AI-Based Automated Visual Inspection for Industrial Automation and Machinery
Joyeeta Mukherjee, Aetna/CVSHealth
This webinar is Part 4 of the Artificial Intelligence in Industrial Automation and Machinery series.
Automated inspection and defect detection are critical for high throughput quality control in production systems. They are widely adopted in many industries for detection of flaws on different kinds of manufactured surfaces such as metallic rails, semiconductor wafers, contact lenses and so on. Recent developments in deep learning have brought revolutionary new tools to automate visual inspection tasks with unprecedented accuracy and robustness. New methods allow you to find arbitrary defects without the need for failure data during training.
In this session we will discuss state-of-the-art approaches for visual inspection and present multiple case studies on how these approaches have been applied in industry. We will cover basic classification problems as well as advanced algorithms like autoencoders, semantic segmentation, generational adversarial networks and one-class learning. Through a couple of hands-on examples you will see how it can be implemented in MATLAB. We will also discuss methods to tackle common pitfalls in the development of such models, and challenges presented in validating and operationalizing a deep network.
- Preprocessing and augmenting image data
- Overview of methods available for automated visual inspection
- How to train a deep neural network in MATLAB
- Managing experiments and tuning training parameters
- Automatically generate code and deploy to embedded targets
- Validating and operationalizing models
About the Presenters
Emelie Andersson is an application engineer at MathWorks focusing on data analytics and image processing applications with a special focus on deep learning. In her role she supports customers to adapt MATLAB products in the entire data analytics workflow. She holds a M.Sc. degree from Lund University in image analysis and signal processing.
Christoph Kammer is an application engineer at MathWorks. He supports customers in many different industries in the areas of machine and deep learning, image and signal processing and deployment to embedded or enterprise systems. Christoph has a master’s degree in Mechanical Engineering from ETHZ and a PhD in Electrical Engineering from EPFL, where he specialized in optimization and control design as well as the control and modelling of power systems.
Joyeeta Mukherjee has a technical background in signal & image processing, computer vision, machine learning and deep learning. She is experienced in designing solutions for machine vision in manufacturing, defense, surveillance and quality control, medical devices, and bio-pharma sectors. Joyeeta works as a Principal Data Scientist at Aetna/CVSHealth in the HealthCare Cost Management team.
Recorded: 13 Oct 2021
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