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https://github.com/wenyyu/Image-Adaptive-YOLO
The code for "Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions (AAAI 2022)"
https://github.com/wenyyu/Image-Adaptive-YOLO
Last synced: 12 days ago
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The code for "Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions (AAAI 2022)"
- Host: GitHub
- URL: https://github.com/wenyyu/Image-Adaptive-YOLO
- Owner: wenyyu
- License: apache-2.0
- Created: 2021-12-11T04:06:06.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2023-05-06T23:51:25.000Z (about 1 year ago)
- Last Synced: 2024-02-29T10:34:06.066Z (4 months ago)
- Language: Python
- Homepage:
- Size: 2.77 MB
- Stars: 437
- Watchers: 12
- Forks: 101
- Open Issues: 40
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Lists
- awesome-yolo-object-detection - Image-Adaptive YOLO - Adaptive-YOLO?style=social"/> : "Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions". (**[AAAI 2022](https://arxiv.org/abs/2112.08088)**). "计算机视觉研究院：《[图像自适应YOLO：模糊环境下的目标检测（附源代码）](https://mp.weixin.qq.com/s/QdM6Dx990VhN97MRIP74XA)》" (Applications)
README
# Image-Adaptive YOLO for Object Detection in Adverse Weather Conditions
#### Accepted by AAAI 2022 [[arxiv]](https://arxiv.org/abs/2112.08088)
Wenyu Liu, Gaofeng Ren, Runsheng Yu, Shi Guo, [Jianke Zhu](https://person.zju.edu.cn/jkzhu/645901.html), [Lei Zhang](https://web.comp.polyu.edu.hk/cslzhang/)
# Update
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]](https://arxiv.org/abs/2207.01331) [[code]]( https://github.com/wenyyu/IA-Seg)
# Installation
```bash
$ 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](http://host.robots.ox.ac.uk/pascal/VOC/) [RTTS](https://sites.google.com/view/reside-dehaze-datasets/reside-%CE%B2) [ExDark](https://github.com/cs-chan/Exclusively-Dark-Image-Dataset/tree/master/Dataset)
Voc_foggy_test & Voc_dark_test & Models: [Google Drive](https://drive.google.com/drive/folders/1P0leuiGHH69kVxyNVFuiCdCYXyYquPqM), [Baidu Netdisk](https://pan.baidu.com/s/1GQE_80rEzs0uCrzauHxwdw) (key: iayl)
# Quick test
```bash
# put checkpoint model in the corresponding directory
# change the data and model paths in core/config.py
$ python evaluate.py
```
# Train and Evaluate on the datasets
1. Download VOC PASCAL trainval and test data
```bashrc
$ 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.
```bashrc
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
```
2. Generate Voc_foggy_train and Voc_foggy_val dataset offline
```bash
# generate ten levels' foggy training images and val images, respectively
$ python ./core/data_make.py
```
3. Edit core/config.py to configure
```bashrc
--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'
```
4. Train and Evaluate
```bash
$ python train.py # we trained our model from scratch.
$ python evaluate.py
$ cd ./experiments/.../mAP & python main.py
```
5. More details of Preparing dataset or Train with your own dataset
reference the implementation [tensorflow-yolov3](https://github.com/YunYang1994/tensorflow-yolov3).
# Train and Evaluate on low_light images
The overall process is the same as above, run the *_lowlight.py to train or evaluate.
# Acknowledgments
The code is based on [tensorflow-yolov3](https://github.com/YunYang1994/tensorflow-yolov3), [exposure](https://github.com/yuanming-hu/exposure).
# Citation
```shell
@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}
}
```