1
Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA
(azaki@jcsis.org)
2
Computer Science and Intelligent Systems Research Center, Blacksburg 24060, Virginia, USA
(sktowfek@jcsis.org)
3
School of Computer Systems, Hebei University of Engineering, Handan, Hebei, 056038, China
(Weiguo.Gee@hotmail.com)
4
School of Earth and Space Sciences, Peking University, Beijing, 100871, China
(W_Zhang@chinamail.com)
5
Department of Architecture, Delta Higher Institute of Engineering and Technology, Mansoura, Egypt; Department of Architecture, Faculty of Engineering, Mansoura University, Mansoura, Egypt
(marwa_elfiky@mans.edu.eg)
Abstract :
This research endeavors to advance the realm of parking space surveillance through a meticulously designed methodology situated within the critical context of urban planning and the dynamic landscape of smart city development. Focused on addressing the challenges posed by escalating urbanization and burgeoning vehicular density, our study introduces a carefully curated dataset comprising images of parking spaces annotated with bounding box masks and occupancy labels. The methodology unfolds across distinct phases, commencing with a comprehensive dataset description that unveils its diversity and intricacies. Feature extraction techniques, harnessing the capabilities of cutting-edge architectures such as AlexNet and ResNet-50, play a pivotal role in enhancing pattern discernment, which is essential for accurate detection. The crux of our approach lies in the integration of Neural Networks with optimization algorithms, including Particle Swarm Optimization (PSO), Grey Wolf Optimization (GWO), and the innovative Dipper Throated Optimization (DTO). Results are presented without explicit mention of tables and figures, strategically emphasizing the methodology's effectiveness in enhancing parking space detection accuracy. Notably, Dipper Throated Optimization (DTO) emerges as a key contributor to optimized Neural Network performance, achieving an impressive accuracy of 0.9908. This research contributes significantly to the ongoing discourse on intelligent urban planning and sets a promising trajectory for the future of efficient parking space utilization in modern cities.
Keywords :
parking space detection system; urban planning; smart city development; object detection; optimization algorithms; Dipper Throated Optimization
References :
[1] Gkolias, K., & Vlahogianni, E. I. (2019). Convolutional Neural Networks for On-Street Parking Space Detection in Urban Networks. IEEE Transactions on Intelligent Transportation Systems, 20(12), 4318–4327. https://doi.org/10.1109/TITS.2018.2882439
[2] Zhuang, Y., Pu, Z., Yang, H., & Wang, Y. (2022). Edge–Artificial Intelligence-Powered Parking Surveillance With Quantized Neural Networks. IEEE Intelligent Transportation Systems Magazine, 14(6), 107–121. https://doi.org/10.1109/MITS.2022.3182358
[3] Ke, R., Zhuang, Y., Pu, Z., & Wang, Y. (2021). A Smart, Efficient, and Reliable Parking Surveillance System With Edge Artificial Intelligence on IoT Devices. IEEE Transactions on Intelligent Transportation Systems, 22(8), 4962–4974. https://doi.org/10.1109/TITS.2020.2984197
[4] Naveed, Q. N., Alqahtani, H., Khan, R. U., Almakdi, S., Alshehri, M., & Abdul Rasheed, M. A. (2022). An Intelligent Traffic Surveillance System Using Integrated Wireless Sensor Network and Improved Phase Timing Optimization. Sensors, 22(9), Article 9. https://doi.org/10.3390/s22093333
[5] Jiang, H., Liu, J., Cheng, H.-W., & Zhang, Y. (2017). Particle swarm optimization based space debris surveillance network scheduling. Research in Astronomy and Astrophysics, 17(3), 30. https://doi.org/10.1088/1674-4527/17/3/30
[6] Rathore, M. M., Ahmad, A., Paul, A., & Rho, S. (2016). Urban planning and building smart cities based on the Internet of Things using Big Data analytics. Computer Networks, 101, 63–80. https://doi.org/10.1016/j.comnet.2015.12.023
[7] Jagannathan, J., & Divya, C. (2021). Deep learning for the prediction and classification of land use and land cover changes using deep convolutional neural network. Ecological Informatics, 65, 101412. https://doi.org/10.1016/j.ecoinf.2021.101412
[8] Mao, W., Lu, D., Hou, L., Liu, X., & Yue, W. (2020). Comparison of Machine-Learning Methods for Urban Land-Use Mapping in Hangzhou City, China. Remote Sensing, 12(17), Article 17. https://doi.org/10.3390/rs12172817
