ASQ Inspection Division Webinar (0.1 RU) — AI Computer Vision | Making Sense of Visual Data for Adv

When:  Jan 11, 2023
Associated with  Inspection Division
Webinar Abstract:
By far our most important organs of sense are our eyes. We perceive about 80% of all impressions by means of our sight. What if we could augment machine vision and supervisory systems with a more intelligent perception? Imagine cameras taking images or video streams and analyzing data to detect/classify the hardest to see defects & objects, human actions, and events. The advancements in AI and Computer Vision are helping manufacturers make sense of visual data to catch and get to the root-cause of the most challenging defects. It’s enabling the ability to monitor and track manual operations, improve processes, enhance safety, and much more. Enterprise scalable platforms such as Matroid are empowering subject matter experts in industry to build, test, deploy, and manage custom AI-based detectors for these applications plant wide and across enterprises. This means the most challenging defects are being caught by quality and manufacturing experts. Machines are given real-time feedback to prevent defects. People are given real-time corrective action to ensure products are assembled in proper sequence. AI-based supervisory systems enable a digitalized and continuous lean thinking tool kit for process improvements and optimizations.

Learning Objectives
  • What is computer vision and how is it different from traditional rules-based machine vision

  • An understanding of what the technology is capable of – detect/classify objects, defects, people, human actions, and events.

  • Have a diverse set of example use-cases (classify materials, defects with variations, people monitoring, process monitoring, etc.).

  • Understand how the complete CV platforms make it so that a company’s most valued assets (SME’s) can be empowered with the latest in AI-based tools to improve quality control and improve processes.

  • Leave with a resource on the topic of Computer Vision.

Event address for attendees: