The Difference Check with TIV makes it possible to teach in objects with multiple test regions (ROIs). The neural network of the TIV is trained by teaching in at least 10 images classified as 'good' (OK) and at least 10 'wrong' (NOK) images. The camera determines the rules behind this on its own. It detects deviations in the test regions and outputs the result via a PLC interface. An image is considered OK if all test regions are correct, otherwise it is considered NOK. A consistent background is essential, as this is also taught in and contributes to object detection. The camera evaluates each test region individually and determines the overall result. If there are faulty test regions, an NOK signal is output, which activates a signal light or, for example, causes the product to be ejected. Employees are notified with a visual indication and can react accordingly.