Comparison of Computer Vision and Convolutional Neural Networks for Vehicle Parking Control
References
- Instituto Nacional de Estadística y Censos, “Anuario de Estadsticas de Transporte 2022,” Aug. 2022.
- D. P. Arias Sampedro and G. P. Álvarez Chamorro, “Sistema de Reconocimiento Sobre la Disponi-bilidad de Zonas para Parqueo Mediante Redes Neuronales Convolucionales con Imágenes en Tiempo Real en el Campus Sur de la Universidad Politécnica Salesiana,” 2021.
- D. C. Shoup, “Cruising for parking,” Transportation Policy (Oxford), vol. 13, no. 6, pp. 479–486, 2006.
- A. Luque-Cerpa, M. A. Gutiérrez-Naranjo, and M. Cárdenas-Montes, “Dynamic Price of Parking Service based on Deep Learning,” Jan. 2022, [Online]; http://arxiv.org/abs/2201.04188.
- Carrascal Cedeño Byron David and Cruz Zambrano Patricio Ivan, “Sistema IOT con Machine Learning para el control de estacionamiento vehicular en el comercial Miñaca del Cantón Santo Domingo,” Pontificia Universidad Católica Del Ecuador Sede Santo Domingo, Santo Domingo, 2024. [Online]; https://repositorio.puce.edu.ec/items/c0afdb e3-d8d1-4019-8c02-77438a53589c.
- S. Weerasinghe, A. Zaslavsky, A. Hassani, S. W. Loke, A. Medvedev, and A. Abken, “Context Query Simulation for Smart Carparking Scenarios in the Melbourne CDB,” 2023.
- R. Ke, Y. Zhuang, Z. Pu, and Y. Wang, “A Smart, Efficient, and Reliable Parking Surveillance System With Edge Artificial Intelligence on IoT Devices,” IEEE Transactions on Intelligent Transportation Systems, vol. 22, no. 8, pp. 4962–4974, 2021. doi: 10.1109/TITS.2020.2984197.
- A. Kalašová, K. Èulík, M. Poliak, and Z. Otahálová, “Smart parking applications and its efficiency,” Sustainability (Switzerland), vol. 13, no. 11, Jun. 2021. doi: 10.3390/su13116031.
- S. Du, M. Ibrahim, M. Shehata, and W. Badawy, “Automatic License Plate Recognition (ALPR): A State-of-the-Art Review,” IEEE Transactions on Circuits and Systems for Video Technology, vol. 23, no. 2, pp. 311–325, 2013. doi: 10.1109/TCSVT. 2012.2203741.
- M. Heimberger, J. Horgan, C. Hughes, J. McDonald, and S. Yogamani, “Computer Vision in Automated Parking Systems: Design, Implementation and Challenges,” Image and Vision Computing, vol. 68, pp. 88–101, 2017. doi: https://doi.org/10.1016/j.imavis.2017.07.002.
- S. Bhalla, V. Bhateja, A. A. Chandavale, A. S. Hiwale, and S. C. Satapathy, “Intelligent Computing and Information and Communication,” Proc. of 2nd International Conf., ICICC 2017, vol. 673. Springer, 2018.
- J. Nyambal and R. Klein, “Automated Parking Space Detection Using Convolutional Neural Networks,” 2017 Pattern Recognition Association of South Africa and Robotics and Mecha-tronics (PRASA-RobMech), 2017, pp. 1–6. doi: 10.1109/RoboMech.2017.8261114.
- L. N. Rosales Lindao, “Diseño e Implementación de un Parqueo Inteligente Utilizando Arduino yun Basado en Internet de las Cosas (loT).,” 2016.
- G. Rodriguez-Miranda, R. Santos-Orsorio, C. S. Ordaz-Banda, and J. A. Lopez-Rivera, “Estacionamiento Inteligente Smart Parking,” Revista de Ingenieria, vol. 3, no. 9, pp. 34–39, 2019.
- S. E. Shih and W. H. Tsai, “A Convenient Vision-Based System for Automatic Detection of Parking Spaces in Indoor Parking Lots Using Wide-Angle Cameras,” IEEE Transactions on Vehicular Technology, vol. 63, no. 6, pp. 2521–2532, 2014.
- S. Víatek and P. Melnièuk, “A Distributed Wireless Camera System for the Management of Parking Spaces,” Sensors, vol. 18, no. 1, p. 69, 2017.
