One of the biggest problems in cities today is the significant increase in the number of motor vehicles. Intelligent traffic control is a fundamental part of controlling city travel. To achieve this goal, it is very important to have sensor technologies capable of identifying the number of vehicles traveling on a road. In this paper, we propose the development of a classifier model capable of reliably counting the number of vehicles in urban areas. In this case, it is proposed the construction of a dataset to carry out the training of a model based on YOLOv4 and YOLOv4Tiny systems that can be embedded in intelligent traffic light systems.
Use of YOLOv4 and Yolov4Tiny for Intelligent Vehicle Detection in Smart City Environments
Advs in Intelligent Syst., Computing
International Conference on Disruptive Technologies, Tech Ethics and Artificial Intelligence ; 2022 ; Salamanca, Spain July 20, 2022 - July 22, 2022
New Trends in Disruptive Technologies, Tech Ethics and Artificial Intelligence ; Chapter : 24 ; 265-274
2022-08-28
10 pages
Article/Chapter (Book)
Electronic Resource
English
Real-Time Vehicle Detection Based on YOLOv4 Neutral Network
Springer Verlag | 2022
|A Improved Yolov4’s vehicle and pedestrian detection method
VDE-Verlag | 2022
|