Accepted by AAAI 2022 [arxiv]
Wenyu Liu, Gaofeng Ren, Runsheng Yu, Shi Guo, Jianke Zhu, Lei Zhang
The image-adaptive filtering techniques used in the segmentation task can be found in our TCSVT 2023 paper.
"Improving Nighttime Driving-Scene Segmentation via Dual Image-adaptive Learnable Filters". [arxiv] [code]
$ git clone https://github.com/wenyyu/Image-Adaptive-YOLO.git
$ cd Image-Adaptive-YOLO
# Require python3 and tensorflow
$ pip install -r ./docs/requirements.txtPSCAL VOC RTTS ExDark
Voc_foggy_test & Voc_dark_test & Models: Google Drive, Baidu Netdisk (key: iayl)
# put checkpoint model in the corresponding directory
# change the data and model paths in core/config.py
$ python evaluate.py 
- Download VOC PASCAL trainval and test data
$ wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtrainval_06-Nov-2007.tar
$ wget http://host.robots.ox.ac.uk/pascal/VOC/voc2012/VOCtrainval_11-May-2012.tar
$ wget http://host.robots.ox.ac.uk/pascal/VOC/voc2007/VOCtest_06-Nov-2007.tar
Extract all of these tars into one directory and rename them, which should have the following basic structure.
VOC # path: /home/lwy/work/code/tensorflow-yolov3/data/VOC
├── test
| └──VOCdevkit
| └──VOC2007 (from VOCtest_06-Nov-2007.tar)
└── train
└──VOCdevkit
└──VOC2007 (from VOCtrainval_06-Nov-2007.tar)
└──VOC2012 (from VOCtrainval_11-May-2012.tar)
$ python scripts/voc_annotation.py
- Generate Voc_foggy_train and Voc_foggy_val dataset offline
# generate ten levels' foggy training images and val images, respectively
$ python ./core/data_make.py - Edit core/config.py to configure
--vocfog_traindata_dir' = '/data/vdd/liuwenyu/data_vocfog/train/JPEGImages/'
--vocfog_valdata_dir' = '/data/vdd/liuwenyu/data_vocfog/val/JPEGImages/'
--train_path = './data/dataset_fog/voc_norm_train.txt'
--test_path = './data/dataset_fog/voc_norm_test.txt'
--class_name = './data/classes/vocfog.names'
- Train and Evaluate
$ python train.py # we trained our model from scratch.
$ python evaluate.py
$ cd ./experiments/.../mAP & python main.py - More details of Preparing dataset or Train with your own dataset
reference the implementation tensorflow-yolov3.
The overall process is the same as above, run the *_lowlight.py to train or evaluate.
The code is based on tensorflow-yolov3, exposure.
@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}
}
@article{liu2022improving,
title={Improving Nighttime Driving-Scene Segmentation via Dual Image-adaptive Learnable Filters},
author={Liu, Wenyu and Li, Wentong and Zhu, Jianke and Cui, Miaomiao and Xie, Xuansong and Zhang, Lei},
journal={arXiv e-prints},
pages={arXiv--2207},
year={2022}
}