Learning-Polar-Encodings-For-Arbitrary-Oriented-Ship-Detection-In-SAR-Images

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Learning-Polar-Encodings-For-Arbitrary-Oriented-Ship-Detection-In-SAR-Images

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# Polar-Encodings
Learning Polar Encodings for Arbitrary-Oriented Ship Detection in SAR Images

[Paper Link](https://ieeexplore.ieee.org/document/9385869)

# Introduction

Common horizontal bounding box (HBB)-based methods are not capable of accurately locating slender ship targets with arbitrary orientations in synthetic aperture radar (SAR) images. Therefore, in recent years, methods based on oriented bounding box (OBB) have gradually received attention from researchers. However, most of the recently proposed deep learning-based methods for OBB detection encounter the boundary discontinuity problem in angle or key point regression. In order to alleviate this problem, researchers propose to introduce some manually set parameters or extra network branches for distinguishing the boundary cases, which make training more difficult and lead to performance degradation. In this paper, in order to solve the boundary discontinuity problem in OBB regression, we propose to detect SAR ships by learning polar encodings. The encoding scheme uses a group of vectors pointing from the center of the ship target to the boundary points to represent an OBB. The boundary discontinuity problem is avoided by training and inference directly according to the polar encodings. In addition, we propose an Intersect over Union (IOU) -weighted regression loss, which further guides the training of polar encodings through the IOU metric and improves the detection performance. Experiments on the Rotating SAR Ship Detection Dataset (RSSDD) show that the proposed method can achieve better detection performance over other comparison algorithms and other OBB encoding schemes, demonstrating the effectiveness of our method.

# Dependencies

Ubuntu 18.04, Python 3.6.10, PyTorch 1.6.0, OpenCV-Python 4.3.0.36

# How to start
## DOTA-devkit
Download and install the DOTA development kit [DOTA_devkit](https://github.com/CAPTAIN-WHU/DOTA_devkit) and put it under datasets folder. Please uncomment the nn.BatchNorm2d(head_conv) in ctrbox_net.py to avoid NAN loss when training with a smaller batch size. Note that the current version of ctrbox_net.py matches the uploaded weights.

## Place data

The ssdd dataset can be placed and organized as:
```
data
--ssdd
----imageset(train/test txts, same as the VOC)
----annotations(xml annotations of ssdd)
----images(images from ssdd)
```
## Train the model
```ruby
python main.py --data_dir data/ssdd --num_epoch 120 --batch_size 8 --dataset ssdd --phase train --K 100
```

## Evaluate the model
You may adjust the conf_thresh to get a better mAP
```ruby
python main.py --data_dir data/ssdd --batch_size 16 --dataset ssdd --resume MODEL_TO_EVALUATE.pth --phase eval
```

## Draw results
```ruby
python main.py --data_dir data/ssdd --batch_size 16 --dataset ssdd --resume MODEL_TO_DRAW_RESULTS.pth --phase test
```

## RSSDD Annotation
Link：[BaiDuYun Disk](https://pan.baidu.com/s/18sCZ7-0hOzbc2N9aul9uBg )

Code：45r4

# Reference
1. [CenterNet](https://github.com/xingyizhou/CenterNet)
2. [BBAVectors](https://github.com/yijingru/BBAVectors-Oriented-Object-Detection)
3. [R-CenterNet](https://github.com/ZeroE04/R-CenterNet)
4. [MBB](https://bitbucket.org/william_rusnack/minimumboundingbox/src/master/)

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