- V. Paidi, H. Fleyeh, J. Håkansson, and R. G. Nyberg, “Smart Parking Sensors, Technologies and Applications for Open Parking Lots: Review,” IET Intelligent Transport Systems, vol. 12, no. 8, pp. 735–741, 2018.
- I. Masmoudi, A. Wali, A. M. Alimi, and A. Jamoussi, “Architecture of Parking Lots Management System for Drivers’ Guidance,” 2015 IEEE International Conf. on Systems, Man, and Cybernetics, 2015, pp. 2974–2978.
- N. Bibi, M. N. Majid, H. Dawood, and P. Guo, “Automatic Parking Space Detection System,” 2017 2nd International Conf. on Multimedia and Image Processing (ICMIP), 2017, pp. 11–15.
- R. M. Nieto, A. Garcia-Martin, A. G. Hauptmann, and J. M. Martinez, “Automatic Vacant Parking Places Management System Using Multicamera Vehicle Detection,” IEEE Transactions on Intelligent Transportation Systems, vol. 20, no. 3, pp. 1069–1080, 2018.
- P. R. L. De Almeida, L. S. Oliveira, A. S. Britto Jr, E. J. Silva Jr, and A. L. Koerich, “PKLot–A Robust Dataset for Parking Lot Classification,” Expert Systems with Applications, vol. 42, no. 11, pp. 4937–4949, 2015.
- L. Zhang, X. Li, J. Huang, Y. Shen, and D. Wang, “Vision-Based Parking-Slot Detection: A Benchmark and a Learning-Based Approach,” Symmetry (Basel), vol. 10, no. 3, p. 64, 2018.
- G. Amato, F. Carrara, F. Falchi, C. Gennaro, and C. Vairo, “Car Parking Occupancy Detection Using Smart Camera Networks and Deep Learning,” 2016 IEEE Sym. on Computers and Communication (ISCC), 2016, pp. 1212–1217.
- J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You Only Look Once: Unified, Real-Time Object Detection,” Proc. of the IEEE Conf. on Computer Vision and Pattern Recognition, 2016, pp. 779–788.
- J. Wang, Y. Chen, Z. Dong, and M. Gao, “Improved YOLOv5 Network for Real-Time Multi-Scale Traffic Sign Detection,” Neural Computing and Applications, vol. 35, no. 10, pp. 7853–7865, Apr. 2023. doi: 10.1007/s00521-022-08077-5.
- Y. Zhang, Z. Guo, J. Wu, Y. Tian, H. Tang, and X. Guo, “Real-Time Vehicle Detection Based on Improved YOLO v5,” Sustainability (Switzerland), vol. 14, no. 19, Oct. 2022. doi: 10.3390/su1419 12274.
- T. Saidani, “Deep Learning Approach: YOLOv5-based Custom Object Detection,” Engineering, Technology and Applied Science Research, vol. 13, no. 6, pp. 12158–12163, Dec. 2023, doi: 10.48084/etasr.6397.
- M. Horvat, L. Jelečević, and G. Gledec, “A Comparative Study of YOLOv5 Models Performance for Image Localization and Classification,” 2022. [Online]; https://github.com/mhorvat/YOLOv 5-models-.
- T. T. Nguyen and M. Sartipi, “Smart Camera Parking System With Auto Parking Spot Detection,” Jul. 2024, [Online]; http://arxiv.org/abs/2407.0 5469.
- J. D. Munoz Romero, “Implementación de un sistema fijo mediante el uso de la Visión Artificial para la detección de lugares disponibles en el estacionamiento vehicular del parque central de la ciudad de Baños,” 2021.
- R. A. Segarra Guzman, “Diseño de un Sistema de Parking Automático Mediante Técnicas de Vision Artificial,” 2022.
- C. O. Luis, M.-C. Isabel, O. M. Pablo, and S. T. Ximo, “Parking Space Detection in the City of Granada,” Jan. 2025, [Online]; http://arxiv.org/abs/2501.06651.
- G. P. C. P. da Luz, G. M. Sato, L. F. G. Gonzalez, and J. F. Borin, “Smart Parking with Pixel-Wise ROI Selection for Vehicle Detection Using YOLOv8, YOLOv9, YOLOv10, and YOLOv11,” Dec. 2024, [Online]; http://arxiv.org/abs/2412.01983.
