ECCV_AERIS

Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object Detection [paper]

News:

Upload degradation generation file data_gen.py, you could generate your own degradation data.

Abstract:

Image restoration algorithms such as super resolution (SR) are indispensable pre-processing modules for object detection in low quality images. Most of these algorithms assume the degradation is fixed and known a priori. However, in practical, either the real degradation or optimal up-sampling ratio rate is unknown or differs from assumption, leading to a deteriorating performance for both the pre-processing module and the consequent high-level task such as object detection. Here, we propose a novel self-supervised framework to detect objects in degraded low resolution images. We utilizes the downsampling degradation as a kind of transformation for self-supervised signals to explore the equivariant representation against various resolutions and other degradation conditions. The Auto Encoding Resolution in Self-supervision (AERIS) framework could further take the advantage of advanced SR architec- tures with an arbitrary resolution restoring decoder to reconstruct the original correspondence from the degraded input image. Both the rep- resentation learning and object detection are optimized jointly in an end-to-end training fashion. The generic AERIS framework could be implemented on various mainstream object detection architectures with different backbones. The extensive experiments show that our methods has achieved superior performance compared with existing methods when facing variant degradation situations.

Fig 1. The gap between recognition methods and restoration methods. Top line detection results build on the SOTA restoration methods BSRGAN, second line is the AERIS detection result.

Fig 2. The scale gap in object detection task. Larger image size is friendly to small object detection, smaller image size is friendly to large object detection.

Fig 3. We use AERIS to find the scale-invariant feature for object detection under low resolution.

Reference:

The work is build on mmdetection and kornia, thanks for their great project!

If you use this work in your research, please consider to cite:

@inproceedings{cui2022exploring,
  title={Exploring Resolution and Degradation Clues as Self-supervised Signal for Low Quality Object Detection},
  author={Cui, Ziteng and Zhu, Yingying and Gu, Lin and Qi, Guo-Jun and Li, Xiaoxiao and Zhang, Renrui and Zhang, Zenghui and Harada, Tatsuya},
  booktitle={European Conference on Computer Vision},
  pages={473--491},
  year={2022},
  organization={Springer}
}

Environment:

The code is base on mmdet 2.23.0, to use this code please following the steps build enviroment:

conda create -n AERIS python==3.7.0

conda activate AERIS

conda install --yes -c pytorch pytorch=1.7.0 torchvision cudatoolkit=11.0 (for other version, please follow [pytorch](https://pytorch.org/))

pip install mmcv-full==1.5.0 -f https://download.openmmlab.com/mmcv/dist/cu110/torch1.7.0/index.html (download mmcv with your cuda and torch version)

pip install -r requirements.txt

pip install -v -e .

Dataset:

COCO-d dataset: Translate COCO-val2017 with random noise/ blur/ down-sampling(1~4) resolution, details please refer to our paper.

1. Download the COCO dataset’s train2017, val2017 and train_val_label.

2. Download the COCO-d dataset. Dataset link: (g-drive) or (baiduyun, passwd:p604). Label link: g-drive or (baiduyun, passwd:iosl).

3. Place the COCO-d dataset into original COCO dataset, the format should like:

${COCO}
|-- annotations
    |-- instances_train2017.json
    |-- instances_val2017.json
    |-- instances_val2017_down1_4.json (COCO-d label)
|-- train2017
|-- val2017
|-- val2017_down1_4 (COCO-d image)

Specific Degradation Setting: TBD

Generate Degradation Dataset by Your Own: TBD

Model inference:

Model	Config	Download Link	AP	AP small	AP medium	AP large
CenterNet-Res18	config	g-drive	14.5	1.2	10.4	38.6
CenterNet-Res18-AERIS (w/o ARRD)	config	g-drive	17.6	2.5	15.9	41.4
CenterNet-Res18-AERIS (Stage1)	config	g-drive	18.2	2.6	16.2	43.2
CenterNet-Res18-AERIS (Stage2)	config	g-drive	18.4	2.7	16.4	46.5
CenterNet-SwinTiny	config	g-drive	19.9	2.7	16.9	46.2
CenterNet-SwinTiny-AERIS (Stage2)	config	g-drive	21.6	3.2	20.4	49.0

Config File:

centernet_res18.py: config of baseline CenterNet-ResNet model.

centernet_res18_woARRD.py: config of baseline CenterNet-ResNet-AERIS without the ARRD decoder.

centernet_res18_stage1.py: config of CenterNet-ResNet-AERIS with the ARRD decoder on stage 1.

centernet_res18_stage2.py: config of CenterNet-ResNet-AERIS with the ARRD decoder on stage 2.

centernet_swint.py: config of baseline CenterNet-Swin model.

centernet_swint_stage2.py: config of CenterNet-Swin-AERIS with the ARRD decoder on stage 2.

For the model testing, please use the following code. We show the CenterNet-Res18-AERIS (Stage2) testing for example:

python tools/test.py configs/centernet_AERIS/centernet_up_stage2.py "model_ckpt_path" --eval bbox

Model Training:

We trained our AERIS on 4 GPUs (with the initial learning rate 0.01), if you want use other GPU number, please change the learning rate.

We show the CenterNet-Res18-AERIS (Stage2) training for example:

CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 bash tools/dist_train.sh configs/centernet_AERIS/centernet_up_stage2.py 4

Other Related Research:

(1). Robust object detection under low-light condition:

ICCV 2021: Multitask AET with Orthogonal Tangent Regularity for Dark Object Detection (project)

(2). Fast low-light enhancement and exposure correction using Transformer:

BMVC 2022: You Only Need 90K Parameters to Adapt Light: A Light Weight Transformer for Image Enhancement and Exposure Correction (project)