Adaptive Real-Time Object Detection for Autonomous Driving Systems.

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dc.contributor.author Hemmati, Maryam
dc.contributor.author Biglari-Abhari, Morteza
dc.contributor.author Niar, Smail
dc.coverage.spatial Switzerland
dc.date.accessioned 2022-05-16T04:42:55Z
dc.date.available 2022-05-16T04:42:55Z
dc.date.issued 2022-04-11
dc.identifier.citation (2022). Journal of Imaging, 8(4), 106-.
dc.identifier.issn 2313-433X
dc.identifier.uri https://hdl.handle.net/2292/59244
dc.description.abstract Accurate and reliable detection is one of the main tasks of Autonomous Driving Systems (ADS). While detecting the obstacles on the road during various environmental circumstances add to the reliability of ADS, it results in more intensive computations and more complicated systems. The stringent real-time requirements of ADS, resource constraints, and energy efficiency considerations add to the design complications. This work presents an adaptive system that detects pedestrians and vehicles in different lighting conditions on the road. We take a hardware-software co-design approach on Zynq UltraScale+ MPSoC and develop a dynamically reconfigurable ADS that employs hardware accelerators for pedestrian and vehicle detection and adapts its detection method to the environment lighting conditions. The results show that the system maintains real-time performance and achieves adaptability with minimal resource overhead.
dc.format.medium Electronic
dc.language eng
dc.publisher MDPI AG
dc.relation.ispartofseries Journal of imaging
dc.rights Items in ResearchSpace are protected by copyright, with all rights reserved, unless otherwise indicated. Previously published items are made available in accordance with the copyright policy of the publisher.
dc.rights.uri https://researchspace.auckland.ac.nz/docs/uoa-docs/rights.htm
dc.rights.uri https://creativecommons.org/licenses/by/4.0/
dc.subject Adaptive ADS
dc.subject DBN
dc.subject FPGA
dc.subject HOG
dc.subject SVM
dc.subject hardware accelerator
dc.subject partial reconfiguration
dc.subject pedestrian detection
dc.subject real-time detection
dc.subject vehicle detection
dc.subject 7 Affordable and Clean Energy
dc.title Adaptive Real-Time Object Detection for Autonomous Driving Systems.
dc.type Journal Article
dc.identifier.doi 10.3390/jimaging8040106
pubs.issue 4
pubs.begin-page 106
pubs.volume 8
dc.date.updated 2022-04-29T14:43:33Z
dc.rights.holder Copyright: The author en
dc.identifier.pmid 35448233 (pubmed)
pubs.author-url https://www.ncbi.nlm.nih.gov/pubmed/35448233
pubs.publication-status Published
dc.rights.accessrights http://purl.org/eprint/accessRights/OpenAccess en
pubs.subtype research-article
pubs.subtype Journal Article
pubs.elements-id 897795
pubs.org-id Engineering
pubs.org-id Department of Electrical, Computer and Software Engineering
dc.identifier.eissn 2313-433X
dc.identifier.pii jimaging8040106
pubs.record-created-at-source-date 2022-04-30
pubs.online-publication-date 2022-04-11


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