Abstract
The number of vehicles on the road has increased in recent years, and many countries now require reliable and sophisticated control of the traffic signal system. By accurately calculating the traffic density based on the images captured by cameras installed on the traffic posts, this research aims to resolve this dilemma. The technique uses a simple algorithm that first processes a fuzzy controller to recognize vehicles using a Yolo classifier. It could be developed further for hardware implementation using specialized CPUs. Our method entails periodically capturing photos and processing them further with a cascade classifier. The classifier is retained for use in calibration. With the help of this method, the system can deduce the traffic density, which a fuzzy controller then evaluates to decide when to turn on the traffic signals. The output function of the fuzzy controller, which compares the vehicle density of the most recent photos, adjusts the output dynamically.