DetectionTeamUCAS/R2CNN_Faster-RCNN_TensorflowPublic

NotificationsYou must be signed in to change notification settings
Fork188
Star588

Rotational region detection based on Faster-RCNN.

License

MIT license

588 stars 188 forks Branches Tags Activity

Star

Notifications

You must be signed in to change notification settings

Branches Tags

Folders and files

Name		Name	Last commit message	Last commit date
Latest commit History 62 Commits
data		data
help_utils		help_utils
libs		libs
output/trained_weights		output/trained_weights
tools		tools
.gitignore		.gitignore
DOTA.png		DOTA.png
LICENSE		LICENSE
README.md		README.md
__init__.py		__init__.py
face.gif		face.gif

Repository files navigation

R2CNN_Faster_RCNN_Tensorflow

Abstract

This is a tensorflow re-implementation ofR²CNN: 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 onFaster-RCNN, and completed byYangXue andYangJirui.

DOTA test results

Comparison

Part of the results are fromDOTA 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
RRPN	61.01	88.52	71.20	31.66	59.30	51.85	56.19	57.25	90.81	72.84	67.38	56.69	52.84	53.08	51.94	53.58
ICN	68.20	81.40	74.30	47.70	70.30	64.90	67.80	70.00	90.80	79.10	78.20	53.60	62.90	67.00	64.20	50.20
R²CNN++	71.16	89.66	81.22	45.50	75.10	68.27	60.17	66.83	90.90	80.69	86.15	64.05	63.48	65.34	68.01	62.05

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	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-	-
FPN	72.00	88.70	75.10	52.60	59.20	69.40	78.80	84.50	90.60	81.30	82.60	52.50	62.10	76.60	66.30	60.10
ICN	72.50	90.00	77.70	53.40	73.30	73.50	65.00	78.20	90.80	79.10	84.80	57.20	62.10	73.50	70.20	58.10
R²CNN++	75.35	90.18	81.88	55.30	73.29	72.09	77.65	78.06	90.91	82.44	86.39	64.53	63.45	75.77	78.21	60.11

Face Detection

Environment: NVIDIA GeForce GTX 1060

ICDAR2015

Requirements

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

Download Model

1、please downloadresnet50_v1、resnet101_v1 pre-trained models on Imagenet, put it to data/pretrained_weights.
2、please downloadmobilenet_v2 pre-trained model on Imagenet, put it to data/pretrained_weights/mobilenet.
3、please downloadtrained model by this project, put it to output/trained_weights.

Data Prepare

1、please downloadDOTA
2、crop data, reference:

cd $PATH_ROOT/data/io/DOTApython 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_utilspython 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 correspondingweights.

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/toolspython train.py

Tensorboard

cd $PATH_ROOT/output/summarytensorboard --logdir=.

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}}

About

Rotational region detection based on Faster-RCNN.

Releases

No releases published

Packages

No packages published

Movatterモバイル変換

License

DetectionTeamUCAS/R2CNN_Faster-RCNN_Tensorflow

Folders and files

Latest commit

History

Repository files navigation

R2CNN_Faster_RCNN_Tensorflow

Abstract

DOTA test results

Comparison

Task1 - Oriented Leaderboard

Task2 - Horizontal Leaderboard

Face Detection

ICDAR2015

Requirements

Download Model

Data Prepare

Compile

Demo

DOTA

FDDB

Eval

Inference

Train

Tensorboard

Citation

About

Topics

Resources

License

Uh oh!

Stars

Watchers

Forks

Releases

Packages0

Uh oh!

Languages

Packages