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Evaluation of Parking Space Detection From Aerial Imagery Using Convolutional Neural Networks

Lindblom, Max LU and Nilsson, Carl LU (2019) In Master's Theses in Mathematical Sciences FMAM05 20182
Mathematics (Faculty of Engineering)
Abstract
In this thesis, the viability of using Convolutional Neural Networks to detect parking spaces using aerial imagery has been evaluated. Three state of the art networks have been tested - YOLOv3, RetinaNet, and Mask R-CNN. A dataset of urban parking lots and corresponding annotations was generated from scratch using a custom built GUI to annotate automatically generated images of parking lots from Open Street Map, from varying aerial imagery providers. This dataset was used to test and evaluate the different networks, and Mask R-CNN was used for a lengthy parameter tuning process, as it seemed to perform optimally of the three networks. The resulting model did not perform the best, believed to be because of the low amount of features... (More)
In this thesis, the viability of using Convolutional Neural Networks to detect parking spaces using aerial imagery has been evaluated. Three state of the art networks have been tested - YOLOv3, RetinaNet, and Mask R-CNN. A dataset of urban parking lots and corresponding annotations was generated from scratch using a custom built GUI to annotate automatically generated images of parking lots from Open Street Map, from varying aerial imagery providers. This dataset was used to test and evaluate the different networks, and Mask R-CNN was used for a lengthy parameter tuning process, as it seemed to perform optimally of the three networks. The resulting model did not perform the best, believed to be because of the low amount of features represented in parking spaces. While results indicated that a somewhat complete solution is possible, it might not be feasible using a pure single CNN approach. (Less)
Popular Abstract (Swedish)
Vi har försökt hjälpa vidareutvecklingen av självkörande bilar med hjälp av att identifiera och klassificera parkeringsplatser i flygbilder. Problemet har visat sig vara svårt, och kanske inte kan lösas helt med hjälp av endast maskininlärningstekniker. Det finns dock tecken på att lösningen går mot rätt riktning, då vi kan se att systemet hittar de flesta av parkeringsplatserna som finns i exemplen, och dessutom sällan gissar fel.
Please use this url to cite or link to this publication:
author
Lindblom, Max LU and Nilsson, Carl LU
supervisor
organization
alternative title
Utvärdering av Parkeringsplatsdetektering från Flygbilder Genom Neurala Faltningsnätverk
course
FMAM05 20182
year
type
H2 - Master's Degree (Two Years)
subject
keywords
Machine Learning, Autonomous Cars, Convolutional Neural Networks, Parking, Image Analysis
publication/series
Master's Theses in Mathematical Sciences
report number
LUTFMA-3376-2019
ISSN
1404-6342
other publication id
2019:E6
language
English
id
8971684
date added to LUP
2019-07-15 11:38:51
date last changed
2019-07-15 11:38:51
@misc{8971684,
  abstract     = {{In this thesis, the viability of using Convolutional Neural Networks to detect parking spaces using aerial imagery has been evaluated. Three state of the art networks have been tested - YOLOv3, RetinaNet, and Mask R-CNN. A dataset of urban parking lots and corresponding annotations was generated from scratch using a custom built GUI to annotate automatically generated images of parking lots from Open Street Map, from varying aerial imagery providers. This dataset was used to test and evaluate the different networks, and Mask R-CNN was used for a lengthy parameter tuning process, as it seemed to perform optimally of the three networks. The resulting model did not perform the best, believed to be because of the low amount of features represented in parking spaces. While results indicated that a somewhat complete solution is possible, it might not be feasible using a pure single CNN approach.}},
  author       = {{Lindblom, Max and Nilsson, Carl}},
  issn         = {{1404-6342}},
  language     = {{eng}},
  note         = {{Student Paper}},
  series       = {{Master's Theses in Mathematical Sciences}},
  title        = {{Evaluation of Parking Space Detection From Aerial Imagery Using Convolutional Neural Networks}},
  year         = {{2019}},
}