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caoyunkang/CDO: [IEEE TII 2023] Collaborative Discrepancy Optimization for Reliable Image Anomaly Localization

Collaborative Discrepancy Optimization for Reliable Image Anomaly Localization

IEEE Transactions on Industrial Informatics 2023

2025.03: 🎉🎉 Check out our updated version of CDO! You can find significant improvements versus our original version! The current version also supports both semi-supervised and unsupervised anomaly detection.
2023.03: 🎉🎉 We published a new paper related to point cloud anomaly detection, Complementary Pseudo Multimodal Feature for Point Cloud Anomaly Detection.
🔗 Paper | 🔗 Code

Most unsupervised image anomaly localization methods suffer from overgeneralization due to the high generalization abilities of convolutional neural networks, leading to unreliable predictions. To mitigate this, we propose Collaborative Discrepancy Optimization (CDO), which optimizes normal and abnormal feature distributions with synthetic anomalies. CDO introduces a margin optimization module and an overlap optimization module to maximize the margin and minimize the overlap between the discrepancy distributions (DDs) of normal and abnormal samples. Experiments on MVTec2D and MVTec3D demonstrate that CDO effectively mitigates overgeneralization, achieving excellent anomaly localization performance with real-time computation efficiency.

If you find our paper or code useful, please cite us using the following BibTex:

@ARTICLE{10034849,
  author={Cao, Yunkang and Xu, Xiaohao and Liu, Zhaoge and Shen, Weiming},
  journal={IEEE Transactions on Industrial Informatics}, 
  title={Collaborative Discrepancy Optimization for Reliable Image Anomaly Localization}, 
  year={2023},
  volume={},
  number={},
  pages={1-10},
  doi={10.1109/TII.2023.3241579}}

Create a new conda environment and install the required packages:

conda create -n cdo_env python=3.9.12
conda activate cdo_env
pip install -r requirements.txt

We support the MVTec AD dataset and VisA dataset for anomaly localization in factory settings. Unzip the files to the following directories:

datasets/
├── mvtec_anomaly_detection/
│   ├── bottle/
│   ├── cable/
│   ├── ...
│   └── meta.json
└── VisA_20220922/
    ├── candle/
    ├── capsules/
    ├── ...
    └── meta.json

To generate the required JSON files for training, run the following scripts:

python ./data/gen_metadata/mvtec.py
python ./data/gen_metadata/visa.py

Execute Different Training Tasks

Modify the self.train_data.mode and self.test_data.mode parameters in ./configs/benchmark/cdo/cdo_256_100e.py to select different training tasks. The available tasks and their corresponding metadata files are:

EXPERIMENTAL_SETUP = {
    'zero_shot': 'meta_zero_shot.json',
    'few_shot1': 'meta_few_shot1.json',
    'few_shot2': 'meta_few_shot2.json',
    'few_shot4': 'meta_few_shot4.json',
    'few_shot8': 'meta_few_shot8.json',
    'unsupervised': 'meta_unsupervised.json',
    'semi1': 'meta_semi1.json',
    'semi5': 'meta_semi5.json',
    'semi10': 'meta_semi10.json',
}

For example, to run unsupervised training:

self.train_data.mode = 'unsupervised'
self.test_data.mode = 'unsupervised'

Reference CDO Quantitative Results

The original code has been further refined and modified. The following are the quantitative results of the current version on the MVTec Dataset and VisA Dataset.

Table 1: Quantitative Results of Unsupervised Experiment on MVTec Dataset

mAUROC_sp_max mAP_sp_max mF1_max_sp_max mAUROC_px mAP_px mF1_max_px mAUPRO_px carpet 100.00 100.00 100.00 98.81 57.30 58.92 95.95 grid 100.00 100.00 100.00 99.24 45.88 49.85 97.64 leather 100.00 100.00 100.00 99.16 43.34 43.58 98.35 tile 100.00 100.00 100.00 96.59 71.68 64.09 88.93 wood 99.47 99.84 98.33 95.84 51.63 53.69 92.06 bottle 100.00 100.00 100.00 98.95 80.95 77.05 96.65 cable 99.57 99.74 97.83 97.51 61.10 62.99 92.05 capsule 97.57 99.48 97.72 98.78 41.99 47.50 96.14 hazelnut 100.00 100.00 100.00 99.11 65.72 65.56 95.94 metal_nut 100.00 100.00 100.00 97.80 77.84 82.10 94.99 pill 99.15 99.86 98.56 99.05 79.95 75.37 96.36 screw 95.57 98.48 94.96 99.31 37.04 40.50 96.54 toothbrush 96.67 98.73 95.24 98.99 52.60 58.28 91.37 transistor 99.83 99.76 97.56 95.61 59.92 55.13 90.60 zipper 99.55 99.88 98.74 98.45 56.23 60.51 95.21 average 99.13 99.68 98.44 98.12 57.46 58.77 94.30

