Real-time Traffic Monitoring System Based on Deep Learning and YOLOv8

Neamah, Saif B. and Karim, Abdulamir A. (2023) Real-time Traffic Monitoring System Based on Deep Learning and YOLOv8. ARO-THE SCIENTIFIC JOURNAL OF KOYA UNIVERSITY, 11 (2). pp. 137-150. ISSN 2410-9355

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Abstract

Computer vision applications are important nowadays because they provide solutions to critical problems that relate to traffic in a cost-effective manner to reduce accidents and preserve lives. This paper proposes a system for real-time traffic monitoring based on cutting-edge deep learning techniques through the state-of-the-art you-only-look-once v8 algorithm, benefiting from its functionalities to provide vehicle detection, classification, and segmentation. The proposed work provides various important traffic information, including vehicle counting, classification, speed estimation, and size estimation. This information helps enforce traffic laws. The proposed system consists of five stages: The preprocessing stage, which includes camera calibration, ROI calculation, and preparing the source video input; the vehicle detection stage, which uses the convolutional neural network model to localize vehicles in the video frames; the tracking stage, which uses the ByteTrack algorithm to track the detected vehicles; the speed estimation stage, which estimates the speed for the tracked vehicles; and the size estimation stage, which estimates the vehicle size. The results of the proposed system running on the Nvidia GTX 1070 GPU show that the detection and tracking stages have an average accuracy of 96.58% with an average error of 3.42%, the vehicle counting stage has an average accuracy of 97.54% with a 2.46% average error, the speed estimation stage has an average accuracy of 96.75% with a 3.25% average error, and the size estimation stage has an average accuracy of 87.28% with a 12.72% average error.

Item Type:	Article
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DOI: https://doi.org/10.3991/ijoe.v17i02.18013 Zhang, Y., Sun, P., Jiang, Y., Yu, D., Weng, F., Yuan, Z., Luo, P., Liu, W., and Wang, X., 2022. ByteTrack: Multi-Object Tracking by Associating Every Detection Box, Computer Vision and Pattern Recognition, Cornell University, New York, arXiv:2110.06864, pp.1-14. DOI: https://doi.org/10.1007/978-3-031-20047-2_1
Uncontrolled Keywords:	Computer vision, Deep learning, Object detection, Traffic monitoring, YOLOv8
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions:	ARO-The Scientific Journal of Koya University > VOL 11, NO 2 (2023)
Depositing User:	Dr Salah Ismaeel Yahya
Date Deposited:	30 Nov 2023 08:38
Last Modified:	30 Nov 2023 08:38
URI:	http://eprints.koyauniversity.org/id/eprint/440

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