A Single-Stage Deep Learning-based Approach for Real-Time License Plate Recognition in Smart Parking System

License plate recognition in smart parking systems plays a crucial role in enhancing parking management efficiency and security. Traditional methods and deep learning-based approaches have been explored for license plate recognition. Deep learning methods have gained prominence due to their ability to extract meaningful features and achieve high accuracy rates. However, existing deep learning-based fire detection methods face challenges in terms of accuracy, real-time requirement, and computation cost, as evident from previous studies. To address these challenges, we propose a single-stage deep learning approach using YOLO (You Only Look Once) algorithm. Our method involves generating a custom dataset and conducting training, validation, and testing processes to train the YOLO-based model. Experimental results and performance evaluations demonstrate that our proposed method achieves high accuracy rates and satisfies real-time requirements, validating its effectiveness for license plate recognition in smart parking systems.

@article{YU2023,
  title     = {A Single-Stage Deep Learning-based Approach for Real-Time License Plate Recognition in Smart Parking System},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {14},
  number    = {9},
  year      = {2023},
  publisher = {The Science and Information Organization},
  author    = {Lina YU and Shaokun LIU},
  doi       = {10.14569/IJACSA.2023.01409119},
  url       = {https://doi.org/10.14569/IJACSA.2023.01409119}
}