Table 2: Quantitative Results of Unsupervised Experiment on VisA Dataset

mAUROC_sp_max mAP_sp_max mF1_max_sp_max mAUROC_px mAP_px mF1_max_px mAUPRO_px pcb1 96.07 94.43 93.78 99.77 86.12 78.36 95.83 pcb2 98.02 97.97 95.38 99.04 17.81 25.88 91.62 pcb3 97.33 97.28 92.23 99.27 31.25 30.53 93.76 pcb4 99.66 99.64 98.49 99.22 49.87 53.25 95.07 macaroni1 93.83 90.93 86.91 99.78 19.02 23.76 98.29 macaroni2 92.62 91.75 84.04 99.73 12.66 21.89 99.33 capsules 93.80 96.33 91.08 99.28 63.91 60.77 95.72 candle 98.06 98.03 94.63 99.42 23.62 34.59 96.92 cashew 97.36 98.75 94.42 98.75 51.07 52.52 95.54 chewinggum 99.43 99.68 98.99 99.14 57.44 56.03 90.52 fryum 97.48 98.79 94.12 96.94 44.90 50.58 93.29 pipe_fryum 99.12 99.56 97.00 99.08 52.60 56.90 95.37 average 96.54 96.20 92.88 99.07 40.34 43.65 94.80

Table 3: Quantitative Results of semi10 Experiment on MVTec Dataset

mAUROC_sp_max mAP_sp_max mF1_max_sp_max mAUROC_px mAP_px mF1_max_px mAUPRO_px carpet 100.00 100.00 100.00 99.44 77.26 69.03 98.06 grid 100.00 100.00 100.00 99.31 47.25 51.51 97.81 leather 100.00 100.00 100.00 99.58 64.79 60.75 98.90 tile 100.00 100.00 100.00 99.03 93.34 85.09 94.19 wood 99.89 99.96 99.01 97.15 75.59 67.42 95.12 bottle 100.00 100.00 100.00 99.49 90.90 84.40 97.97 cable 99.39 99.60 97.01 97.20 72.03 63.97 91.51 capsule 98.81 99.71 98.49 98.86 44.79 49.35 96.72 hazelnut 100.00 100.00 100.00 99.47 83.28 74.39 96.35 metal_nut 100.00 100.00 100.00 99.45 96.04 88.89 95.91 pill 98.88 99.79 97.73 99.24 85.89 77.39 96.82 screw 95.55 98.37 94.44 99.36 41.10 44.65 96.70 toothbrush 97.92 98.81 95.24 99.42 69.43 65.18 91.69 transistor 100.00 100.00 100.00 99.80 95.81 88.25 98.16 zipper 100.00 100.00 100.00 99.27 78.41 70.39 96.92 average 99.18 99.65 98.55 99.01 73.73 68.73 95.83

Table 4: Quantitative Results of semi10 Experiment on VisA Dataset

mAUROC_sp_max mAP_sp_max mF1_max_sp_max mAUROC_px mAP_px mF1_max_px mAUPRO_px pcb1 97.20 95.45 94.57 99.80 89.77 82.90 95.46 pcb2 98.17 98.06 96.09 99.41 43.23 47.07 93.30 pcb3 97.34 97.11 92.13 99.27 29.59 29.12 93.03 pcb4 99.70 99.64 98.32 99.48 59.06 57.72 96.44 macaroni1 95.17 91.12 89.89 99.78 18.09 22.49 98.42 macaroni2 92.06 90.26 83.13 99.71 12.12 20.18 99.15 capsules 94.87 96.74 91.21 99.71 72.69 65.49 97.32 candle 97.72 97.77 93.33 99.55 30.29 39.28 96.89 cashew 98.71 99.33 96.17 99.84 94.73 89.91 96.12 chewinggum 99.78 99.87 99.44 99.27 70.83 64.77 90.38 fryum 97.29 98.66 93.71 97.25 56.69 54.73 92.16 pipe_fryum 99.60 99.78 97.80 99.50 76.15 68.20 95.33 average 96.88 96.54 93.00 99.31 52.78 51.95 94.46 Reference CDO Qualitative Results

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