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Deep Learning-Based Congestion Detection at Urban Intersections

1
School of Information Science and Technology, Qingdao University of Science and Technology, Qingdao 266061, China
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School of Information Science and Engineering, University of Jinan, Jinan 250022, China
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School of Information Science and Engineering, Shandong University, Qingdao 266237, China
*
Authors to whom correspondence should be addressed.
Academic Editors: Marco Diani and Cosimo Distante
Sensors 2021, 21(6), 2052; https://doi.org/10.3390/s21062052
Received: 13 January 2021 / Revised: 5 March 2021 / Accepted: 11 March 2021 / Published: 15 March 2021
(This article belongs to the Section Intelligent Sensors)
In this paper, a deep learning-based traffic state discrimination method is proposed to detect traffic congestion at urban intersections. The detection algorithm includes two parts, global speed detection and a traffic state discrimination algorithm. Firstly, the region of interest (ROI) is selected as the road intersection from the input image of the You Only Look Once (YOLO) v3 object detection algorithm for vehicle target detection. The Lucas-Kanade (LK) optical flow method is employed to calculate the vehicle speed. Then, the corresponding intersection state can be obtained based on the vehicle speed and the discrimination algorithm. The detection of the vehicle takes the position information obtained by YOLOv3 as the input of the LK optical flow algorithm and forms an optical flow vector to complete the vehicle speed detection. Experimental results show that the detection algorithm can detect the vehicle speed and traffic state discrimination method can judge the traffic state accurately, which has a strong anti-interference ability and meets the practical application requirements. View Full-Text
Keywords: congestion detection; image processing; optical flow; surveillance video; YOLOv3 congestion detection; image processing; optical flow; surveillance video; YOLOv3
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MDPI and ACS Style

Yang, X.; Wang, F.; Bai, Z.; Xun, F.; Zhang, Y.; Zhao, X. Deep Learning-Based Congestion Detection at Urban Intersections. Sensors 2021, 21, 2052. https://doi.org/10.3390/s21062052

AMA Style

Yang X, Wang F, Bai Z, Xun F, Zhang Y, Zhao X. Deep Learning-Based Congestion Detection at Urban Intersections. Sensors. 2021; 21(6):2052. https://doi.org/10.3390/s21062052

Chicago/Turabian Style

Yang, Xinghai, Fengjiao Wang, Zhiquan Bai, Feifei Xun, Yulin Zhang, and Xiuyang Zhao. 2021. "Deep Learning-Based Congestion Detection at Urban Intersections" Sensors 21, no. 6: 2052. https://doi.org/10.3390/s21062052

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