One Convolutional Layer Model for Parking Occupancy Detection

Soumia Goumiri; Dalila Benboudjema; Wojciech Pieczynski

doi:10.1109/ISC253183.2021.9562946

One Convolutional Layer Model for Parking Occupancy Detection

Soumia Goumiri
, Dalila Benboudjema
, Wojciech Pieczynski

Natl. Institute of Computer Science

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

6 Citations (Scopus)

Résumé

Convolutional Neural Networks (CNN) have recently performed wonders in image recognition tasks. In this paper, we propose a new CNN model composed of one convolutional layer, which we called 1Conv. We apply 1Conv for the problem of parking space detection. We used the most popular datasets to evaluate the performance of our model that are: National Research Council Park (CNRPark), National Research Council Park Extension (\mathbf{CNRPark}+\mathbf{EXT}), and Parking Lot (PKLot). We compared the results with mAlexNet, a CNN model similar to 1Conv. The results show that our model outperforms mAlexNet in terms of accuracy, Area Under the Curve (AUC), and execution time. The better accuracy of 1 Conv compared to mAlexNet was 99.06% against 90.71 % using CNRPark dataset. Which means that our model outperforms mAlexNet by 9% in term of accuracy. Execution time of mAlexNet is double compared to 1Conv.

langue originale	Anglais
titre	2021 IEEE International Smart Cities Conference, ISC2 2021
Editeur	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronique)	9781665449199
Les DOIs	https://doi.org/10.1109/ISC253183.2021.9562946
état	Publié - 7 sept. 2021
Evénement	2021 IEEE International Smart Cities Conference, ISC2 2021 - Manchester, Royaume-Uni Durée: 7 sept. 2021 → 10 sept. 2021

Série de publications

Nom	2021 IEEE International Smart Cities Conference, ISC2 2021

Une conférence

Une conférence	2021 IEEE International Smart Cities Conference, ISC2 2021
Pays/Territoire	Royaume-Uni
La ville	Manchester
période	7/09/21 → 10/09/21

SDG des Nations Unies

Ce résultat contribue à ou aux Objectifs de développement durable suivants

SDG 7 Énergie abordable et propre
SDG 11 Villes et communautés durables

Accès au document

10.1109/ISC253183.2021.9562946

Contient cette citation

@inproceedings{42a19f211e344845992520b122dbbea5,

title = "One Convolutional Layer Model for Parking Occupancy Detection",

abstract = "Convolutional Neural Networks (CNN) have recently performed wonders in image recognition tasks. In this paper, we propose a new CNN model composed of one convolutional layer, which we called 1Conv. We apply 1Conv for the problem of parking space detection. We used the most popular datasets to evaluate the performance of our model that are: National Research Council Park (CNRPark), National Research Council Park Extension (\textbackslash{}mathbf\{CNRPark\}+\textbackslash{}mathbf\{EXT\}), and Parking Lot (PKLot). We compared the results with mAlexNet, a CNN model similar to 1Conv. The results show that our model outperforms mAlexNet in terms of accuracy, Area Under the Curve (AUC), and execution time. The better accuracy of 1 Conv compared to mAlexNet was 99.06\% against 90.71 \% using CNRPark dataset. Which means that our model outperforms mAlexNet by 9\% in term of accuracy. Execution time of mAlexNet is double compared to 1Conv.",

keywords = "Convolutional Neural Networks (CNN) model, deep learning, parking space detection",

author = "Soumia Goumiri and Dalila Benboudjema and Wojciech Pieczynski",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Smart Cities Conference, ISC2 2021 ; Conference date: 07-09-2021 Through 10-09-2021",

year = "2021",

month = sep,

day = "7",

doi = "10.1109/ISC253183.2021.9562946",

language = "English",

series = "2021 IEEE International Smart Cities Conference, ISC2 2021",

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

booktitle = "2021 IEEE International Smart Cities Conference, ISC2 2021",

}

Goumiri, S, Benboudjema, D & Pieczynski, W 2021, One Convolutional Layer Model for Parking Occupancy Detection. Dans 2021 IEEE International Smart Cities Conference, ISC2 2021. 2021 IEEE International Smart Cities Conference, ISC2 2021, Institute of Electrical and Electronics Engineers Inc., 2021 IEEE International Smart Cities Conference, ISC2 2021, Manchester, Royaume-Uni, 7/09/21. https://doi.org/10.1109/ISC253183.2021.9562946

One Convolutional Layer Model for Parking Occupancy Detection. / Goumiri, Soumia; Benboudjema, Dalila; Pieczynski, Wojciech.
2021 IEEE International Smart Cities Conference, ISC2 2021. Institute of Electrical and Electronics Engineers Inc., 2021. (2021 IEEE International Smart Cities Conference, ISC2 2021).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

TY - GEN

T1 - One Convolutional Layer Model for Parking Occupancy Detection

AU - Goumiri, Soumia

AU - Benboudjema, Dalila

AU - Pieczynski, Wojciech

PY - 2021/9/7

Y1 - 2021/9/7

N2 - Convolutional Neural Networks (CNN) have recently performed wonders in image recognition tasks. In this paper, we propose a new CNN model composed of one convolutional layer, which we called 1Conv. We apply 1Conv for the problem of parking space detection. We used the most popular datasets to evaluate the performance of our model that are: National Research Council Park (CNRPark), National Research Council Park Extension (\mathbf{CNRPark}+\mathbf{EXT}), and Parking Lot (PKLot). We compared the results with mAlexNet, a CNN model similar to 1Conv. The results show that our model outperforms mAlexNet in terms of accuracy, Area Under the Curve (AUC), and execution time. The better accuracy of 1 Conv compared to mAlexNet was 99.06% against 90.71 % using CNRPark dataset. Which means that our model outperforms mAlexNet by 9% in term of accuracy. Execution time of mAlexNet is double compared to 1Conv.

AB - Convolutional Neural Networks (CNN) have recently performed wonders in image recognition tasks. In this paper, we propose a new CNN model composed of one convolutional layer, which we called 1Conv. We apply 1Conv for the problem of parking space detection. We used the most popular datasets to evaluate the performance of our model that are: National Research Council Park (CNRPark), National Research Council Park Extension (\mathbf{CNRPark}+\mathbf{EXT}), and Parking Lot (PKLot). We compared the results with mAlexNet, a CNN model similar to 1Conv. The results show that our model outperforms mAlexNet in terms of accuracy, Area Under the Curve (AUC), and execution time. The better accuracy of 1 Conv compared to mAlexNet was 99.06% against 90.71 % using CNRPark dataset. Which means that our model outperforms mAlexNet by 9% in term of accuracy. Execution time of mAlexNet is double compared to 1Conv.

KW - Convolutional Neural Networks (CNN) model

KW - deep learning

KW - parking space detection

U2 - 10.1109/ISC253183.2021.9562946

DO - 10.1109/ISC253183.2021.9562946

M3 - Conference contribution

AN - SCOPUS:85118153579

T3 - 2021 IEEE International Smart Cities Conference, ISC2 2021

BT - 2021 IEEE International Smart Cities Conference, ISC2 2021

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE International Smart Cities Conference, ISC2 2021

Y2 - 7 September 2021 through 10 September 2021

ER -

One Convolutional Layer Model for Parking Occupancy Detection

Résumé

Série de publications

Une conférence

SDG des Nations Unies

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