TY - GEN
T1 - One Convolutional Layer Model for Parking Occupancy Detection
AU - Goumiri, Soumia
AU - Benboudjema, Dalila
AU - Pieczynski, Wojciech
N1 - Publisher Copyright:
© 2021 IEEE.
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 -
