Evaluating Testing and Use of Photometric Stereo Applications for Riblet Inspection

Computer vision is vital for various applications like object tracking for autonomous driving or quality assurance. Hence, assuring that computer vision fulfills given quality criteria is essential and requires sufficient testing. In previous work, authors introduced a testing method relying on image modifications for a photometric stereo application. Image modifications include pixel errors or the rotation of images to be analyzed, revealing a substantial impact on the computed outcome of the photometric stereo application, depending on the applied modification. This paper focuses on two questions, i.e., (i) whether we can reproduce the impact of image modifications in a real-world setup and (ii) on the practicability of the application of photometric stereo in the application context of quality assurance of riblet surfaces. To answer the first question, we compare the impact of the rotation and light conditions of the analyzed sample with the rotation and light modification applied to the image of the sample. The comparison indicates a similar effect when using rotation and light modifications, showing that testing based on image modifications is valuable for verifying computer vision applications. For the second question, we outline a usage scenario, present experimental observations, and discuss a stochastical model, allowing us to confirm the experiments. Moreover, we outline what to expect when using the computer vision application for real quality assurance.