/mobilenet.
3、please download trained model by this project, put it to output/trained_weights.

Compile

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

Demo(available)

Select a configuration file in the folder (libs/configs/) and copy its contents into cfgs.py, then download the corresponding weights.

DOTA:

python demo.py --src_folder='/PATH/TO/DOTA/IMAGES_ORIGINAL/' 
               --image_ext='.png' 
               --des_folder='/PATH/TO/SAVE/RESULTS/' 
               --save_res=True
               --gpu='0'

Face:

python camera_demo.py --gpu='0'

Eval(available)

python eval.py --img_dir='/PATH/TO/DOTA/IMAGES/' 
               --image_ext='.png' 
               --test_annotation_path='/PATH/TO/TEST/ANNOTATION/'

Inference(available)

python inference.py --data_dir='/PATH/TO/DOTA/IMAGES_CROP/'

Train

python train.py

guojiapeng00/R2CNN_Faster-RCNN_Tensorflow