This repository is of a project named real-time indian 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 India.
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 Indian 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.
The project uses data for the indian license plate identification:
License Plate Detection results with 40 FPS on Jetson Nano:
python3 detectnet-camera.py --model=./networks/indian_plate/indian_plate_ssd_v1.onnx --class_labels=./networks/indian_plate/labels.txt --input_blob=input_0 --output_cvg=scores --output_bbox=boxes --camera=csi://0 --width=640 --height=480License Plate Recognition results with 40 FPS on Jetson Nano:
python3 detectnet-camera.py --model=./networks/indian_plate_ocr/indian_plate_ocr_ssd_v1.onnx --class_labels=./networks/indian_plate_ocr/labels.txt --input_blob=input_0 --output_cvg=scores --output_bbox=boxes --camera=csi://0 --width=640 --height=480
The project uses data for the indian license plate identification:
1. License Plate Detection:
| Network | FPS | num_class | Model |
|---|---|---|---|
| SSD-Mobilenet-v1 | 40 | 1 | link |
| YoloV4 | None | 1 | link |
| YoloV4-tiny | None | 1 | link |
| Wpod | 10 | 1 | link |
2. License Plate Recognition:
| Network | FPS | num_class | Model |
|---|---|---|---|
| SSD-Mobilenet-v1 | 40 | 36 | link |
| SVM | None | 36 | link |
[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).