Factory lighting can be brutal. A label looks perfect from one angle and unreadable from another. A reflective pouch catches glare. A conveyor casts shadows. A package edge disappears under mixed LED lighting. Traditional industrial vision systems solve these very real problems, and that’s why they became expensive. However, many inspection tasks don’t require a closed, high-cost smart camera. They just need a reliable prototype path: collect images, train a model, deploy locally, trigger an action, and improve.  On UNO Q , the Linux side of the board can run the camera pipeline, OpenCV preprocessing, an Edge Impulse object-detection or classification model, and a local web dashboard. Meanwhile, the MCU side can handle encoder pulses, trigger timing, stack-light outputs, and reject-actuator logic. You can already browse Arduino® Project Hub for a variety of practical vision examples that combine UNO Q with Edge Impulse models.…