CPA-Enhancer: Chain-of-Thought Prompted Adaptive Enhancer for Object Detection under Unknown Degradations

📰 ArXiv Preprint: Arxiv 2403.11220

✅ Updates

March. 24th, 2024: We have released the CPA-Seg for segmentation tasks of CPA-Enhancer.

🚀 Overview

Overview of the proposed CPA-Enhancer.

Our proposed content-driven prompt block (CPB).

Abstract : Object detection methods under known single degradations have been extensively investigated. However, existing approaches require prior knowledge of the degradation type and train a separate model for each, limiting their practical applications in unpredictable environments. To address this challenge, we propose a chain-of-thought (CoT) prompted adaptive enhancer, CPA-Enhancer, for object detection under unknown degradations. Specifically, CPA-Enhancer progressively adapts its enhancement strategy under the step-by-step guidance of CoT prompts, that encode degradation-related information. To the best of our knowledge, it’s the first work that exploits CoT prompting for object detection tasks. Overall, CPA-Enhancer is a plug-and-play enhancement model that can be integrated into any generic detectors to achieve substantial gains on degraded images, without knowing the degradation type priorly. Experimental results demonstrate that CPA-Enhancer not only sets the new state of the art for object detection but also boosts the performance of other downstream vision tasks under multiple unknown degradations.

🛠️ Installation

Step0. Download and install Miniconda from the official website.
Step1. Create a conda environment and activate it.

conda create --name openmmlab python=3.8 -y
conda activate openmmlab

Step2.Install PyTorch following official instructions, e.g.

conda install pytorch==1.11.0 torchvision==0.12.0 torchaudio==0.11.0 cudatoolkit=11.3 -c pytorch

Step3. Install MMEngine and MMCV using MIM.

pip install -U openmim
mim install mmengine
mim install "mmcv>=2.0.0"

Step4. Install other related packages.

cd CPA_Enhancer
pip install -r ./cpa/requirements.txt

📁 Data Preparation

Synthetic Datasets

Step1. 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

Step2. Construct VnA-T ( containing 5 categories, with a total of 8111 images) / VnB-T (containing 10 categories, with a total of 12334 images) from VOCtrainval_06-Nov-2007.tar and VOCtrainval_11-May-2012.tar; Construct VnA-T ( containing 5 categories, with a total of 2734 images) / VnB-T (containing 10 categories, with a total of 3760 images) from VOCtest_06-Nov-2007.tar.

We also provide a list of image names included in each dataset, which you can find in the cpa/dataSyn/datalist.

# 5 class
target_classes = ['person','car','bus','bicycle','motorbike']
# 10 class
target_classes = ['bicycle','boat','bottle','bus','car','cat','chair','dog','motorbike','person']

Make sure the directory follows this basic VOC structure.

data_vocnorm  (data_vocnorm_10) 	# path\to\vocnorm
├── train   # VnA-T (VnB-T)      
|    ├── Annotations
|    |    └──xxx.xml
|    |       ...
|    └── ImageSets
|    |    └──Main
|    |        └──train_voc.txt  # you can find it in cpa\dataSyn\datalist
|    └── JPEGImages
|         └──xxx.jpg
|            ...
├── test  # VnA (VnB)        
|    ├── Annotations
|    |    └──xxx.xml
|    |       ...
|    └── ImageSets
|    |    └──Main
|    |        └──test_voc.txt # you can find it in cpa\dataSyn\datalist
|    └── JPEGImages
|         └──xxx.jpg
|            ...

Step3. Sythesize degraded datasets from VnA and VnB by executing the following command and restructure them into VOC format.

# Modify the paths in the code to match your actual paths.
# all-in-one setting 
python cpa/dataSyn/data_make_fog.py   		# VF/VF-T 
python cpa/dataSyn/data_make_lowlight.py 	# VD/VD-T/VDB
python cpa/dataSyn/data_make_snow.py 		# VS/VS-T
python cpa/dataSyn/data_make_rain.py 		# VR/VR-T
# one-by-one setting 
python cpa/dataSyn/data_make_fog_hybrid.py		 	# VF-HT
python cpa/dataSyn/data_make_lowlight_hybrid.py 	# VD-HT

Real-world Datasets

Step1. Download Exdark and RTTS datasets.
Step2. Restructure the RTTS dataset (4322 images) into VOC format, ensuring that the directory conforms to this basic structure.

RTTS          # path\to\RTTS
├── Annotations
|    └──xxx.xml
|       ...
└── ImageSets
|    └──Main
|        └──test_rtts.txt
└── JPEGImages
     └──xxx.jpg
        ...

Step3. Similarly, restructure the ExdarkA dataset (containing 5 categories, with a total of 1283 images) and the ExdarkB dataset (containing 10 categories, with a total of 2563 images) into VOC format.

exdark_5 (exdark_10)         #  path\to\ExDarkA (ExDarkB)
├── Annotations
|    └──xxx.xml
|       ...
└── ImageSets
|    └──Main
|        └──test_exdark_5.txt (test_exdark_10.txt) # you can find it in cpa\dataSyn\datalist
└── JPEGImages
     └──xxx.jpg
        ...

