Deep Learning Based Novel Vehicle Detection and Counting System Using Intelligent Transportation Systems

It proposes deep learning-based vehicle detection and counting system designed to enhance real-time traffic monitoring and urban planning. The system employs advanced object detection models, including YOLO (You Only Look Once) and Faster R-CNN, to identify and track vehicles in various environmental conditions, such as different lighting, weather, and traffic densities. Unlike traditional sensor-based methods, which are prone to inefficiencies and high maintenance costs, this approach offers a scalable and cost-effective solution with high accuracy. The core functionality of the system revolves around the integration of robust tracking mechanisms, which enable precise vehicle counting through line-crossing or region-based techniques. By tracking vehicles across multiple frames, the system ensures accurate counts, even in the presence of occlusions or overlapping vehicles.This deep learning-based system is designed to integrate seamlessly into intelligent transportation systems, smart cities, and urban planning efforts, providing real-time data for decision-making. It offers significant improvements in traffic management, addressing the limitations of traditional vehicle detection methods. With its ability to handle complex scenarios and provide real time analytics, this system plays a crucial role in optimizing traffic flow, enhancing safety, and contributing to more efficient urban transportation systems