WXSOD: A Benchmark for Robust Salient Object Detection in Adverse Weather Conditions

(Under Review)

(https://arxiv.org/abs/2508.12250v1)

(We hope this paper will be accepted 🤗)

Quan Chen,  Xiong Yang,  Rongfeng Lu,  Qianyu Zhang,  Yu Liu,  Xiaofei Zhou,  Bolun Zheng

Hangzhou Dianzi University, Tsinghua University, Jiaxing University

• Part I: WXSOD Dataset
• Part II: Benchmark Results
• Part III: Train and Test
• Part IV: Pre-trained Checkpoints

📧 Contact

I am actively seeking academic collaboration. If you’re interested in collaborating or would like to connect, feel free to reach out 😊.

• Email: chenquan@alu.hdu.edu.cn
• WeChat: cq1045333951

📚 Table of contents

• 🌟 Dataset Highlights
• 💾 Dataset Access
• 📁 Dataset Structure
• 🔥 Benchmark Results
• 🛠️ Requirements
• 🚀 Train and Test
• 🤗 Pre-trained Checkpoints
• 🎫 License
• 🙏 Acknowledgments
• 📌 Citation

🌟 Dataset Highlights

WXSOD dataset provides a large-scale dataset (14,945 RGB images) for salient object detection under extreme weather conditions. Distinguishing itself from existing RGB-SOD benchmarks, it provides images with ​​diverse degradation​​ patterns and ​​pixel-wise annotations​​. Our dataset contains:

• A synthetic training set consisting of 12,891 images, including 8 types of weather noise and a small amount of clean images

• A composite test set consisting of 1,500 images, including 8 types of weather noise and a small amount of clean images

• A real test set consisting of 554 images, including 5 types of weather noise

💾 Dataset Access

The WXSOD dataset is released in two ways:

| BaiduDisk | Google Drive |Hugging Face|

📁 Dataset Structure

├─ WXSOD_data
|   ├── train_sys/
|   |   └──input/
|   |       ├── 0001_light.jpg
|   |       └── ...
|   |   └── gt/
|   |       ├── 0001_light.jpg
|   |       └── ...
|   ├── test_sys/
|   |   └──input/
|   |       ├── 0004_clean.jpg
|   |       └── ...
|   |   └── gt/
|   |       ├── 0004_clean.jpg
|   |       └── ...
|   ├── test_real/
|   |   └──input/
|   |       ├── 0001_dark.jpg
|   |       └── ...
|   |   └── gt/
|   |       ├── 0001_dark.jpg
|   |       └── ...

🔥 Benchmark Results

The prediction results of 18 methods on WXSOD benchmark are available at Google Drive and BaiduDisk.

• Quantitative results are derived from the predicted image at the original resolution, while the MACs is measured on a 384×384 image.
• For a fair comparison, all models adopt the unified loss function and learning strategy, with default hyper-parameters.

🛠️ Requirements

• torch == 2.1.0+cu121
• timm == 1.0.11
• imgaug == 0.4.0
• pysodmetrics == 1.4.2

🚀 Train and Test

• Train the WFANet.

sh run.sh

• Generate saliency images based on the weights obtained during the training phase (or the weight we provide).

sh runtest.sh

• Calculate the quantitative values of WFANet's predicted images.

sh runEvaluation.sh

🤗 Pre-trained Checkpoints

Pre-training weights for PVTV2-b and WFANet need to be downloaded. The pre-trained weights of ResNet18 can be automatically downloaded through Timm. Remember to modify the weight path!

• The pre-trained backbone PVTV2-b is available at Google Drive and BaiduDisk.

• The pre-trained WFANet is available at Google Drive and BaiduDisk.

🎫 License

This project is licensed under the Apache 2.0 license.

🙏 Acknowledgments

The scenarios for synthesized data come from:

• DUTS
• DUT-O
• ECSSD
• HKU-IS
• VT5000

📌 Citation

If you find our repository useful for your research, please consider citing our paper:

@misc{chen2025wxsodbenchmarkrobustsalient,
      title={WXSOD: A Benchmark for Robust Salient Object Detection in Adverse Weather Conditions}, 
      author={Quan Chen and Xiong Yang and Rongfeng Lu and Qianyu Zhang and Yu Liu and Xiaofei Zhou and Bolun Zheng},
      year={2025},
      eprint={2508.12250},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2508.12250}, 
}