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Windscreen Optical Quality for AI Algorithms: Refractive Power and MTF not Sufficient

Wolf, Dominik Werner; Ulrich, Markus ORCID iD icon 1; Braun, Alexander
1 Institut für Photogrammetrie und Fernerkundung (IPF), Karlsruher Institut für Technologie (KIT)

Abstract:

Windscreen optical quality is an important aspect of any advanced driver assistance system, and also for future autonomous driving, as today at least some cameras of the sensor suite are situated behind the windscreen. Automotive mass production processes require measurement systems that characterize the optical quality of the windscreens in a meaningful way, which for modern perception stacks implies meaningful for artificial intelligence (AI) algorithms. The measured optical quality needs to be linked to the performance of these algorithms, such that performance limits - and thus production tolerance limits - can be defined. In this article we demonstrate that the main metric established in the industry - refractive power - is fundamentally not capable of capturing relevant optical properties of windscreens. Further, as the industry is moving towards the modulation transfer function (MTF) as an alternative, we mathematically show that this metric cannot be used on windscreens alone, but that the windscreen forms a novel optical system together with the optics of the camera system. Hence, the required goal of a qualification system that is installed at the windscreen supplier and independently measures the optical quality cannot be achieved using MTF. ... mehr


Volltext §
DOI: 10.5445/IR/1000167377
Veröffentlicht am 17.01.2024
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Photogrammetrie und Fernerkundung (IPF)
Publikationstyp Forschungsbericht/Preprint
Publikationsmonat/-jahr 05.2023
Sprache Englisch
Identifikator KITopen-ID: 1000167377
Verlag arxiv
Vorab online veröffentlicht am 23.05.2023
Schlagwörter Computer Vision and Pattern Recognition (cs.CV)
Nachgewiesen in arXiv
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