Event
How Industrial AI Visual Inspection Handles Unknown Inputs Outside Training
1 reports · 1 sources
Cognex product manager Andrew Parrett gives an example illustrating a reliability issue in industrial AI visual inspection: a classification system used to sort chicken breasts and chicken legs encountered a chicken wing and, rather than flagging it as unknown, forced it into one of the known categories. In other words, the model still produces a prediction for inputs outside its training range. The report states that manufacturers typically handle such cases through rejection, reflow, or manual review, and conduct training and validation for variations in lighting, materials, and incoming materials.
Report timeline
- 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.
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