Document Type : ORIGINAL RESEARCH ARTICLE
1 School of Engineering, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
2 Department of Mathematics, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
3 Department of Electrical Engineering, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
4 Graduate School of Mathematics and Applied Sciences, Universitas Syiah Kuala, Banda Aceh 23111, Indonesia
BACKGROUND AND OBJECTIVES: The increase in the number of vehicles has several negative impacts, including traffic congestion, air pollution, noise levels, and the availability of parking spaces. Drivers looking for parking spaces can cause traffic jams and air pollution. The solution offered at this time is the development of a smart parking system to overcome these problems. The smart parking system offers a parking availability information feature in a parking area to break up congestion in the parking space. Deep learning is a successful method to solve parking space classification problems. It is known that this method requires a large computational process. Th aims of this study are to modified the architecture of Convolutional Neural Networks, part of deep learning to classify parking spaces. Modification of the Convolutional Neural Networks architecture is assumed to increase the work efficiency of the smart parking system in processing parking availability information.
METHODS: Research is focusing on developing parking space classification techniques using camera sensors due to the rapid advancement of technology and algorithms in computer vision. The input image has 3x3 dimensions. The first convolution layer accepts the input image and converts it into 56x56 dimensions. The second convolution layer is composed in the same way as the first layer with dimensions of 25x25. The third convolution layer employs a 3 x 3 filter matrix with padding of up to 15 and converts it into 10x10 dimensions. The fourth layer is composed in the same way as the third layer, but with the addition of maximum pooling. The software used in the test is Python with a Python framework.
FINDINGS: The proposed architecture is the Efficient Parking Network or EfficientParkingNet. It can be shown that this architecture is more efficient in classifying parking spaces compared to some other architectures, such as the mini–Alex Network (mAlexnet) and the Grassmannian Deep Stacking Network with Illumination Correction (GDSN-IC). EfficientParkingNet has not been able to pass the accuracy of Yolo Mobile Network (Yolo+MobileNet). Furthermore, Yolo+MobileNet has so many parameters that it cannot be used on low computing devices. Selection of EfficientParkingNet as a lightweight architecture tailored to the needs of use. EfficientParkingNet's lightweight computing architecture can increase the speed of information on parking availability to users.
CONCLUSION: EfficientParkingNet is more efficient in determining the availability of parking spaces compared to mAlexnet, but still cannot match Yolo+MobileNet. Based on the number of parameters, EfficientParkingNet uses half of the number of parameters of mAlexnet and is much smaller than Yolo+MobileNet. EfficientParkingNet has an accuracy rate of 98.44% for the National Research Council parking dataset and higher than other architectures. EfficientParkingNet is suitable for use in parking systems with low computing devices such as the Raspberry Pi because of the small number of parameters.
- EffecientParkingNet is small architecture developed based on mAlexnet architecture with four layers of convolution and LeakyRelu as an activation function;
- The accuracy level of EffecientParkingNet is better than the two previous studies which classified the sub dataset of CNRPark+EXT Test using CNRPark_EXT TRAIN C1-C8 training data;
- EfficientParkingNet is better than mAlexnet and GDSN-IC, and EfficientParkingNet are close to the results of Yolo+MobileNet;
- EfficientParkingNet is suitable to be used on low computing devices such as the Raspberry Pi.
©2022 The author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit: http://creativecommons.org/licenses/by/4.0/
GJESM Publisher remains neutral concerning jurisdictional claims in published maps and institutional affliations.
Citation Metrics & Captures
Google Scholar | Scopus | Web of Science | PlumX Metrics | Altmetrics | Mendeley |
Letters to Editor
 Letters that include statements of statistics, facts, research, or theories should include appropriate references, although more than three are discouraged.
 Letters that are personal attacks on an author rather than thoughtful criticism of the author’s ideas will not be considered for publication.
 Letters can be no more than 300 words in length.
 Letter writers should include a statement at the beginning of the letter stating that it is being submitted either for publication or not.
 Anonymous letters will not be considered.
 Letter writers must include their city and state of residence or work.
 Letters will be edited for clarity and length.
Send comment about this article