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
This repo	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
Current improvement	65.33	84.89	77.07	38.55	67.88	61.78	51.86	56.22	89.82	75.77	76.30	53.68	63.25	63.85	65.05	53.99

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

kuan-li / r2cnn_faster-rcnn_tensorflow Goto Github PK