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.…
Verfasserangaben: | Patrick Müller, Alexander BraunORCiD |
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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 |