- N. Thakur, E. Bhattacharjee, R. Jain, B. Acharya, and Y. C. Hu, “Deep Learning-Based Parking Occupancy Detection Framework Using ResNet and VGG-16,” Multimedia Tools and Applications, vol. 83, no. 1, pp. 1941–1964, Jan. 2024, doi: 10.1007/s11042-023-15654-w.
- C. Jang and M. Sunwoo, “Semantic Segmentation-Based Parking Space Detection with Standalone Around View Monitoring System,” Machine Vision and Application, vol. 30, no. 2, pp. 309–319, Mar. 2019. doi: 10.1007/s00138-018-0986-z.
- D. P. Arias Sampedro and G. P. Álvarez Chamorro, “Sistema de Reconocimiento Dobre la Disponi-bilidad de Zonas para Parqueo Mediante Redes Neuronales Convolucionales con Imágenes en Tiempo Real en el Campus Sur de la Universidad Politécnica Salesiana.,” 2021.
- J. Kim, J. Y. Sung, and S. Park, “Comparison of Faster-RCNN, YOLO, and SSD for Real-Time Vehicle Type Recognition,” 2020 IEEE International Conf. on Consumer Electronics-Asia (ICCE-Asia), 2020, pp. 1–4. doi: 10.1109/ICCE-Asia49877.2020.9277040.
- De-Hui Jian and Chang-Hong Lin, “Vision-Based Parking Slot Detection Based on End-to-End Semantic Segmentation Training,” 2020.
- G. S. Adi, H. Nugroho, G. M. Rahmatullah, M. Y. Fadhlan, and D. Mutamaddin, “Fusion Algorithms on Identifying Vacant Parking Spots Using Vision-Based Approach,” Indonesian Journal of Electrical Engineering and Computer Science, vol. 36, no. 3, pp. 1640–1654, Dec. 2024, doi: 10.11591/ijeecs.v36.i3.pp1640-1654.
- C. Mertz, S. Qian, and J. Chiang, “Improving Rush Hour Traffic Flow by Computer-Vision-Based Parking Detection and Regulations,” 2020.
- M. Abul Hassan, F. Ullah, S. Irfan Ullah, A. Salam, W. Ullah Khan, and M. Imad, “A Vision Based Road Blocker Detection and Distance Calculation for Intelligent Vehicles,” 2020. [Online]; https : //www.researchgate.net/publication/34261 0671.
- J. D. Trivedi, S. D. Mandalapu, and D. H. Dave, “Vision-Based Real-Time Vehicle Detection and Vehicle Speed Measurement Using Morphology and Binary Logical Operation,” Journal of Industrial Information Integration, vol. 27, May 2022, doi: 10.1016/j.jii.2021.100280.
- H. Y. Lin, J. M. Dai, L. T. Wu, and L. Q. Chen, “A Vision-Based Driver Assistance System with For-ward Collision and Overtaking Detection,” Sensors (Switzerland), vol. 20, no. 18, pp. 1–19, Sep. 2020. doi: 10.3390/s20185139.
- J. D. Trivedi, S. D. Mandalapu, and D. H. Dave, “Real-Time Parking Slot Availability for Bhavnagar, Using Statistical Block Matching Approach,” World Journal of Engineering, vol. 17, no. 6, pp. 811–821, Oct. 2020. doi: 10.1108/WJE-09-2019-0263.
- Z. Chen, J. Qiu, B. Sheng, P. Li, and E. Wu, “GPSD: Generative Parking Spot Detection Using Multi-Clue Recovery Model,” Visual Computer, vol. 37, no. 9–11, pp. 2657–2669, Sep. 2021. doi: 10.1007/s00371-021-02199-y.
Language: English
Page range: 26 - 33
Submitted on: Nov 14, 2024
Accepted on: Apr 4, 2025
Published on: Jun 26, 2025
Published by: Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP
In partnership with: Paradigm Publishing Services
Publication frequency: 4 times per year
Related subjects:
© 2025 Jonathan Aguilar Alvarado, Karina Garcia Galarza, Wilmer Rivas Asanza, Bertha Mazón Olivo, published by Łukasiewicz Research Network – Industrial Research Institute for Automation and Measurements PIAP
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.