Intelligently minimize defects and reduce costs
Machine vision systems were supposed to automate defect detection – but instead struggle in many challenging situations where the boundaries between true defects and pseudo-defects are blurred.
These struggles happen because traditional machine vision defect decisioning is based on a series of binary, yes/no choices – essentially forcing the system into guessing when it comes to difficult defects, which in turn leads to high false reject rates and costly human reinspection.
Because human quality experts are good at being able to distinguish between true defects and pseudo-defects. And our Deep Learning AI is trained just like your quality experts are, with images labeled by your quality experts, and it then applies that training to your machine vision system -- meaning it performs as well as your best inspector on their best day.
Glass manufacturer saves over $4M annually
With Spyglass Visual Inspection + Azure
A leading glass manufacturer finds that accurate defect identification helps them achieve a substantial reduction in false positives, resulting in significant quarterly savings and more effective deployment of production personnel.
We were able to train SVI to detect defects called rolled edges, which their existing system could not do. Accuracy increased so much that Vitro was able to remove human inspectors from their lines – and with savings in the millions of dollars annually, are expanding SVI implementations to more lines in more of their factories globally.