  Anomaly localization is a practical technology for improving industrial
production line efficiency. Due to anomalies are manifold and hard to be
collected, existing unsupervised researches are usually equipped with anomaly
synthesis methods. However, most of them are biased towards structural defects
synthesis while ignoring the underlying logical constraints. To fill the gap
and boost anomaly localization performance, we propose an edge manipulation
based anomaly synthesis framework, named LogicAL, that produces photo-realistic
both logical and structural anomalies. We introduce a logical anomaly
generation strategy that is adept at breaking logical constraints and a
structural anomaly generation strategy that complements to the structural
defects synthesis. We further improve the anomaly localization performance by
introducing edge reconstruction into the network structure. Extensive
experiments on the challenge MVTecLOCO, MVTecAD, VisA and MADsim datasets
verify the advantage of proposed LogicAL on both logical and structural anomaly
localization.
