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This repository provides you with a detailed guide on how to build a real-time license plate detection and recognition system. The source code of the repository implemented on Jetson Nano reached 40 FPS.

The license plate data set for this repository was collected in Vietnam. You can train your model to detect and recognize number plates by following the instructions below.

• Real-time Auto License Plate Recognition with Jetson Nano
  • Table of Contents
  • Pipeline
  • Setting up your Jetson
  • Vienamese Plate Dataset
  • License PLate Detection
  • License Plate Recognition
  • Trained Models
  • Reference

This project is developed based on the pipeline described below. From a set of data collected in practice to the problem you want to solve. For details in this project, we will use the dataset of Vietnamese license plates.

First, you need to prepare a labeled dataset. Then train the object detection model with the GPU on Google Colab or your computer. Depending on the Deeplearning Framework you use, it will output the model file in different formats. With ONNX you can convert most of the above formats to a single .onnx format. Then with TensorRT installed on the Jetpack Jetson Nano, you can run the object detection algorithms with high accuracy and FPS.

To get started with this project you need to install your jetson nano with the libraries and source code as follows:

• Setting up your Jetson

The project shares two sets of data for the license plate identification problem in Vietnam:

• Vienamese Plate Dataset

• License PLate Detection Dataset

• License Plate Recognition Dataset

License PLate Detection results with 40 FPS on Jetson Nano:

License Plate Detection tutorial:

• License Plate Detection

License Plate Recognition results with 40 FPS on Jetson Nano:

License Plate Recognition tutorial:

• License Plate Recognition

1. License PLate Detection:

2. License Plate Recognition:

[1] https://github.com/dusty-nv/jetson-inference
[2] Liu, Wei, et al. "Ssd: Single shot multibox detector." European conference on computer vision. Springer, Cham, 2016.
[3] Howard, Andrew G., et al. "Mobilenets: Efficient convolutional neural networks for mobile vision applications." arXiv preprint arXiv:1704.04861 (2017).
[4] Bochkovskiy, Alexey, Chien-Yao Wang, and Hong-Yuan Mark Liao. "YOLOv4: Optimal Speed and Accuracy of Object Detection." arXiv preprint arXiv:2004.10934 (2020).