Volume 16 • Issue 1 • PP: 132-141 • 2025
Implement Intelligent YOLOv8 for Car Crowd Detection and Counting in the Roads
View PDF Abstract
Car crowd management refers to the process of efficiently and safely managing the movement and flow of cars in crowded areas, such as parking lots, traffic intersections, event venues, and busy streets. Effective car crowd management is essential to ensure smooth traffic flow, prevent accidents, reduce congestion, and optimize the utilization of available parking spaces. It is a critical aspect of urban planning and traffic management to enhance the overall transportation experience and safety for both drivers and pedestrians. Deep learning methods are used to create an artificial system that is shown in this study. Proposed in detecting cars in streets and traffic intersections, in addition to determining the quantity of cars based on the YOLOv8 algorithm. Where the proposed system was trained on three types of datasets for the purpose of testing the algorithm used to determine the number of cars in each direction of the traffic intersection and then give priority to the most crowded direction with cars and then less and less. Where the system reached a high accuracy in detecting cars, reaching 98%, and through it conclude that the YOLOv8 algorithm used was suitable to be employed in solving the problem of determining the priority of traffic by identifying places of congestion with high accuracy.
Keywords
References
[1] D. K. Dewangan and S. P. Sahu, "RCNet: road classification convolutional neural networks for intelligent vehicle system," Intelligent Service Robotics, vol. 14, pp. 199–214, 2021.
[2] S. Yassine and A. Stanulov, "A comparative analysis of machine learning algorithms for the purpose of predicting Norwegian air passenger traffic," Int. J. Math., Stat. Comput. Sci., vol. 2, pp. 28–43, 2024. doi: 10.59543/ijmscs.v2i.7851.
[3] M. A. B. Zuraimi and F. H. K. Zaman, "Vehicle detection and tracking using YOLO and DeepSORT," in Proc. 2021 IEEE 11th IEEE Symposium on Computer Applications & Industrial Electronics (ISCAIE), 2021.
[4] W. Farag, "A comprehensive vehicle-detection-and-tracking technique for autonomous driving," Int. J. Comput. Digit. Syst., vol. 9, no. 4, pp. 567–580, 2020.
[5] J. Azimjonov and A. Özmen, "A real-time vehicle detection and a novel vehicle tracking system for estimating and monitoring traffic flow on highways," Adv. Eng. Informatics, vol. 50, no. 11, pp. 101393, 2021.
[6] M. Contreras and E. Gamess, "Real-time counting of vehicles stopped at a traffic light using vehicular network technology," IEEE Access, vol. 8, pp. 135244–135263, 2020.
[7] V. Mandal and Y. Adu-Gyamfi, "Object detection and tracking algorithms for vehicle counting: a comparative analysis," J. Big Data Anal. Transp., vol. 2, pp. 251–261, 2020.
[8] A. Appathurai, R. Sundarasekar, C. Raja, E. J. Alex, C. A. Palagan, and A. Nithya, "An efficient optimal neural network-based moving vehicle detection in traffic video surveillance system," Circuits Syst. Signal Process., vol. 39, pp. 734–756, 2020.
[9] N. Balamuralidhar, S. Tilon, and F. Nex, "MultEYE: Monitoring system for real-time vehicle detection, tracking, and speed estimation from UAV imagery on edge-computing platforms," Remote Sensing, vol. 13, no. 4, pp. 573, 2021.
[10] X. Luo, X. Tian, H. Zhang, W. Hou, G. Leng, W. Xu, H. Jia, X. He, M. Wang, and J. Zhang, "Fast automatic vehicle detection in UAV images using convolutional neural networks," Remote Sensing, vol. 12, no. 12, pp. 1994, 2020.
[11] R. Ghosh, "On-road vehicle detection in varying weather conditions using faster R-CNN with several region proposal networks," Multimedia Tools Appl., vol. 80, no. 17, pp. 25985–25999, 2021.
[12] A. Ojha, S. P. Sahu, and D. K. Dewangan, "Vehicle detection through instance segmentation using mask R-CNN for intelligent vehicle system," in Proc. 2021 5th Int. Conf. Intell. Comput. Control Syst. (ICICCS), IEEE, 2021.
[13] A. Gomaa, T. Minematsu, M. M. Abdelwahab, M. Abo-Zahhad, R. Taniguchi, "Faster CNN-based vehicle detection and counting strategy for fixed camera scenes," Multimedia Tools Appl., vol. 81, no. 18, pp. 25443–25471, 2022.
[14] Z. Chen, L. Cao, and Q. Wang, "YOLOv5-based vehicle detection method for high-resolution UAV images," Mobile Inf. Syst., vol. 2022, no. 6, 2022.
[15] J. L. Andika, A. S. M. Khairuddin, H. Ramiah, and J. Kanesan, "Improved feature extraction network in lightweight YOLOv7 model for real-time vehicle detection on low-cost hardware," J. Real-Time Image Process., vol. 21, no. 3, pp. 1–12, 2024.
Cite This Article
Choose your preferred format