AI-driven quality testing can increase productivity by up to 50% and defect detection rates by up to 90% compared to human inspection. Though machines with automated optical inspection (AOI), powered by machine vision, have replaced most of the manual processes in the modern assembly line, quality control still remains a huge and costly challenge.
To build a smart automated optical inspection solution, AI software can be combined with a machine vision platform, which supports a wide variety of deep learning applications. In addition, an appropriately-optimized toolkit can enable deep learning inference from edge to cloud.
With this in place, automatic load balancing and asynchronous execution can deliver cost-effective performance, while heterogeneity support allows seamless execution across cloud and local servers and edge AI vision devices. This type of solution also collects data from inspection devices to train AI and iterate on inspection performance gains and maintains inspection logs for customer references.