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
