- The initial prototype implements a deep learning network based on CNN to detect and classify defects present in scanning electron microscope images
- 5 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
- Automatic defect detection and classification
- Identification of multiple types of defects from the image
- Activation maps overlayed on images as heatmaps for better visualisation
The accuracy achieved is 79.18 % on validation data. The work is currently ongoing.