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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

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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

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We are Quest Global. We’re in the business of engineering, but what we’re really building is a brighter future. It’s not just what we do, but why we do it that makes us different. We believe engineering has the unique opportunity to solve the problems of today that stand in the way of tomorrow. For 25 years, we have strived to be the most trusted partner for the world’s hardest engineering problems. As a global organization headquartered in Singapore, we live and work in 17 countries, with 56 global delivery centers, driven by 13,000+ extraordinary employees who make the impossible possible every day.

Quest Global brings deep industry knowledge and digital expertise to deliver E2E global product engineering services. We bring together technologies and industries alongside the contributions of diverse individuals and their areas of expertise to solve problems better, faster. This multi-dimensional approach enables us to solve the most important and large-scale challenges across the Aerospace & Defense, Automotive, Energy, Hi-Tech, Healthcare, Medical Devices, Rail and Semiconductor industries.

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