Article

When a vision model encounters something it wasn't trained on: reliability issues in industrial AI inspection

Cognex product manager Andrew Parrett gives an example: a classification system used to sort chicken breasts and drumsticks, when it encountered a chicken wing, did not flag it as unknown but instead forced it into one of the known categories. This reflects a real problem in industrial inspection and robotic sorting—models still produce predictions for inputs outside their training range. The article describes how manufacturers handle such cases through rejection, reflow, or manual review, and how they train and validate against changes in lighting, materials, and incoming materials.

Part of the event: How Industrial AI Visual Inspection Handles Unknown Inputs Outside Training

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