PatchFlow: Leveraging a Flow-Based Model with Patch Features

TL;DR

PatchFlow introduces a flow-based model utilizing patch features and an adapter to improve automated defect detection in industrial images, significantly reducing error rates and enhancing accuracy without requiring anomalous training data.

Contribution

The paper presents a novel flow-based anomaly detection method combining patch features and an adapter, tailored for industrial defect detection, outperforming existing state-of-the-art approaches.

Findings

20% error reduction on MVTec AD dataset

Achieved 99.28% image-level AUROC on MVTec AD

Proprietary die casting dataset accuracy of 95.77%

Abstract

Die casting plays a crucial role across various industries due to its ability to craft intricate shapes with high precision and smooth surfaces. However, surface defects remain a major issue that impedes die casting quality control. Recently, computer vision techniques have been explored to automate and improve defect detection. In this work, we combine local neighbor-aware patch features with a normalizing flow model and bridge the gap between the generic pretrained feature extractor and industrial product images by introducing an adapter module to increase the efficiency and accuracy of automated anomaly detection. Compared to state-of-the-art methods, our approach reduces the error rate by 20\% on the MVTec AD dataset, achieving an image-level AUROC of 99.28\%. Our approach has also enhanced performance on the VisA dataset , achieving an image-level AUROC of 96.48\%. Compared to the state-of-the-art models, this represents a 28.2\% reduction in error. Additionally, experiments on a proprietary die casting dataset yield an accuracy of 95.77\% for anomaly detection, without requiring any anomalous samples for training. Our method illustrates the potential of leveraging computer vision and deep learning techniques to advance inspection capabilities for the die casting industry