Automated inspection and defect detection systems use AI to inspect manufacturing parts for failures and defects. This approach enables industries to automatically detect flaws on manufactured surfaces such as metallic rails, semiconductor wafers, and contact lenses.

This ebook shows how you can use MATLAB® to develop a deep learning network to detect and classify different types of anomalies.

You will learn about the three main stages of the defect detection workflow:

  1. Preparing data, including denoising, registration, and labeling
  2. Building and training a deep learning network
  3. Deploying the network to multiple hardware platforms such as CPUs and GPUs

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