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DMPR-PS: A Novel Approach for Parking-Slot Detection Using Directional Marking-Point Regression
Tongji University.
Tongji University.
Tongji University.
Tongji University.
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2019 (English)Conference paper, Published paper (Refereed)
Abstract [en]

The self-parking system plays an important role in autonomous driving, and one of its critical issues is parking-slot detection. Previous studies in this field are mostly based on off-the-shelf models designed for universal purposes, which have various limitations in solving specific problems. In this paper, we propose a parking-slot detection method using directional marking-point regression, namely DMPR-PS. Instead of utilizing multiple off-the-shelf models, DMPR-PS uses a novel CNN-based model specially designed for directional marking-point regression. Given a surround-view image I, the model predicts position, shape and orientation of each marking-point on I. From marking-points, parking-slots on I could be easily inferred using geometric rules. DMPR-PS outperforms state-of-the-art competitors on the benchmark dataset with a precision rate of 99.42% and a recall rate of 99.37%, while achieving a real-time detection speed of 12ms per frame on Nvidia Titan Xp. To make the results reproducible, the source code is available at https://github.com/Teoge/DMPR-PS.

Place, publisher, year, edition, pages
Shanghai, China, 2019.
Keywords [en]
Computer architecture, Microprocessors, Shape, Predictive models, Neural networks, Image edge detection, Junctions
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:uu:diva-390160DOI: 10.1109/ICME.2019.00045ISBN: 978-1-5386-9552-4 (electronic)ISBN: 978-1-5386-9553-1 (print)OAI: oai:DiVA.org:uu-390160DiVA, id: diva2:1340726
Conference
2019 IEEE International Conference on Multimedia and Expo (ICME)
Available from: 2019-08-06 Created: 2019-08-06 Last updated: 2019-08-06

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Yang, Yukai
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CiteExportLink to record
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Citation style
  • apa
  • ieee
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  • Other style
More styles
Language
  • de-DE
  • en-GB
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  • fi-FI
  • nn-NO
  • nn-NB
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Output format
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