Enhanced Vehicle Detection and License Plate Recognition for Intelligent Transportation Systems

Within computer vision, the field of object detection has grown significantly in importance and advancement. The capability to swiftly and accurately identify and classify objects, whether within a single frame or across multiple frames, bears significant weight in a range of applications. The integration of computer vision and intelligent transportation systems has revolutionized the way that manage traffic, enforce laws, and optimize parking. The automatic identification of vehicles and the recognition of their license plates are essential components of these innovations. The initial phase of comparative analysis focuses on two stalwart methodologies, YOLO (You Only Look Once) and Faster R-CNN, in the domains of vehicle detection and classification. YOLO, celebrated for its real-time capabilities and accuracy. In contrast, Faster R-CNN, renowned for its robustness and precision. Leveraging the COCO dataset, both are trained on a diverse range of vehicle classes. The analysis unveils the relative strengths and weaknesses of these methodologies, considering performance, computational demands, and real-time viability. Furthermore, system emphasizes license plate localization, employing a fusion of YOLO or Faster R-CNN and contour-based methods to enhance accuracy and adaptability, even for tilted plates. Additionally, the use of perspective transformation enhances the system's accuracy, especially in complex scenarios. Furthermore, the study delves into license plate recognition, system evaluate two cutting-edge methods for character recognition: Transfer Learning with MobileNetV2 and a custom-designed Convolutional Neural Network (CNN) model. These techniques epitomize the highest standards of character recognition in the domain of license plate analysis. In the comparison analysis that follows, closely examining the outcomes of these character recognition methods to provide valuable insights into their respective strengths and capabilities. The integrated approach, encompassing these vital components, presents a comprehensive and real-time solution that transcends conventional limitations, underscoring its excellence in the ever-changing area of computer vision.