DRENet: Fast and Accurate Tiny Ship detection Method

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This repo is the official implementation of the DRENet in “A Degraded Reconstruction Enhancement-based Method for Tiny Ship Detection in Remote Sensing Images with A New Large-scale Dataset” [IEEE | Lab Server]. (Accepted by TGRS 2022)

If you encounter any question, please feel free to contact us. You can create an issue or just send email to me [email protected]. Also welcome for any idea exchange and discussion.



The code cleanup is finished and the complete codes are provided, also the weights of our model on LEVIR-Ship dataset.


We will finish the code cleanup within a week, and make both the code and dataset fully public. Please be patient.

Table of Contents


Our Network Structure

We focus on tiny ship detection task in medium-resolution (MR, about 16m/pixel) remote sensing (RS) images . Compared with high-resolution (HR) RS image, an MR image covers a much wider area, thus facilitating quick ship detection. This direction has great research significance, and can greatly benefit the rapid ship detection under massive RS images.

For the task, we propose an effective Degraded Reconstruction Enhancement Network (DRENet), where a degraded reconstruction enhancer is designed to learn to regress an object-aware blurred version of the input image. Our method achieves both great effectiveness and efficiency, and outperforms many recent methods.

Results and Trained Model

Models trained on LEVIR-Ship dataset

Methods Params(M) FLOPs(G) AP FPS
YOLOv3 61.52 99.2 69.9 61
YOLOv5s 7.05 10.4 75.6 [Google Drive | Baidu Pan (code:ogdm)] 95
Retinanet 36.33 104.4 74.9 12
SSD 24.39 175.2 52.6 25
FasterRCNN 136.70 299.2 70.8 10
EfficientDet-D0 3.84 4.6 71.3 32
EfficientDet-D2 8.01 20.0 80.9 21
FCOS 5.92 51.8 75.5 37
CenterNet 191.24 584.6 77.7 25
HSFNet 157.59 538.1 73.6 7
ImYOLOv3 62.86 101.9 72.6 51
MaskRCNN+DFR+RFE 24.99 237.8 76.2 6
DRENet 4.79 8.3 82.4 [Google Drive | Baidu Pan (code:x710)] 85


Please at first download dataset LEVIR-Ship, then prepare the dataset as the following structure:

├── train
        ├── images
            ├── img_1.png
            ├── img_2.png
            ├── ...
        ├── degrade  
        # images processed by Selective Degradation (refer to our paper for detals)
            ├── degraded_img_1.png
            ├── degraded_img_2.png
            ├── ...
        ├── labels
            ├── label_1.txt
            ├── label_2.txt
            ├── ...
├── val
├── test

Note that apart from the images and labels in LEVIR-Ship dataset, you should also generate the degraded images, which are the supervision of the enhancer (see details in our paper). Here, we provide DegradeGenerate.py to easily generate the degraded images.

After preparing the dataset as above, change the paths in ship.yaml.

(The partitioned dataset, including the degraded images, can all be accessed here)


  • Windows/Linux Support

  • python 3.8

  • pytorch 1.9.0

  • torchvision

  • wandb (Suggested, a good tool to visualize the training process. If not want to use it, you should comment out the related codes.)

  • …… (See more details in requirements.txt)

    (The code is constructed based on YOLOv5s, for more details about YOLOv5, please refer to their repo here.)

Run Details

Train Process

To train our DRENet, run:

python train.py --cfg "./models/DRENet.yaml" --epochs 1000 --workers 8 --batch-size 16 --device 0 --project "./LEVIR-Ship" --data "./data/ship.yaml"

Parameters Description

  • cfg: You can change it to use different network structures. More structure configurations can be found in models directory, where we provide the baseline YOLOv5s, and the ablation structures of DRENet. You can try them if you are interested
  • epochs: A longer training time is suggested, and 1,000 epochs are enough.
  • project: The path where you want to save your experiments. Also the name of the project in wandb.


The current codes use fixed weight balance, which can also achieve a good result.

If you want to make use of automatic weight balance, please search the key word weightOptimizer in train.py and uncomment the code lines, also the code lines with the key word ForAuto in loss.py be uncommented and the other lines be commented out.

Test Process

To test our DRENet, you should first train the network or download our provided weights, then run:

python test.py --weights "./DRENet.pt" --project "runs/test" --device 0 --batch-size 16 --data "./data/ship.yaml"

You can set how many detected results to plot by changing the value of plot_batch_num in test.py.


If you find this paper useful in your research, please consider citing:

  author={Chen, Jianqi and Chen, Keyan and Chen, Hao and Zou, Zhengxia and Shi, Zhenwei},
  journal={IEEE Transactions on Geoscience and Remote Sensing},
  title={A Degraded Reconstruction Enhancement-based Method for Tiny Ship Detection in Remote Sensing Images with A New Large-scale Dataset},


This project is licensed under the GPL-3.0 License. See LICENSE for details


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