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MTF as a performance indicator for AI algorithms?

  • Abstract The modulation-transfer function (MTF) is a fundamental optical metric to measure the optical quality of an imaging system. In the automotive industry it is used to qualify camera systems for ADAS/AD. Each modern ADAS/AD system includes evaluation algorithms for environment perception and decision making that are based on AI/ML methods and neural networks. The performance of these AI algorithms is measured by established metrics like Average Precision (AP) or precision-recall-curves. In this article we research the robustness of the link between the optical quality metric and the AI performance metric. A series of numerical experiments were performed with object detection and instance segmentation algorithms (cars, pedestrians) evaluated on image databases with varying optical quality. We demonstrate with these that for strong optical aberrations a distinct performance loss is apparent, but that for subtle optical quality differences – as might arise during productionAbstract The modulation-transfer function (MTF) is a fundamental optical metric to measure the optical quality of an imaging system. In the automotive industry it is used to qualify camera systems for ADAS/AD. Each modern ADAS/AD system includes evaluation algorithms for environment perception and decision making that are based on AI/ML methods and neural networks. The performance of these AI algorithms is measured by established metrics like Average Precision (AP) or precision-recall-curves. In this article we research the robustness of the link between the optical quality metric and the AI performance metric. A series of numerical experiments were performed with object detection and instance segmentation algorithms (cars, pedestrians) evaluated on image databases with varying optical quality. We demonstrate with these that for strong optical aberrations a distinct performance loss is apparent, but that for subtle optical quality differences – as might arise during production tolerances – this link does not exhibit a satisfactory correlation. This calls into question how reliable the current industry practice is where a produced camera is tested end-of-line (EOL) with the MTF, and fixed MTF thresholds are used to qualify the performance of the camera-under-test.zeige mehrzeige weniger

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Metadaten
Verfasserangaben:Patrick Müller, Alexander BraunORCiD
Open Access (DINI-Set):open_access
Open Access :Bronze - frei zugänglich aber ohne Lizenzhinweis, d.h. keine Weitergabe
Fachbereich/Einrichtung:Hochschule Düsseldorf / Fachbereich - Elektro- & Informationstechnik
Dokumentart:Wissenschaftlicher Artikel
Erscheinungsjahr:2023
Sprache der Veröffentlichung:Englisch
Verlag:Society for Imaging Sciences and Technology
Titel des übergeordneten Werkes (Englisch):Electronic Imaging: Society for Imaging Science and Technology
Band/Jahrgang/Volume:35
Heft/Ausgabe/Issue:16
Aufsatznummer:AVM-125
Seitenzahl:1–7
Erste Seite:1
Letzte Seite:7
DOI:https://doi.org/10.2352/EI.2023.35.16.AVM-125
ISSN:2470-1173
Freies Schlagwort / Tag:AI performance metrics; Artificial Intelligence; Modulation Transfer Function (MTF); Optical Quality; Perception
DDC-Klassifikation:6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften / 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
Lizenz (Deutsch):Es gilt das deutsche Urheberrecht
Datum der Freischaltung:25.08.2023
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