Overview of License Plate Detection Approaches Based on IoT and AI Models

This paper provides a comprehensive analysis of the most recent developments in License Plate Recognition (LPR) systems, emphasizing the convergence of artificial intelligence (AI), especially deep learning methods, with Internet of Things (IoT) technology. The study covers the history of LPR systems across time, starting with the initial breakthroughs in image processing and moving on to the ground-breaking incorporation of machine learning and deep learning models. It highlights how IoT can improve LPR’s effectiveness and versatility in a range of urban and automotive settings. In the context of real-time object identification and classification, the paper critically assesses a number of methods and frameworks, including SSD (Single Shot MultiBox Detector), Faster R-CNN, and YOLO (You Only Look Once). The study also looks at the difficulties encountered in the field, such as problems with various environmental circumstances, different license plate layouts, and high-speed vehicle identification. The study concludes with a discussion of the significance of these technologies in applications for smart cities, including information about possible improvements to vehicle monitoring and urban surveillance systems as well as future directions.