Intelligent traffic control system using computer vision algorithms

Abstract

Traffic congestion has become one of the major issues in Bangladesh. The vehicle density on the road is slowly becoming greater than the road capacity and resulting in difficult commutes. This traffic delay leads to wastage of valuable time which impacts the economic development of the country. One of the main reasons for this type of road congestion is due to poor traffic management. This paper presents implementation of an intelligent traffic control system using computer vision algorithms. In this research, we propose a smart traffic management system by measuring the traffic density of the road by real time detection and image processing. The vehicle detection system counts the number of vehicles approaching a traffic signal to determine the congestion of the traffic on the road. Then traffic controller uses an algorithm to control the timings of the traffic signals, red, green and yellow, based on the number of vehicles on the road. Our system was developed by capturing real traffic video using smartphone, vehicle detection system tested in the computer and the traffic signal was implemented in Arduino hardware. Vehicle detection accuracy was increased by training a more extensive dataset with Faster R-CNN (Region-based Convolutional Neural Network) and YOLOv5 (You Only Look Once version 5) models.