Implementasi Google Cloud Vision untuk Deteksi Ketersediaan Lahan Parkir Kendaraan Mobil di Perguruan Tinggi
Abstract
The increasing number of car users in colleges has posed significant challenges in effectively managing parking lots. This research focuses on the implementation of Google Cloud Vision to detect and monitor the availability of car parking lots in a college environment. By utilizing cloud-based image processing services and machine learning algorithms from Google Cloud Vision, the proposed system aims to provide real-time analysis and reporting on parking slot availability. The methodology used involves capturing images of the parking area using strategically placed cameras, then uploading these images to Google Cloud Vision to detect the presence or absence of cars. The system is designed to be scalable, ensuring that it can handle different sizes and complexities of parking areas. Preliminary results show that the approach using Google Cloud Vision offers a high level of accuracy in identifying occupied and empty parking slots, thus providing a reliable tool for parking management in a college environment. This work discusses the development, implementation, and evaluation of the system, and highlights its potential in improving parking lot utilization efficiency and reducing the time users spend searching for available parking spaces.
References
[2] O. Pavlova, V. Kovalenko, T. Hovorushchenko, and V. Avsiyevych, "Neural network based image recognition method for smart parking," Comput. Syst. Inf. Technol., vol. 3, no. 1, pp. 49-55, 2021, doi: 10.31891/CSIT-2021-3-7.
[3] S. D. Khan and H. Ullah, "A survey of advances in vision-based vehicle re-identification," Comput. Vis. Image Underst., vol. 182, pp. 50-63, Mar. 2019, doi: 10.1016/j.cviu.2019.03.001.
[4] M. Dixit, C. Srimathi, R. Doss, S. Loke, and M. A. Saleemdurai, "Smart parking with computer vision and IoT technology," in 2020 43rd International Conference on Telecommunications and Signal Processing (TSP-2020), 2020, pp. 170-174, doi: 10.1109/TSP49548.2020.9163467..
[5] P. Radiuk, O. Pavlova, E. B. Houda, V. Avsiyevych, and V. Kovalenko, "Convolutional Neural Network for Parking Slots Detection," presented at the 3rd International Workshop on Intelligent Information Technologies & Systems of Information Security, Khmelnytskyi, Ukraine, Mar. 2022, pp. 31-56.
[6] J. Nyambal and R. Klein, "Automated parking space detection using convolutional neural networks," in 2017 Pattern Recognition Association of South Africa and Robotics and Mechatronics International Conference (PRASA-RobMech), 2017, pp. 1-6, doi: 10.1109/RoboMech.2017.8261114.
[7] H. Al-Absi, J. Devaraj, P. Sebastian, and V. Yap, "Vision-based automated parking system," in 2010 10th International Conference on Information Sciences Signal Processing and their Applications (ISSPA), 2010, pp. 757-760, doi: 10.1109/ISSPA.2010.5605408.
[8] R. G. Guntara, "Aplikasi Pengenalan Citra Wajah di KTP Menggunakan Google Cloud Vision API dan Kairos API Berbasis Android," ILKOMNIKA: Journal of Computer Science and Applied Informatics, vol. 4, no. 1, pp. 198-207, 2022, doi: 10.28926/ilkomnika.v4i2.504.
[9] S. Chandrasekaran, J. Reginald, W. Wang, and T. Zhu, "Computer Vision Based Parking Optimization System," Comput. Vis. Pattern Recognit., pp. 1-13, 2021, doi: arxiv.org/abs/2201.00095.
[10] Z.-Q. Zhao, P. Zheng, S.-T. Xu, and X. Wu, "Object Detection with Deep Learning: A Review," IEEE Trans. Neural Netw. Learn. Syst., vol. 30, no. 11, pp. 3212-3232, Nov. 2019, doi: 10.1109/TNNLS.2018.2876865.
[11] Q. An, H. Wang, and X. Chen, "EPSDNet: Efficient Campus Parking Space Detection via Convolutional Neural Networks and Vehicle Image Recognition for Intelligent Human–Computer Interactions," Sensors, vol. 22, Dec. 2022, pp. 9835-9853, doi: 10.3390/s22249835.
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.