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zhangzhengde0225 / FINet

Foggy Insulator Network, Dataset and Code. Improved object detection network, synthetic fog, data augmentation, channel attention mechanism. Insulator & defect detection
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FINet

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This is the official implemtentation of Foggy Insulator Network in the paper FINet: An Insulator Dataset and Detection Benchmark Based on Synthetic Fog and Improved YOLOv5

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Fig.1 Graphical Abstract of FINet.

Highlights

  1. This project realizes and optimizes the fogging algorithm based on the dark channel prior, and the codes are availalbe now.

  2. We provides an datasets for Insulator Detection with about 13,700 images.

  3. The improved network SE-YOLOv5 realizes a more robust model in both sunny and foggy scenarios.

Getting Started

For FINet, the HAI framework is used to provide simple dataset download, training, evaluation, inference and deployment functions. To install hai, run:

pip install hepai
hai --version  # check the version

  1. Get Source Codes

    git clone https://github.com/zhangzhengde0225/FINet.git
cd FINet
pip install -r requirements.txt  # install dependencies

  2. Check dataset

    To check the dataset by drawing labels into images, run:

    python scripts/check_dataset.py
    [-s --source DATASET_PATH]  # [optional] Default: data/SFID_demo

  3. Train model

    To train a model, run:

    python train.py
    [-s --source DATASET_PATH]  # [optional] Default: data/SFID_demo
    [-w --weights WEIGHTS_PATH]  # [optional] Default: None
    [--epochs EPOCHS]  # [optional] Default: 3
    [--batch-size BATCH_SIZE]  # [optional] Default: 32
    [--device CPU/GPU]  # [optional] Default: GPU:0
    [--img-size IMAGE_SIZE]  # [optional] Default: 640

    After training, the model will be saved in runs/exp/weights/last.pt. You can train the model with your own dataset by modifying the --source parameter.

  4. Download Datasets and Trained Weights

    We released the Synthetic Foggy Insulator Dataset (SFID) and Trained logs & weights, download them by following command:

    python download.py [SFID|logs]  # Choice: SFID, logs
    [--save-dor SAVE_DIR]  # [optional] Default: current directory

    After SFID downloading, the dataset will be saved in data/SFID.

    After logs downloading, three trained logs and weights will be saved in runs/xx, and the runs/se_m_ep99_fogged/weights/best.pt will be used for evaluation by default.

    For other download ways and the previous UPID and CPLID datasets, please refer to docs/dataset.md.

  5. Evaluate

    After training or download trained weights, you can evaluate the model by running:

    # evaluate the model on the testset
python evaluate.py
    [--source DATASET_PATH]  # [optional] Default: data/SFID
    [--weights TRAINED_WEIGHTS]  # [optional] Deafult: runs/se_m_ep99_fogged/weights/best.pt

  6. Inference [TODO]

    The HAI provides simple way to deploy the FINet via docker and provides remote inference API, which can be used to detect insulators in images or videos.

    # Deploy the FINet in docker
hai deploy --name FINet --image zhangzhengde0225/finet:latest

python inference.py 
    [--source IMAGE_PATH]  # [optional] Default: data/SFID_demo/images/test/00400.jpg
    [--weights TRAINED_WEIGHTS]  # [optional] Deafult: runs/se_m_ep99_fogged/weights/best.pt
    [--device CPU/GPU]  # [optional] Default: GPU:0
    [--img-size IMAGE_SIZE]  # [optional] Default: 640

  7. Synthetic fog

    You can use the synthetic_fog.py to generate foggy images. The fogging algorithm is based on the dark channel prior described in our paper, and the codes are availalbe now.

    python scripts/synthetic_fog.py
    [-s --source INPUT_PATH]  # [optional] Default: data/SFID_demo/images/train/001040.jpg
    [--save-dir OUTPUT_PATH]  # [optional] Default: None, display it
    [--speed_up NEED_SPEED_UP]  # Default: False

Detailed tutorials are available in docs/tutorial.md.

Contributors

The FINet is authored by Zheng-De Zhang*, Bo Zhang,*, Zhi-Cai Lan, Hai-Chun Liu, Dong-Ying Li, Ling Pei and Wen-Xian Yu.

Currently, it is maintained by Zheng-De Zhang (zdzhang@ihep.ac.cn) and Bo Zhang (zhangbo20@sjtu.edu.cn)

If you have any questions, please new an issue or feel free contact us by email, thank you for your attention!

Please Star this project and Cite this paper if its helpful to you .

Citation

@article{FINet,
Title={FINet: An Insulator Dataset and Detection Benchmark Based on Synthetic Fog and Improved YOLOv5},
Author={Zheng-De Zhang, Bo Zhang, Zhi-Cai Lan, Hai-Chun Liu, Dong-Ying Li, Ling Pei and Wen-Xian Yu},
Journal={IEEE T INSTRUM MEAS},
DOI={10.1109/TIM.2022.3194909},
Year={2022},
Pages={1-8},
ISSN={0018-9456},
Online_ISSN={1557-9662},
}

License

THe FINet and it's datasets is freely available for non-commercial use, and may be redistributed under these conditions. For commercial queries, please drop an e-mail to zdzhang@ihep.a.cn, we will send the detail agreement to you.

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