A Comparative Study on Thai License Plate Recognition: Object Detection and Transformer Learning Approaches

Car theft remains a significant issue in Thailand, necessitating advanced security solutions. This research investigates the application of License Plate Recognition (LPR) technology using deep learning models for vehicle and license plate detection, as well as both YOLO and Transformer models for license plate reading. Experimental results demonstrate that YOLOv5nu achieved a high Mean Average Precision (mAP) of 0.995 for license plate detection, while the Transformer model excelled in license plate reading with an accuracy of 0.9108 and a loss of 0.0326. Despite these promising results, the models faced limitations in low-light conditions and complex plate layouts, affecting detection accuracy. Future work should focus on enhancing the models’ adaptability to real-world environments, expanding datasets to include diverse scenarios, and improving robustness against environmental variations. These findings underscore the potential of AI-driven LPR systems in advancing vehicle security technology.