🎯 Usage

📍 All-in-One Setting

Step 1. Modify the METAINFO in mmdet/datasets/voc.py

METAINFO = {
        'classes': ('person', 'car', 'bus', 'bicycle',  'motorbike'), # 5 classes
        'palette': [(106, 0, 228), (119, 11, 32), (165, 42, 42), (0, 0, 192),(197, 226, 255)]
    }

Step 2. Modify the voc_classes in mmdet/evaluation/functional/class_names.py

def voc_classes() -> list:
    return [
        'person', 'car', 'bus', 'bicycle',  'motorbike' # 5 classes
    ]

Step 3. Modify the num_classes in configs\yolo\cpa_config.py

bbox_head=dict(
        type='YOLOV3Head',
        num_classes=5, # 5 classes
				...
)

Step 4. Recompile the code.

cd CPA_Enhancer
pip install -v -e .

Step 5. Modify the data_root ,ann_fileand data_prefix in configs\yolo\cpa_config.py to match your actual paths of the used datasets.

The pretrained models and training/testing logs can be found in checkpoint.zip

🔹 Train

# Train our model from scratch.  
python tools/train.py configs/yolo/cpa_config.py

🔹 Test

# you can download our pretrained model for testing 
python tools/test.py configs/yolo/cpa_config.py path/to/checkpoint/xx.pth

🔹 Demo

# you can download our pretrained model for inference
python demo/cpa_demo.py \
	--inputs ../cpa/testimage  # path to your input images or dictionary
	--model ../configs/yolo/cpa_config.py 
	--weights path/to/checkpoint/xx.pth 
	--out-dir ../cpa/output # output file

📍 One-by-One Setting

For the foggy conditions (containing five categories), the overall process is the same as above (Step1-5).

For the low-light conditions ( containing ten categories ) , You only need to modify a few places as follows (Step1-3).

Step 1. Modify the METAINFO in mmdet/datasets/voc.py

# 10 classes
METAINFO = {
        'classes': ('bicycle', 'boat', 'bottle','bus', 'car', 'cat', 'chair','dog','motorbike','person'),
        'palette': [(106, 0, 228), (119, 11, 32), (165, 42, 42), (0, 0, 192),(197, 226, 255),
										(0, 60, 100), (0, 0, 142), (255, 77, 255), (153, 69, 1), (120, 166, 157),]
    }

Step 2. Modify the voc_classes in mmdet/evaluation/functional/class_names.py

def voc_classes() -> list:
    return [
        'bicycle', 'boat', 'bottle','bus', 'car', 'cat', 'chair','dog','motorbike','person' # 10 classes
    ]

Step 3. Modify the num_classes in configs/yolo/cpa_config.py

bbox_head=dict(
        type='YOLOV3Head',
        num_classes=10, # 10 classes
				...
)

📊 Results

Quantitative results

Quantitative comparisons under the all-in-one setting.

Comparisons in the one-by-one setting under the foggy degradation (left) and low-light degradation (right)

Visual Results

Visual comparisons of CPA-Enhancer under the all-in-one setting.

💐 Acknowledgments

Special thanks to the creators of mmdetection upon which this code is built, for their valuable work in advancing object detection research.

🔗 Citation

If you use this codebase, or CPA-Enhancer inspires your work, we would greatly appreciate it if you could star the repository and cite it using the following BibTeX entry.

@misc{zhang2024cpaenhancer,
      title={CPA-Enhancer: Chain-of-Thought Prompted Adaptive Enhancer for Object Detection under Unknown Degradations}, 
      author={Yuwei Zhang and Yan Wu and Yanming Liu and Xinyue Peng},
      year={2024},
      eprint={2403.11220},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}

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README.md

README.md

CPA-Enhancer: Chain-of-Thought Prompted Adaptive Enhancer for Object Detection under Unknown Degradations

✅ Updates

🚀 Overview

🛠️ Installation

📁 Data Preparation

Synthetic Datasets

Real-world Datasets

🎯 Usage

📍 All-in-One Setting

📍 One-by-One Setting

📊 Results

Quantitative results

Visual Results

💐 Acknowledgments

🔗 Citation

Files

README.md

Latest commit

History

README.md

File metadata and controls

CPA-Enhancer: Chain-of-Thought Prompted Adaptive Enhancer for Object Detection under Unknown Degradations

✅ Updates

🚀 Overview

🛠️ Installation

📁 Data Preparation

Synthetic Datasets

Real-world Datasets

🎯 Usage

📍 All-in-One Setting

📍 One-by-One Setting

📊 Results

Quantitative results

Visual Results

💐 Acknowledgments

🔗 Citation