Learning deep feature correspondence for unsupervised anomaly detection and segmentation
作者:
Highlights:
• A learnable deep feature correspondence (DFC) method is proposed for unsupervised anomaly detection and segmentation.
• DFC achieves state of the art results on the benchmark unsupervised anomaly detection and segmentation task MVTec AD.
• DFC is very effective for detecting and segmenting the anomalous structures and patterns that appear in confined local regions of images, especially the industrial anomalies.
• The generality of DFC is demonstrated by applying it on a real industrial inspection scene.
摘要
•A learnable deep feature correspondence (DFC) method is proposed for unsupervised anomaly detection and segmentation.•DFC achieves state of the art results on the benchmark unsupervised anomaly detection and segmentation task MVTec AD.•DFC is very effective for detecting and segmenting the anomalous structures and patterns that appear in confined local regions of images, especially the industrial anomalies.•The generality of DFC is demonstrated by applying it on a real industrial inspection scene.
论文关键词:Anomaly detection,Anomaly segmentation,Feature correspondence,Dual network
论文评审过程:Received 20 February 2021, Revised 25 May 2022, Accepted 26 June 2022, Available online 27 June 2022, Version of Record 11 August 2022.
论文官网地址:https://doi.org/10.1016/j.patcog.2022.108874