Hierarchical image transformation and multi-level features for anomaly defect detection

HIGHLIGHTS

  • who: Isack Farady and colleagues from the Department of Electrical Engineering, Mercu Buana University, Jakarta, Indonesia have published the article: Hierarchical Image Transformation and Multi-Level Features for Anomaly Defect Detection, in the Journal: Sensors 2023, 23, 988. of 15/01/2023
  • what: The authors design a potential methodology to tackle poison or non-ideal images that commonly appear in industrial production lines by enhancing the existing training data. The authors propose Hierarchical Image and Features (HIT-MiLF) modules for an anomaly detection network to adapt to perturbances from novelties in testing images. This approach . . .

     

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