FCRN: Fast contamination recognition network

Fast contamination recognition network: a knowledge distillation based hyperspectral outdoor insulator contamination level recognition network

Junbo Zhou, Guoqiang Gao*, Yujun Guo, Pu Zhang, Kangle Wang, Xueqin Zhang, Song Xiao, Guangning Wu

Southwest Jiaotong University, *Corresponding author

Overview

FCRN is a novel knowledge distillation based hyperspectral outdoor insulator contamination level recognition network. It effectively integrates state-of-the-art knowledge distillation methods to fulfill the growing demand for lightweight yet powerful insulator contamination level recognition models.

How to start

Installation

Step 1: Cloning the repository

git clone https://github.com/IIMARSS/FCRN.git

Step 2: Environment setup
Following the requirements and installing the packages.

pip install -r requirements.txt

Data preparation

Download the insulator contamination HSI datasets from Google Drive or Baidu Drive and put them under the [Datasets] folder. Of course, you can use the public HSI datasets or your own datasets as well. It will have the following structure:

${DATASET_ROOT}   # Dataset root directory
├── Datasets
│   │
│   ├── xianchang1        # Sunny condition
│   │   ├──xianchang1_2.mat
│   │   ├──xianchang1_2_gt.mat
│   │
│   ├── xianchang2  # Cloudy condition
│   │   ├──xianchang2.mat
│   │   ├──xianchang2_gt.mat
│   │
│   ├── ip         # Indian Pines data	
│   │   ├──Indian_pines_corrected.mat
│   │   ├──Indian_pines_gt.mat 
│   │
│   ├── other HSI Datasets   
│   │   ├ ... 
│   │    

Running the code

python main.py --model kd --dataset xianchang1

Reference

If you find our model is useful for your research, please cite our paper! We are very grateful for your support!❤️❤️

@article{zhou2025fast,
  title={Fast contamination recognition network: a knowledge distillation based hyperspectral outdoor insulator contamination level recognition network},
  author={Zhou, Junbo and Gao, Guoqiang and Guo, Yujun and Zhang, Pu and Wang, Kangle and Zhang, Xueqin and Xiao, Song and Wu, Guangning},
  journal={Expert Systems with Applications},
  pages={130401},
  year={2025},
  publisher={Elsevier}
}

Questions

If you have any questions, please let me know!

Thanks

Our project is based on DeepHyperX. Thanks for the wonderful work.🌹🌹