This is the official code for the paper entitled "Component-aware anomaly detection framework for adjustable and logical industrial visual inspection"
The latest version is on: https://www.sciencedirect.com/science/article/abs/pii/S1474034623002896
The old version: https://arxiv.org/abs/2305.08509
Different from existing image reconstruction-based or feature-based industrial anomaly detection methods, we propose a new component-based detection paradigm for adjustable and logical anomaly detection, as shown in (c)
The overall detection process is:
Due to the randomness of KMeans, the results of each experiment will vary slightly. For the original paper, we ran a total of five times and took the average value.
If you have any questions, you could also contact ltk98633@stu.xjtu.edu.cn
Our selected benchmarks include the following:
MVTec Loco AD dataset: https://www.mvtec.com/company/research/datasets/mvtec-loco.
CAD-AD dataset: https://github.com/IshidaKengo/SA-PatchCore
First run:
python seg_image.py --datasetpath .../mvtec_loco/to segment the image. Change the --datasetpath to your own file path.
Then run (make sure you have previously finished seg_image.py)
python logical_anomaly_detection.py to achieve logical anomaly detection
If you find this work helpful to your project, please cite
@article{liu2023component,
title={Component-aware anomaly detection framework for adjustable and logical industrial visual inspection},
author={Liu, Tongkun and Li, Bing and Du, Xiao and Jiang, Bingke and Jin, Xiao and Jin, Liuyi and Zhao, Zhuo},
journal={Advanced Engineering Informatics},
volume={58},
pages={102161},
year={2023},
publisher={Elsevier}
}
We use some codes from https://github.com/mhamilton723/STEGO, https://github.com/facebookresearch/dino, https://github.com/amazon-science/patchcore-inspection, and https://github.com/VitjanZ/DRAEM. A big thanks to their great work!