• Treffer 14 von 838
Zurück zur Trefferliste

Analysis of the Impact of Lens Blur on Safety-Critical Automotive Object Detection

  • Camera-based object detection is widely used in safety-critical applications such as advanced driver assistance systems (ADAS) and autonomous vehicle research. Road infrastructure has been designed for human vision, so computer vision, with RGB cameras, is a vital source of semantic information from the environment. Sensors, such as LIDAR and RADAR, are also often utilized for these applications; however, cameras provide a higher spatial resolution and color information. The spatial frequency response (SFR), or sharpness of a camera, utilized in object detection systems must be sufficient to allow a detection algorithm to localize objects in the environment over its lifetime reliably. This study explores the relationship between object detection performance and SFR. Six state-of-the-art object detection models are evaluated with varying levels of lens defocus. A novel raw image dataset is created and utilized, containing pedestrians and cars over a range of distances up to 100-m fromCamera-based object detection is widely used in safety-critical applications such as advanced driver assistance systems (ADAS) and autonomous vehicle research. Road infrastructure has been designed for human vision, so computer vision, with RGB cameras, is a vital source of semantic information from the environment. Sensors, such as LIDAR and RADAR, are also often utilized for these applications; however, cameras provide a higher spatial resolution and color information. The spatial frequency response (SFR), or sharpness of a camera, utilized in object detection systems must be sufficient to allow a detection algorithm to localize objects in the environment over its lifetime reliably. This study explores the relationship between object detection performance and SFR. Six state-of-the-art object detection models are evaluated with varying levels of lens defocus. A novel raw image dataset is created and utilized, containing pedestrians and cars over a range of distances up to 100-m from the sensor. Object detection performance for each defocused dataset is analyzed over a range of distances to determine the minimum SFR necessary in each case. Results show that the relationship between object detection performance and lens blur is much more complex than previous studies have found due to lens field curvature, chromatic aberration, and astigmatisms. We have found that smaller objects are disproportionately impacted by lens blur, and different object detection models have differing levels of robustness to lens blurzeige mehrzeige weniger

Metadaten exportieren

Weitere Dienste

Teilen auf Twitter Suche bei Google Scholar
Metadaten
Verfasserangaben:Dara Molloy, Patrick Müller, Brian Deegan, Darragh Mullins, Jonathan Horgan, Enda Ward, Edward Jones, Alexander BraunORCiD, Martin Glavin
Open Access (DINI-Set):open_access
Qualitätssicherung:peer reviewed
Open Access :Gold - Erstveröffentlichung mit Lizenzhinweis
Fachbereich/Einrichtung:Hochschule Düsseldorf / Fachbereich - Elektro- & Informationstechnik
Dokumentart:Wissenschaftlicher Artikel
Erscheinungsjahr:2024
Sprache der Veröffentlichung:Englisch
Verlag:IEEE
Titel des übergeordneten Werkes (Englisch):IEEE Access
Band/Jahrgang/Volume:12
Seitenzahl:16
Erste Seite:3554
Letzte Seite:3569
URN:urn:nbn:de:hbz:due62-opus-42999
DOI:https://doi.org/10.1109/ACCESS.2023.3348663
ISSN:2169-3536
Freies Schlagwort / Tag:DOAJ
ADAS; Object detection; autonomous vehicles; intelligent transportation system; lens blur
DDC-Klassifikation:6 Technik, Medizin, angewandte Wissenschaften / 62 Ingenieurwissenschaften / 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten
Lizenz (Deutsch):Creative Commons - CC BY-NC-ND - Namensnennung - Nicht kommerziell - Keine Bearbeitungen 4.0 International
Datum der Freischaltung:17.01.2024
Accept ✔
Diese Webseite verwendet technisch erforderliche Session-Cookies. Durch die weitere Nutzung der Webseite stimmen Sie diesem zu. Unsere Datenschutzerklärung finden Sie hier.