Webinar: Deep Learning Based Defect Detection for Visual Inspection Systems by MA Business

Deep Learning Based Defect Detection for Visual Inspection Systems

About

Visual inspection and defect detection are critical for high throughput quality control in production systems and widely adopted in many industries for the detection of flaws.

In this session, MathWorks experts Harshita Buhrat, MathWorks Product Manager and Matthew Elliott, Applications Engineer will explore how developments in deep learning have significantly improved the ability to detect defects and develop deep learning-based approaches to detect and localize different types of anomalies.

Join us to learn about:

- Data access and pre-processing techniques
- Image classification and labelling of defects and abnormalities
- Defect detection using deep learning techniques
- Deploying to multiple hardware platforms such as CPUs and GPUs

Presenters

Harshita Bhurat

Product Marketing Manager, MathWorks

Harshita Bhurat is the product manager for Image Processing and Computer Vision products. She has been at MathWorks for 8 years. In the past at MathWorks, she has managed the Robotics and Autonomous Systems and code generation products. Prior to joining MathWorks, Harshita was an embedded application engineer at Broadcom, where she was a part of the team responsible for the development, integration, and deployment of Broadcom's High-Definition ICs into turnkey Blu-ray and streaming service products. Harshita has over 15 years of experience in image and video processing and software for embedded systems. She holds a B.S. in computer engineering and M.S. in computer science from Illinois Institute of Technology, Chicago.

Matthew Elliott

Application Engineer, MathWorks

Matt is an application engineer at MathWorks, working on MATLAB projects in a range of areas, including machine learning, image processing, parallel computing, and enterprise integration. Prior to joining MathWorks in 2017, he completed a PhD in physics, focusing on theory and simulation of superconducting circuits for applications in quantum computing. Matt received his PhD from the University of Surrey and holds a master’s degree in mathematics and physics from Durham University.

Jon Excell

Editor - The Engineer

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