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


  • 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


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


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

Deep Learning for Automatic Defect Detection And Classification