AI-based 3D detection of parked vehicles on a mobile mapping platform using edge computing
Dateien
Autor:innen
Autor:in (Körperschaft)
Publikationsdatum
2022
Typ der Arbeit
Studiengang
Sammlung
Typ
04B - Beitrag Konferenzschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
The international archives of the photogrammetry, remote sensing and spatial information sciences
Themenheft
DOI der Originalpublikation
Link
Reihe / Serie
Reihennummer
Jahrgang / Band
XLIII-B1-2022
Ausgabe / Nummer
Seiten / Dauer
437-445
Patentnummer
Verlag / Herausgebende Institution
Verlagsort / Veranstaltungsort
Nice
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
In this paper we present an edge-based hardware and software framework for the 3D detection and mapping of parked vehicles on a mobile mapping platform for the use case of on-street parking statistics. First, we investigate different point cloud-based 3D object detection methods on our extremely dense and noisy depth maps obtained from low-cost RGB-D sensors to find a suitable object detector and determine the optimal preparation of our data. We then retrain the chosen object detector to detect all types of vehicles, rather than standard cars only. Finally, we design and develop a software framework integrating the newly trained object detector. By repeating the parking statistics of our previous work (Nebiker et al., 2021), our software is tested regarding the detection accuracy. With our edge-based framework, we achieve a precision and recall of 100% and 98% respectively on any parking configuration and vehicle type, outperforming all other known work on on-street parking statistics. Furthermore, our software is evaluated in terms of processing speed and volume of generated data. While the processing speed reaches only 1.9 frames per second due to limited computing resources, the amount of data generated is just 0.25 KB per frame.
Schlagwörter
3D vehicle detection, Deep neural networks, Edge computing, Mobile mapping, RGB-D, Robot operating system, Point clouds
Fachgebiet (DDC)
600 - Technik, Medizin, angewandte Wissenschaften
Veranstaltung
XXIVth ISPRS Congress
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
06.06.2022
Enddatum der Konferenz
11.06.2022
Datum der letzten Prüfung
ISBN
ISSN
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Ja
Zukunftsfelder FHNW
Publikationsstatus
Veröffentlicht
Begutachtung
Peer-Review der ganzen Publikation
Open Access-Status
Gold
Lizenz
Zitation
MEYER, Jonas, Stefan BLASER und Stephan NEBIKER, 2022. AI-based 3D detection of parked vehicles on a mobile mapping platform using edge computing. In: The international archives of the photogrammetry, remote sensing and spatial information sciences. Nice. 2022. S. 437–445. Verfügbar unter: https://doi.org/10.26041/fhnw-9463