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.
Demo available !
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
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 |
Environment: NVIDIA GeForce GTX 1060
1、tensorflow >= 1.2
2、cuda8.0
3、python2.7 (anaconda2 recommend)
4、opencv(cv2)
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.
Select a configuration file in the folder (libs/configs/) and copy its contents into cfgs.py, then download the corresponding weights.
python demo.py --src_folder='/PATH/TO/DOTA/IMAGES_ORIGINAL/'
--image_ext='.png'
--des_folder='/PATH/TO/SAVE/RESULTS/'
--save_res=True
--gpu='0'
python camera_demo.py --gpu='0'
python eval.py --img_dir='/PATH/TO/DOTA/IMAGES/'
--image_ext='.png'
--test_annotation_path='/PATH/TO/TEST/ANNOTATION/'
python inference.py --data_dir='/PATH/TO/DOTA/IMAGES_CROP/'
python train.py