Call for Paper - Upcoming Issues

Object Detection Algorithms for Parking Detection - Survey

International Journal of Electrical and Electronics Engineering
© 2024 by SSRG - IJEEE Journal
Volume 11 Issue 4
Year of Publication : 2024
Authors : Wan Nazirul Hafiz Bin Abdul Rani, Lokman Mohd Fadzil
10.14445/23488379/IJEEE-V11I4P118
pdf
How to Cite?

Wan Nazirul Hafiz Bin Abdul Rani, Lokman Mohd Fadzil, "Object Detection Algorithms for Parking Detection - Survey," SSRG International Journal of Electrical and Electronics Engineering, vol. 11,  no. 4, pp. 167-174, 2024. Crossref, https://doi.org/10.14445/23488379/IJEEE-V11I4P118

Abstract:

Parking detection plays a pivotal role in the development of smart cities, aiding in the efficient management of urban parking spaces. With the advent of edge computing, devices like the NVIDIA Jetson Nano have emerged as powerful tools for real-time processing in such applications. This research aims to benchmark various object detection algorithms on the Jetson Nano to determine their efficacy and efficiency in parking detection tasks. Traditional and deep learning-based algorithms, including YOLO, Faster R-CNN, and SSD, are being evaluated in terms of accuracy, computational speed, and power consumption. Preliminary results indicate that while deep learning algorithms exhibit high accuracy, their performance varies based on the complexities of the parking environment and the computational constraints of the Jetson Nano. This study provides insights into the optimal deployment of object detection algorithms for parking detection on edge devices, paving the way for the development of cost-effective and efficient smart parking solutions.

Keywords:

Benchmarking, Deep Learning, Edge computing, Faster R-CNN, Jetson Nano, Object detection, Parking detection, Smart parking, SSD, YOLO.

References:

[1] Waheb A. Jabbar, Lu Yi Tiew, and Nadiah Y. Ali Shah, “Internet of Things Enabled Parking Management System Using Long Range Wide Area Network for Smart City,” Internet of Things and Cyber-Physical Systems, vol. 4, pp. 82-98, 2024.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Sarmad Rafique et al., “Optimized Real-Time Parking Management Framework Using Deep Learning,” Expert Systems with Applications, vol. 220, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Paulo Ricardo Lisboa de Almeida et al., “A Systematic Review on Computer Vision-Based Parking IoT Management Applied on Public Datasets,” Expert Systems with Applications, vol. 198, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[4] Wei Chen et al., “2D and 3D Object Detection Algorithms from Images: A Survey,” Array, vol. 19, pp. 1-23, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Lu Shengyu et al., “A Real-Time Object Detection Algorithm for Video,” Computers and Electrical Engineering, vol. 77, pp. 398-408, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Cheng Pin Lee et al., “Edge Computing-Enabled Secure and Energy-Efficient Smart Parking: A Review,” Microprocessors and Microsystems, vol. 93, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Hayder Sabah Salih et al., “Application of Edge Computing-Based Information-Centric Networking in Smart Cities,” Computer Communications, vol. 211, pp. 46-58, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[8] S. Shitharth et al., “Edge-Based Algorithm for Moving-Object Detection Using Background Modeling,” Computer Communications, vol. 211, pp. 46-58, 2023.
[9] Ahmet Ali Süzen, Burhan Duman, and Betül Şen, “Benchmark Analysis of Jetson TX2, Jetson Nano, and Raspberry PI Using Deep-CNN,” 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications (HORA), Ankara, Turkey, pp. 1-5, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Artiom Basulto-Lantsova et al., “Performance Comparative of OpenCV Template Matching Method on Jetson TX2 and Jetson Nano Developer Kits,” 2020 10th Annual Computing and Communication Workshop and Conference (CCWC), Las Vegas, USA, pp. 812-816, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[11] M.I. Uddin et al., “Development of an AI-Based Traffic Control System Using Deepstream and IoT on Jetson Nano,” 2021 11th International Conference on Advanced Computer Information Technologies (ACIT), Deggendorf, Germany, pp. 1-5, 2021.
[12] Ratko Grbić, and Brando Koch, “Automatic Vision-Based Parking Slot Detection and Occupancy Classification,” Expert Systems with Applications, vol. 225, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Zoja Šćekić et al., “Image-Based Parking Occupancy Detection Using Deep Learning and Faster R-CNN,” 2022 26th International Conference on Information Technology (IT), Zabljak, Montenegro, pp. 1-5, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Ramadhan Admiral Hamzah et al., “Parking Violation Detection on the Roadside of Toll Roads with Intelligent Transportation System Using Faster R-CNN Algorithm,” 2022 6th International Conference on Informatics and Computational Sciences (ICICoS), Semarang, Indonesia, pp. 169-174, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Xinggan Peng et al., “Real-Time Illegal Parking Detection Algorithm in Urban Environments,” IEEE Transactions on Intelligent Transportation Systems, vol. 23, no. 11, pp. 20572-20587, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Galib Ibne Haidar, and Hasin Ishraq Reefat, “Smart Parking System Using MobileNet and Single Shot Detection Algorithm and IoT,” 2020 11th International Conference on Electrical and Computer Engineering (ICECE), Dhaka, Bangladesh, pp. 69-72, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[17] Raj Patel, and Praveen Meduri, “Car Detection Based Algorithm for Automatic Parking Space Detection,” 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA), Miami, USA, pp. 1418-1423, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[18] Yuxin Song et al., “Vision-Based Parking Space Detection: A Mask R-CNN Approach,” 2021 IEEE/CIC International Conference on Communications in China (ICCC), Xiamen, China, pp. 300-305, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[19] Caroline Potts et al., “ParkingSticker: A Real-World Object Detection Dataset,” ArXiv, pp. 1-8, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[20] Judith Nkechinyere Njoku et al., “State-of-the-Art Object Detectors for Vehicle, Pedestrian, and Traffic Sign Detection for Smart Parking Systems,” 2022 13th International Conference on Information and Communication Technology Convergence (ICTC), Jeju Island, Korea, pp. 1585-1590, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[21] Stephan Nebiker et al., “Outdoor Mobile Mapping and AI-Based 3D Object Detection with Low-Cost RGB-D Cameras: The Use Case of On-Street Parking Statistics,” Remote Sensing, vol. 13, no. 16, pp. 1-23, 2021.
[CrossRef] [Google Scholar] [Publisher Link]