Automotive cameras are a safety critical component in the sensor stack of a car. Therefore, you want to make sure that each and every camera that is installed in a car works as intended, that the user is satisfied, and the machine vision algorithms can work as expected.

In this tutorial we have a look at properties of a camera that in most systems vary enough that you want to have a closer look in the validation phase and for some even in an end-of-line test station. We discuss items that are typically characterized and result in a pass/fail decision and in properties that are part of a calibration process to make sure that the signal from the camera and the input into a machine vision system is well defined and has only small variation. These include the spatial frequency response, noise, relative illumination, color processing, geometric calibration and more.

We have a look at existing international standards and best practice procedures so that all attendees have a good understanding of the used metrics and the caveats connected to them.