Determining the Occupancy of Vehicle Parking Areas by Deep Learning

Ayse Betul Baktir; Bulent Bolat

doi:10.1109/ICECCE49384.2020.9179399

Determining the Occupancy of Vehicle Parking Areas by Deep Learning

Ayse Betul Baktir
, Bulent Bolat

Yildiz Technical University

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

7 Atıf (Scopus)

Özet

Parking a vehicle in heavy traffic situations leads to prolonged driving time, deterioration of traffic flow and therefore environmental pollution when searching for free space. Although the sensor systems in the indoor parking lots are beneficial, these systems cannot be applied to outdoor spaces. In this study, a deep learning application was developed which classifies the occupancy status of the parking spaces in outdoor parking areas. High accuracy rates were obtained in this application where transfer learning was performed using ResNet model.

Orijinal dil	İngilizce
Ana bilgisayar yayını başlığı	2nd International Conference on Electrical, Communication and Computer Engineering, ICECCE 2020
Yayınlayan	Institute of Electrical and Electronics Engineers Inc.
ISBN (Elektronik)	9781728171166
DOI'lar	https://doi.org/10.1109/ICECCE49384.2020.9179399
Yayın durumu	Yayınlandı - Haz 2020
Harici olarak yayınlandı	Evet
Etkinlik	2nd International Conference on Electrical, Communication and Computer Engineering, ICECCE 2020 - Istanbul, Turkey Süre: 12 Haz 2020 → 13 Haz 2020

Yayın serisi

Adı	2nd International Conference on Electrical, Communication and Computer Engineering, ICECCE 2020

???event.eventtypes.event.conference???

???event.eventtypes.event.conference???	2nd International Conference on Electrical, Communication and Computer Engineering, ICECCE 2020
Ülke/Bölge	Turkey
Şehir	Istanbul
Periyot	12/06/20 → 13/06/20

Bibliyografik not

Publisher Copyright:
© 2020 IEEE.

BM SKH

Bu sonuç, aşağıdaki Sürdürülebilir Kalkınma Hedefine/Hedeflerine katkıda bulunur

SKH 11 Sürdürülebilir Şehirler ve Topluluklar

Belgeye Erişim

10.1109/ICECCE49384.2020.9179399

Diğer Dosyalar ve Linkler

Link to publication in Scopus

Alıntı Yap

Baktir, A. B., & Bolat, B. (2020). Determining the Occupancy of Vehicle Parking Areas by Deep Learning. In 2nd International Conference on Electrical, Communication and Computer Engineering, ICECCE 2020 Makale 9179399 (2nd International Conference on Electrical, Communication and Computer Engineering, ICECCE 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICECCE49384.2020.9179399

@inproceedings{d5b201067d5644de8e4691c3cb56f6d9,

title = "Determining the Occupancy of Vehicle Parking Areas by Deep Learning",

abstract = "Parking a vehicle in heavy traffic situations leads to prolonged driving time, deterioration of traffic flow and therefore environmental pollution when searching for free space. Although the sensor systems in the indoor parking lots are beneficial, these systems cannot be applied to outdoor spaces. In this study, a deep learning application was developed which classifies the occupancy status of the parking spaces in outdoor parking areas. High accuracy rates were obtained in this application where transfer learning was performed using ResNet model.",

keywords = "CNRPark, Deep Learning, PKLot, Parking Lot, ResNet",

author = "Baktir, \{Ayse Betul\} and Bulent Bolat",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2nd International Conference on Electrical, Communication and Computer Engineering, ICECCE 2020 ; Conference date: 12-06-2020 Through 13-06-2020",

year = "2020",

month = jun,

doi = "10.1109/ICECCE49384.2020.9179399",

language = "English",

series = "2nd International Conference on Electrical, Communication and Computer Engineering, ICECCE 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "2nd International Conference on Electrical, Communication and Computer Engineering, ICECCE 2020",

address = "United States",

}

Baktir, AB & Bolat, B 2020, Determining the Occupancy of Vehicle Parking Areas by Deep Learning. in 2nd International Conference on Electrical, Communication and Computer Engineering, ICECCE 2020., 9179399, 2nd International Conference on Electrical, Communication and Computer Engineering, ICECCE 2020, Institute of Electrical and Electronics Engineers Inc., 2nd International Conference on Electrical, Communication and Computer Engineering, ICECCE 2020, Istanbul, Turkey, 12/06/20. https://doi.org/10.1109/ICECCE49384.2020.9179399

Determining the Occupancy of Vehicle Parking Areas by Deep Learning. / Baktir, Ayse Betul; Bolat, Bulent.
2nd International Conference on Electrical, Communication and Computer Engineering, ICECCE 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 9179399 (2nd International Conference on Electrical, Communication and Computer Engineering, ICECCE 2020).

Araştırma sonucu: Kitap/Rapor/Konferans Bildirisinde Bölüm › Konferans katkısı › bilirkişi

TY - GEN

T1 - Determining the Occupancy of Vehicle Parking Areas by Deep Learning

AU - Baktir, Ayse Betul

AU - Bolat, Bulent

PY - 2020/6

Y1 - 2020/6

N2 - Parking a vehicle in heavy traffic situations leads to prolonged driving time, deterioration of traffic flow and therefore environmental pollution when searching for free space. Although the sensor systems in the indoor parking lots are beneficial, these systems cannot be applied to outdoor spaces. In this study, a deep learning application was developed which classifies the occupancy status of the parking spaces in outdoor parking areas. High accuracy rates were obtained in this application where transfer learning was performed using ResNet model.

AB - Parking a vehicle in heavy traffic situations leads to prolonged driving time, deterioration of traffic flow and therefore environmental pollution when searching for free space. Although the sensor systems in the indoor parking lots are beneficial, these systems cannot be applied to outdoor spaces. In this study, a deep learning application was developed which classifies the occupancy status of the parking spaces in outdoor parking areas. High accuracy rates were obtained in this application where transfer learning was performed using ResNet model.

KW - CNRPark

KW - Deep Learning

KW - PKLot

KW - Parking Lot

KW - ResNet

UR - https://www.scopus.com/pages/publications/85091942037

U2 - 10.1109/ICECCE49384.2020.9179399

DO - 10.1109/ICECCE49384.2020.9179399

M3 - Conference contribution

AN - SCOPUS:85091942037

T3 - 2nd International Conference on Electrical, Communication and Computer Engineering, ICECCE 2020

BT - 2nd International Conference on Electrical, Communication and Computer Engineering, ICECCE 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2nd International Conference on Electrical, Communication and Computer Engineering, ICECCE 2020

Y2 - 12 June 2020 through 13 June 2020

ER -

Baktir AB, Bolat B. Determining the Occupancy of Vehicle Parking Areas by Deep Learning. In 2nd International Conference on Electrical, Communication and Computer Engineering, ICECCE 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 9179399. (2nd International Conference on Electrical, Communication and Computer Engineering, ICECCE 2020). doi: 10.1109/ICECCE49384.2020.9179399

Determining the Occupancy of Vehicle Parking Areas by Deep Learning

Özet

Yayın serisi

???event.eventtypes.event.conference???

Bibliyografik not

BM SKH

Belgeye Erişim

Diğer Dosyalar ve Linkler

Parmak izi

Alıntı Yap