Warning: json_decode() expects parameter 1 to be string, array given in /var/www/stage/quest-global-v2/wp-content/plugins/rx-dynamic-sections/sections.php on line 7088
Deep Learning for Automatic Defect Detection and Classification
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
Warning: Use of undefined constant string - assumed 'string' (this will throw an Error in a future version of PHP) in /var/www/stage/quest-global-v2/wp-content/plugins/rx-dynamic-sections/sections.php on line 6721
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