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Small-Object Detection in Remote Sensing (satellite) Images with End-to-End Edge-Enhanced GAN and Object Detector Network
| Date | Stars |
|---|---|
| 2026-07-24 | 315 |
| 2026-07-25 | 315 |
| 2026-07-28 | 315 |
| 2026-07-30 | 315 |
| 2026-08-06 | 315 |
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# EESRGAN
## Model Architecture
<img src="Detection_Results/overall_pipeline.PNG" width="100%"></img>
## Enhancement and Detection
|Low Resolution <br/> Image & Detection|Super Resolved <br/> Image & Detection|High Resolution Ground Truth <br/> Image & Bounding Box|
| --- | --- | --- |
|<img src="Detection_Results/1_LR.jpg" width="256" height="256"></img>|<img src="Detection_Results/1_SR.jpg" width="256" height="256"></img>|<img src="Detection_Results/1_GT.jpg" width="256" height="256"></img>|
|<img src="Detection_Results/1_LR_detection_new.jpg" width="256" height="256"></img>|<img src="Detection_Results/1_SR_detection.jpg" width="256" height="256"></img>|<img src="Detection_Results/1_GT_box.jpg" width="256" height="256"></img>|
|<img src="Detection_Results/2_LR.jpg" width="256" height="256"></img>|<img src="Detection_Results/2_SR.jpg" width="256" height="256"></img>|<img src="Detection_Results/2_GT.jpg" width="256" height="256"></img>|
|<img src="Detection_Results/2_LR_detect_new.jpg" width="256" height="256"></img>|<img src="Detection_Results/2_SR_detection.jpg" width="256" height="256"></img>|<img src="Detection_Results/2_GT_box.jpg" width="256" height="256"></img>|
## Dependencies and Installation
- Python 3 (Recommend to use Anaconda)
- PyTorch >= 1.0
- NVIDIA GPU + CUDA
- Python packages: `pip install -r path/to/requirement.txt`
## Training
`python train.py -c config_GAN.json`
## Testing
`python test.py -c config_GAN.json`
## Dataset
Download dataset from [here.](https://gdo152.llnl.gov/cowc/download/cowc-m/datasets/)
[Here](https://github.com/LLNL/cowc/tree/master/COWC-M) is a GitHub repo to create custom image patches.
Download pre-made dataset from [here](https://gdo152.llnl.gov/cowc/download/cowc-m/datasets/DetectionPatches_256x256.tgz) and [this](https://github.com/Jakaria08/EESRGAN/blob/1f93130d8e99166e7bc4d1640329450feec9ff9c/scripts_for_datasets/scripts_GAN_HR-LR.py#L24) script can be used with pre-made dataset to create high/low-resolution and bicubic images. Make sure to copy annotation files (.txt) in the HR, LR and Bic folder.
## Edit the JSON File
The directory of the following JSON file is needed to be changed according to the user directory. For details see [config_GAN.json](https://github.com/Jakaria08/EESRGAN/blob/master/config_GAN.json) and pretrained weights are uploaded in [google drive](https://drive.google.com/drive/folders/15xN_TKKTUpQ5EVdZWJ2aZUa4Y-u-Mt0f?usp=sharing)
```yaml
{
"data_loader": {
"type": "COWCGANFrcnnDataLoader",
"args":{
"data_dir_GT": "/Directory for High-Resolution Ground Truth images/",
"data_dir_LQ": "/Directory for 4x downsampled Low-Resolution images from the above High-Resolution images/"
}
},
"path": {
"models": "saved/save_your_model_in_this_directory/",
"pretrain_model_G": "Pretrained_model_path_for_train_test/170000_G.pth",
"pretrain_model_D": "Pretrained_model_path_for_train_test/170000_G.pth",
"pretrain_model_FRCNN": "Pretrained_model_path_for_train_test/170000_G.pth",
"data_dir_Valid": "/Low_resoluton_test_validation_image_directory/"
"Test_Result_SR": "Directory_to_store_test_results/"
}
}
```
## Paper
Find the published version on [Remote Sensing](https://www.mdpi.com/2072-4292/12/9/1432).
Find the preprints of the related paper on [preprints.org](https://www.preprints.org/manuscript/202003.0313/v1), [arxiv.org](https://arxiv.org/abs/2003.09085) and [researchgate.net](https://www.researchgate.net/publication/340095015_Small-Object_Detection_in_Remote_Sensing_Images_with_End-to-End_Edge-Enhanced_GAN_and_Object_Detector_Network).
### Abstract
The detection performance of small objects in remote sensing images has not been satisfactory compared to large objects, especially in low-resolution and noisy images. A generative adversarial network (GAN)-based model called enhanced super-resolution GAN (ESRGAN) showed remarkable image enhancement performance, bExcerpt of 6,290 characters
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matched fp:1a2e5ea673da90ea, topic:object-detection, desc:object detection, readme:object detection
matched fp:1a2e5ea673da90ea, topic:super-resolution, readme:super resolution
matched fp:1a2e5ea673da90ea, topic:dataset, readme:dataset, readme:datasets