LPR Models

Overview

degirum-vehicle uses two types of AI models for license plate recognition: license plate detection models and license plate OCR models. Both are optimized and compiled for a wide range of hardware platforms through the DeGirum model registry.

Important: The degirum_vehicle library provides code and pipelines for license plate recognition workflows. Model licensing is separate from the degirum_vehicle library licensing. Contact DeGirum for licensing information.

License Plate Detection Models

Purpose

License plate detection models locate license plates in images and provide bounding boxes for each detected plate.

License

License plate detection models are trained by DeGirum and can be used commercially when users license the degirum-vehicle package. Contact DeGirum for licensing information.

Model Details

• Architecture: YOLOv8 nano (yolov8n_relu6_global_lp_det)

• Training: All license plate detection models are trained by DeGirum

• Input: 640×640 images

• Output: Bounding boxes + confidence scores per detected license plate

• Compilation: Models are compiled and optimized for all supported hardware platforms

Usage

Detection models are automatically selected from the model registry based on your hardware configuration:

detector_spec = degirum_vehicle.get_license_plate_detection_model_spec(
    device_type="HAILORT/HAILO8",
    inference_host_address="@cloud"
)

License Plate OCR Models

Purpose

License plate OCR models extract text from detected license plate regions, converting the plate image into recognized characters.

License

License plate OCR models are trained by DeGirum and can be used commercially when users license the degirum-vehicle package. Contact DeGirum for licensing information.

Model Details

• Architecture: YOLOv8 small (yolov8s_relu6_lp_ocr_7ch)

• Training: All license plate OCR models are trained by DeGirum

• Input: 256×128 cropped license plate images

• Output: Text string with character-level confidence scores

• Character Set: 7-character format (alphanumeric)

• Compilation: Models are compiled and optimized for all supported hardware platforms

OCR Process

The OCR models use object detection techniques adapted for character recognition:

• Each character is detected as a separate object

• Characters are assembled into the final plate text

• Character-level confidence scores are aggregated into overall OCR score

Usage

OCR models are automatically selected from the model registry:

ocr_spec = degirum_vehicle.get_license_plate_ocr_model_spec(
    device_type="HAILORT/HAILO8",
    inference_host_address="@cloud"
)

Model Registry

The DeGirum model registry automatically selects the optimal model for your hardware platform. This eliminates the need to manually choose models or tune parameters.

Benefits:

• Pre-optimized models for each hardware platform

• Automatic model selection based on device type

• Consistent performance across different hardware

• Simplified configuration

Accessing the registry:

# List all supported hardware platforms
supported_hw = degirum_vehicle.model_registry.get_hardware()

# Get available hardware on cloud or local
available_hw = degirum_vehicle.get_system_hw("@cloud")

# Find compatible hardware you can use now
compatible_hw = degirum_vehicle.get_compatible_hw("@cloud")

Supported Hardware Platforms

The following hardware platforms have optimized LPR models:

Platform	Device Type	Detection Model	OCR Model
TFLite CPU	TFLITE/CPU	yolov8n_relu6_global_lp_det (int8)	yolov8s_relu6_lp_ocr_7ch (float32/int8)
Intel OpenVINO	OPENVINO/CPU	yolov8n_relu6_global_lp_det (int8)	yolov8s_relu6_lp_ocr_7ch (int8)
Hailo-8	HAILORT/HAILO8	yolov8n_relu6_global_lp_det (int8)	yolov8s_relu6_lp_ocr_7ch (int8)
Hailo-8L	HAILORT/HAILO8L	yolov8n_relu6_global_lp_det (int8)	yolov8s_relu6_lp_ocr_7ch (int8)
DeepX M1A	DEEPX/M1A	yolov8n_relu6_global_lp_det (int8)	yolov8s_relu6_lp_ocr_7ch (int8)
Google Coral TPU	TFLITE/EDGETPU	yolov8n_relu6_global_lp_det (int8)	yolov8s_relu6_lp_ocr_7ch (int8)
DeGirum Orca	N2X/ORCA1	yolov8n_relu6_global_lp_det (int8)	yolov8s_relu6_lp_ocr_7ch (int8)

Custom Models

While the model registry provides optimized models for all supported platforms, you can also use custom models by creating ModelSpec objects directly. This is useful for:

• Using proprietary license plate detection models

• Testing alternative OCR architectures

• Integrating region-specific models

See ModelSpec Documentation for details on using custom models.

Model Performance

Performance varies by hardware platform and model complexity. Key factors:

License Plate Detection:

• Input resolution: 640×640 pixels

• Typical throughput: 10-100+ FPS depending on hardware

• Detection accuracy optimized for various lighting conditions and angles

License Plate OCR:

• Input size: 256×128 pixels per plate

• Extraction time: 1-50ms per plate depending on hardware

• Supports various plate formats and fonts

For production deployments, test with your specific hardware and video resolution to determine optimal settings.

Pipeline Integration

Both models work together in the LPR pipeline:

1. Detection Stage: YOLOv8n detects plate regions in the full frame

2. Cropping: Detected regions are cropped and resized to 256×128

3. OCR Stage: YOLOv8s extracts text from cropped plate images

4. Aggregation: (Tracker only) Bayesian fusion combines results across frames for improved accuracy

This two-stage approach ensures optimal accuracy and performance.