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Title	Contribution Determination Of The Statistical Loading Of The Crossroads By Means Of The Yolo5 Neural Network
ID_Doc	16000
Authors	Kazinski A.; Puptsau A.
Year	2023
Published	Lecture Notes in Networks and Systems, 640 LNNS
DOI	http://dx.doi.org/10.1007/978-3-031-26655-3_2
Abstract	The effectiveness of publicly available CCTV cameras should be significantly improved. Currently, webcams only allow users to observe the current situation in the online mode. Users do not have the opportunity to receive additional information for decision-making. This is due to the fact that there are not enough services on the network for automatic collection and processing of video information. Such services are not available to a wide range of users. The data obtained is used by a small number of companies and organizations. In this article, we describe the study of a neural network of the YOLO family for detecting and tracking road users. First, we offer a variant of training the YOLOv5 neural network on the assembled dataset. We have analyzed the qualitative indicators of training. Secondly, the method of collecting statistical loading of the intersection by traffic participants is described. The obtained results were used to create a web application for an operational assessment of situations that deserve attention. Situations that require attention are based on the time parameters of detected objects. The results of the study can be used in the development of the “Smart City” concept. Such a concept should be developed on the basis of publicly available open source access services. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
Author Keywords	Emergency situations; Traffic intensity; Urban transport; YOLOv5

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