Deep Learning-Based Turkish License Plate Recognition System on Low-Power Microcontroller Systems

Automatic License Plate Recognition (ALPR) systems play a crucial role in intelligent transportation systems, traffic management, and security. However, ensuring that these systems operate efficiently on embedded devices with low power consumption presents a significant challenge. In this study, an energy-efficient ALPR system has been designed using the RISC-V-based Kendryte K210 microcontroller. The proposed system adopts a two-stage deep learning-based architecture consisting of YOLOv2 and LPRNet models for license plate detection and recognition. The YOLOv2 model achieves high accuracy in the license plate detection process, while the LPRNet model performs character recognition on the detected license plates. This deep learning-based approach offers an ideal solution for portable and embedded systems with low power consumption. The training of the models leveraged various open-access datasets, aiming to enhance the model’s ability to adapt to different conditions. The findings of the study demonstrate that the developed system achieves high accuracy rates in both license plate detection and recognition processes. The performance observed in the license plate detection stage indicates that the system can reliably operate under different conditions. However, the high inference time observed in the license plate recognition process suggests that the model needs to be optimized for real-time applications. In future work, optimization and quantization techniques in deep learning models are planned to enhance the speed and efficiency of the license plate recognition process. Additionally, the applicability of different text recognition models for the license plate recognition problem in embedded systems will be explored.