Abstract
Traffic congestion is becoming a serious problem with a large number of vehicles on the roads. The present traffic system is a timer-based system that operates irrespective of the amount of traffic if there exists an ambulance. So, this Deep Learning project is designed in such a way that the traffic control system is based on vehicle density in a lane and also detecting the ambulance’s lane and let that particular lane pass considering as a first priority. In fact, we use computer vision to have the characteristics of the competing traffic rows at the signals. This is done by a object detection model based on a Deep Learning model called You Only Look Once (YOLO)v8. Then traffic signal phases are optimized according to collected data, mainly queue density and waiting time per vehicle, to enable as much as more vehicles to pass safely with minimum waiting time. Keywords— Object detection, ambulance, YOLOv8, Deep learning
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
