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.

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

Part of the results are from DOTA paper.

Task1 - Oriented Leaderboard

Approaches	mAP	PL	BD	BR	GTF	SV	LV	SH	TC	BC	ST	SBF	RA	HA	SP	HC
SSD	10.59	39.83	9.09	0.64	13.18	0.26	0.39	1.11	16.24	27.57	9.23	27.16	9.09	3.03	1.05	1.01
YOLOv2	21.39	39.57	20.29	36.58	23.42	8.85	2.09	4.82	44.34	38.35	34.65	16.02	37.62	47.23	25.5	7.45
R-FCN	26.79	37.8	38.21	3.64	37.26	6.74	2.6	5.59	22.85	46.93	66.04	33.37	47.15	10.6	25.19	17.96
FR-H	36.29	47.16	61	9.8	51.74	14.87	12.8	6.88	56.26	59.97	57.32	47.83	48.7	8.23	37.25	23.05
FR-O	52.93	79.09	69.12	17.17	63.49	34.2	37.16	36.2	89.19	69.6	58.96	49.4	52.52	46.69	44.8	46.3
R2CNN	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	68.01	89.43	81.18	42.68	70.28	63.74	50.15	62.76	90.21	76.17	83.57	58.53	61.06	63.33	66.30	60.68

Task2 - Horizontal Leaderboard

Approaches	mAP	PL	BD	BR	GTF	SV	LV	SH	TC	BC	ST	SBF	RA	HA	SP	HC
SSD	10.94	44.74	11.21	6.22	6.91	2	10.24	11.34	15.59	12.56	17.94	14.73	4.55	4.55	0.53	1.01
YOLOv2	39.2	76.9	33.87	22.73	34.88	38.73	32.02	52.37	61.65	48.54	33.91	29.27	36.83	36.44	38.26	11.61
R-FCN	47.24	79.33	44.26	36.58	53.53	39.38	34.15	47.29	45.66	47.74	65.84	37.92	44.23	47.23	50.64	34.9
FR-H	60.46	80.32	77.55	32.86	68.13	53.66	52.49	50.04	90.41	75.05	59.59	57	49.81	61.69	56.46	41.85
R2CNN	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
Current improvement	72.80	89.85	80.90	51.91	69.29	70.72	73.52	77.90	90.64	77.28	84.28	59.51	60.17	75.02	74.64	56.37

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.

Data Prepare

1、please download DOTA
2、crop data, reference:

cd $PATH_ROOT/data/io/DOTA
python train_crop.py 
python val_crop.py

3、data format

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

Compile

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

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

Demo

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

DOTA

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

FDDB

python camera_demo.py --gpu='0'

Eval

python eval.py --img_dir='/PATH/TO/DOTA/IMAGES/' 
               --image_ext='.png' 
               --test_annotation_path='/PATH/TO/TEST/ANNOTATION/'
               --gpu='0'

Inference

python inference.py --data_dir='/PATH/TO/DOTA/IMAGES_CROP/'      
                    --gpu='0'

Train

1、If you want to train your own data, please note:

(1) Modify parameters (such as CLASS_NUM, DATASET_NAME, VERSION, etc.) in $PATH_ROOT/libs/configs/cfgs.py
(2) Add category information in $PATH_ROOT/libs/label_name_dict/lable_dict.py     
(3) Add data_name to line 75 of $PATH_ROOT/data/io/read_tfrecord.py

2、make tfrecord

cd $PATH_ROOT/data/io/  
python convert_data_to_tfrecord.py --VOC_dir='/PATH/TO/VOCdevkit/VOCdevkit_train/' 
                                   --xml_dir='Annotation'
                                   --image_dir='JPEGImages'
                                   --save_name='train' 
                                   --img_format='.png' 
                                   --dataset='DOTA'

3、train

cd $PATH_ROOT/tools
python train.py

Tensorboard

cd $PATH_ROOT/output/summary
tensorboard --logdir=.

nnpapers / r2cnn_faster-rcnn_tensorflow Goto Github PK