Do you have a machine vision system for defect detection? Are you happy with its accuracy?
If your answer is “yes” and “no”, this webinar is for you. Read no further and register below.
Improve Quality Inspection
with Deep Learning
October 1, 2019
12:00 pm – 12:45 pm EDT
Increasing regulatory compliance and OEM quality requirements create an environment of greater accountability to quality. Manufacturers have made significant investments in machine vision systems to detect defects. While these systems include high quality optics, effective lighting and high-resolution image capture, their software algorithms are incapable of the accuracy of modern deep learning methods. Consequently, human intervention is necessary to compensate for their existing system’s inaccuracy and the application of further automation using robotics is costly, if not impossible.
In this webinar, Stephen Welch will discuss:
- Spyglass Visual Inspection vs. Traditional Machine Vision
- The Rise of Deep Learning
- Case Study: Glass Manufacturer
- Case Study: Automotive Interior Manufacturer
Stephen Welch is VP of Data Science at Mariner, where he leads a team developing deep-learning based solutions for manufacturing applications. Prior to working with Mariner, Stephen was VP of Machine Learning at Autonomous Fusion, an Atlanta-based autonomous driving startup, where Stephen lead the design, development, and deployment of machine learning algorithms for autonomous driving.Stephen has extensive experience training and deploying machine learning models across a wide variety of domains, including an on-board crash detection algorithm that is now deployed in over 1M vehicles as part of the Verizon Hum product. Stephen strives to not just develop strong technology, but to explain and communicate results in clear and accessible ways – as an adjunct professor at UNCC, Stephen teaches a 60+ person graduate level class in machine learning and computer vision.Stephen is also the author of the educational YouTube channel Welch Labs, which has earned 200k+ subscribers and 10M+ views. Stephen holds 10+ US patents, and engineering degrees from Georgia Tech and UC Berkeley.