Intelligent and Smart Traffic System Based on Yo-Lo Version-8 through Video Streaming for Longer Distance Level

The creation of intelligent traffic systems is essential in the age of smart cities and developing technologies in order to guarantee effective and secure transportation networks. This study uses the state-of-the-art YOLO (You Only Look Once) v8 deep learning architecture to give a novel solution to the problems of real-time car and pedestrian recognition in video frames. The suggested method is centered on obtaining useful data from video streams, especially when it comes to recognizing cars and pedestrians in individual frames. In this context, we show that YOLO version 8 is accurate and effective through a series of thorough trials and assessments. YOLO version 8 is used for object recognition after pictures from video frames are captured as part of the research method. Our technology can quickly recognize and track automobiles and people by using real- time visual analysis. We also look at how this technology may be used in driverless cars, traffic control, and spying. The report also explores the technological aspects of YOLO version 8, emphasizing its advancements over earlier versions, including increased resilience, speed, and accuracy. We go over the process of training and fine-tuning needed for best results when it comes to traffic monitoring. We demonstrate the system’s capacity to properly recognize cars and pedestrians, even in difficult settings like changing illumination conditions and occlusions, by thorough testing and validation on many datasets. The findings demonstrate how this technology has the ability to completely transform traffic management and improve road safety. Finally, using YOLO version 8 for real-time car and pedestrian recognition in video frames, this research study provides an intelligent traffic system. The technology exhibits encouraging outcomes, laying the path for further advancements in intelligent transportation, traffic optimization, and public safety.