Enhancing Traffic Surveillance Through License Plate Detection and Recognition

In many intelligent transportation systems, license plate detection and recognition (LPDR) is essential for efficient traffic management or automated toll collection procedures. To improve the LPDR precision levels in various situations, numerous algorithms have been developed which have their own benefits and drawbacks. Most LPDR systems, on the other hand, are designed and built to function best in ideal circumstances. Unfortunately, a number of real-world circumstances, including weather, light changes, and low light levels, present numerous difficulties and impact the plate system's localization and identification. The proposed system implements YOLOv10 for real-time license plate detection to be effective in real-world environments with varying in light and weather conditions. In addition to being computationally efficient, the system is lightweight and designed to provide fast inference. There is also a strong optical character recognition (OCR) engine embedded to retrieve all the letters and numbers from the identified plates accurately. The system architecture supports video input of various qualities and processes more than one frame in real time to enable coverage of traffic monitoring application. Evaluation metrics demonstrate a high detection and recognition accuracy across multiple benchmark datasets, reflecting the model’s reliability in real-world use. Our solution is extensible for the needs of automated vehicle surveillance systems and is amenable to multiple smart traffic control systems.