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Scope

  • Detect the defects
  • Classify the defects
  • Bin the defects that do not fall into standard bincodes into a new category ‘Other’

Solution

  • The initial prototype implements a Deep Learning network based on CNN to detect and classify defects present in scanning electron microscope images
  • Five defect classes were present in the images. A new class ‘other’ was introduced to handle unseen defects.
  • As the images available were not sufficient for developing the deep neural network, image augmentation techniques like flip, random crop and contrast enhancement was used

Benefits

  • The accuracy achieved is 79.18% on validation data.
  • The work is currently ongoing

Features

  • Automatic defect detection and classification
  • Identification of multiple types of defects from the image

Deep Learning for Automatic Defect Detection And Classification