AI BriefingLG AI Research 401
Key point
Introduces industrial anomaly detection technology and the PatchCore algorithm for quality control in manufacturing processes.
Details
Anomaly Detection is a technology that finds abnormal patterns deviating from the general patterns in a data set. It is widely used in various fields such as security, finance, and manufacturing.
In vision inspection for the manufacturing industry, a Class Imbalance problem occurs where defect data is very scarce. To address this, anomaly detection models are used that learn the feature distribution of normal products and detect features that deviate from it.
Methods for detecting anomalies in product images can be broadly classified into three categories.
- Reconstruction-based: Uses an Autoencoder or Diffusion Model to measure the deviation of reconstruction Error.
- Density-based: Measures the probability density of data through a Gaussian Mixture Model (GMM), etc.
- Distance-based: Measures the distance from normal features, with PatchCore being a representative model.
PatchCore extracts patch-level features through an ImageNet pre-trained model and stores representative features in a memory bank through Coreset Sampling. This optimizes memory usage and inference time while preserving the diversity of the overall feature distribution.
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