Deep Learning-based License Plate Recognition in IoT Smart Parking Systems using YOLOv6 Algorithm

License plate recognition (LPR) is pivotal for the seamless operation of Internet of things (IoT) and smart parking systems, ensuring the swift and effective identification and management of vehicles. Recent research has concentrated on refining LPR methods through deep learning approaches, proposing diverse strategies to enhance accuracy and reduce computation costs. This work tackles these challenges by introducing an innovative method rooted in the YOLOv6 algorithm. Leveraging a tailored dataset for model generation, the study employs rigorous methodologies involving validation, testing, and training. The resultant model demonstrates marked improvements in license plate recognition capabilities, surpassing the performance of existing methods. This breakthrough bears significant implications for advancing IoT smart parking systems, promising heightened reliability and efficiency in vehicle identification and management. Thorough experimental results and performance evaluations validate the efficacy of the proposed YOLOv6-based method. In-depth discussions and comparisons with state-of-the-art methods in the field lead to the conclusion that the introduced approach not only elevates accuracy but also enhances overall efficiency in license plate recognition for smart parking systems, thereby providing valuable contributions to the domain.

@article{Li2023,
  title     = {Deep Learning-based License Plate Recognition in IoT Smart Parking Systems using YOLOv6 Algorithm},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {12},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Ming Li and Li Zhang},
  doi       = {10.14569/IJACSA.2023.0141223},
  url       = {https://doi.org/10.14569/IJACSA.2023.0141223}
}