Machine Vision
Traditional machine vision excels at measurable, repeatable checks — dimensions, presence, codes. It struggles with variable, textured or subtle defects.
Deep-learning classification learns the difference between acceptable variation and true defects from examples, catching flaws that defeat rule-based systems.
The engineering challenge is data and speed: curated defect datasets, models optimized to run at line rate, and reject integration that keeps pace.
IPASS builds hybrid systems — rules where they're precise, deep learning where they're needed — for the lowest possible escape rate.