[9] Zhang, C., Sargent, I., Pan, X., Li, H., Gardiner, A., Hare, J., & Atkinson, P. M. (2018). An object-based convolutional neural network (OCNN) for urban land use classification. Remote Sensing of Environment, 216, 57–70. https://doi.org/10.1016/j.rse.2018.06.034
[10] Ramasamy, S., Selvarajan, A., Kaliyaperumal, V., & Aruchamy, P. (2023). A hybrid location-dependent ultra convolutional neural network-based vehicle number plate recognition approach for intelligent transportation systems. Concurrency and Computation: Practice and Experience, 35(8), e7615. https://doi.org/10.1002/cpe.7615
[11] Khafaga, D., Karim, F., Abdelhamid, A., El-kenawy, E.-S., Alkahtani, H., Khodadadi, N., Hadwan, M., & Ibrahim, A. (2022). Voting Classifier and Metaheuristic Optimization for Network Intrusion Detection. Computers, Materials & Continua, 74(2), 3183–3198. https://doi.org/10.32604/cmc.2023.033513
[12] Darapaneni, N., Mogeraya, K., Mandal, S., Narayanan, A., Siva, P., Paduri, A. R., Khan, F., & Agadi, P. M. (2020). Computer Vision based License Plate Detection for Automated Vehicle Parking Management System. 2020 11th IEEE Annual Ubiquitous Computing, Electronics & Mobile Communication Conference (UEMCON), 0800–0805. https://doi.org/10.1109/UEMCON51285.2020.9298091
[13] Schulte, M. R., Thiée, L.-W., Scharfenberger, J., & Funk, B. (2021). Parking Space Management Through Deep Learning – An Approach for Automated, Low-Cost and Scalable Real-Time Detection of Parking Space Occupancy. In F. Ahlemann, R. Schütte, & S. Stieglitz (Eds.), Innovation Through Information Systems (pp. 642–655). Springer International Publishing. https://doi.org/10.1007/978-3-030-86797-3_42
[14] Parking Space Detection & Classification Dataset. (n.d.). [dataset]. Retrieved December 21, 2023, from https://www.kaggle.com/datasets/trainingdatapro/parking-space-detection-dataset.
[15] Rajpal, S., Lakhyani, N., Singh, A. K., Kohli, R., & Kumar, N. (2021). Using handpicked features in conjunction with ResNet-50 for improved detection of COVID-19 from chest X-ray images. Chaos, Solitons & Fractals, 145, 110749. https://doi.org/10.1016/j.chaos.2021.110749
| Style | # |
|---|---|
| MLA | Ahmed Mohamed Zaki, S. K. Towfek, Weiguo Gee, Wang Zhang, Marwa Adel Soliman. "Advancing Parking Space Surveillance using A Neural Network Approach with Feature Extraction and Dipper Throated Optimization Integration." Journal of Artificial Intelligence and Metaheuristics, Vol. 6, No. 2, 2023 ,PP. 16-25 (Doi : https://doi.org/10.54216/JAIM.060202) |
| APA | Ahmed Mohamed Zaki, S. K. Towfek, Weiguo Gee, Wang Zhang, Marwa Adel Soliman. (2023). Advancing Parking Space Surveillance using A Neural Network Approach with Feature Extraction and Dipper Throated Optimization Integration. Journal of Journal of Artificial Intelligence and Metaheuristics, 6 ( 2 ), 16-25 (Doi : https://doi.org/10.54216/JAIM.060202) |
| Chicago | Ahmed Mohamed Zaki, S. K. Towfek, Weiguo Gee, Wang Zhang, Marwa Adel Soliman. "Advancing Parking Space Surveillance using A Neural Network Approach with Feature Extraction and Dipper Throated Optimization Integration." Journal of Journal of Artificial Intelligence and Metaheuristics, 6 no. 2 (2023): 16-25 (Doi : https://doi.org/10.54216/JAIM.060202) |
| Harvard | Ahmed Mohamed Zaki, S. K. Towfek, Weiguo Gee, Wang Zhang, Marwa Adel Soliman. (2023). Advancing Parking Space Surveillance using A Neural Network Approach with Feature Extraction and Dipper Throated Optimization Integration. Journal of Journal of Artificial Intelligence and Metaheuristics, 6 ( 2 ), 16-25 (Doi : https://doi.org/10.54216/JAIM.060202) |
| Vancouver | Ahmed Mohamed Zaki, S. K. Towfek, Weiguo Gee, Wang Zhang, Marwa Adel Soliman. Advancing Parking Space Surveillance using A Neural Network Approach with Feature Extraction and Dipper Throated Optimization Integration. Journal of Journal of Artificial Intelligence and Metaheuristics, (2023); 6 ( 2 ): 16-25 (Doi : https://doi.org/10.54216/JAIM.060202) |
| IEEE | Ahmed Mohamed Zaki, S. K. Towfek, Weiguo Gee, Wang Zhang, Marwa Adel Soliman, Advancing Parking Space Surveillance using A Neural Network Approach with Feature Extraction and Dipper Throated Optimization Integration, Journal of Journal of Artificial Intelligence and Metaheuristics, Vol. 6 , No. 2 , (2023) : 16-25 (Doi : https://doi.org/10.54216/JAIM.060202) |