Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions

Overview

Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions

Accepted by AAAI 2022 [arxiv]

Wenyu Liu, Gaofeng Ren, Runsheng Yu, Shi Guo, Jianke Zhu, Lei Zhang

image

Installation

git clone https://github.com/wenyyu/Image-Adaptive-YOLO.git  
cd Image-Adaptive-YOLO  
# Require python3 and tensorflow
pip install -r ./docs/requirements.txt

Datasets and Models

PSCAL VOC RTTS ExDark Voc_foggy_test & Voc_dark_test & Models (key: iayl)

Quick test

# put checkpoint model in the corresponding directory 
# change the data and model paths in core/config.py
python evaluate.py 

image

Train and Evaluate on the datasets

  1. Prepare the training and testing datasets, edit core/config.py to configure
python train.py # we trained our model from scratch.  
python evaluate.py   
cd mAP & python main.py 
  1. Train with your own dataset
    reference the implementation tensorflow-yolov3 to prepare the files.

Acknowledgments

The code is based on tensorflow-yolov3, exposure.

Citation

@inproceedings{liu2022imageadaptive,
  title={Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions},
  author={Liu, Wenyu and Ren, Gaofeng and Yu, Runsheng and Guo, Shi and Zhu, Jianke and Zhang, Lei},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  year={2022}
}
Owner
liuwenyu
PhD candidate in Zhejiang University.
liuwenyu
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