Implementation of an Intelligent Traffic Light Control System Using Object-Counting Technique for Traffic Detection and Control

The application of object counting for a real-time traffic control system is presented in this work. The system, which uses computer vision techniques to detect and count automobiles at junctions, was created and implemented using a Raspberry Pi 400 using COCO (Common Objects in Context) dataset is a large-scale object detection, segmentation, and captioning dataset widely used in computer vision research. It provides images, bounding boxes, segmentation masks, key points, and captions for real-world objects. This method drastically cuts down on needless waiting times by dynamically adjusting signal timing based on actual traffic circumstances, unlike traditional traffic light systems that run on set time cycles. The approach simplifies the design of the control system by doing away with the requirement for intricate networking architectures that are usually used to link cameras to centralized monitoring stations. The system reduces hardware needs and increases efficiency while lowering total costs by combining detection and control into a single processing unit. According to experimental data, traffic congestion significantly decreased, and vehicle flow, and improved flexibility in contrast to conventional systems. With an overall 48.94% reduction in time delays. An economical, scalable, and successful substitute for traditional traffic control techniques is provided by the suggested approach.