vehicle-license-plate-detection-barrier-0123

Use Case and High-level Description

This is a MobileNetV2 + SSD-based vehicle and (Chinese) license plate detector for the “Barrier” use case.

Example

_images/vehicle-license-plate-detection-barrier-0123.jpg

Specification

Metric

Value

Mean Average Precision (mAP)

99.52%

AP vehicles

99.90%

AP plates

99.13%

Car pose

Front facing cars

Min plate width

96 pixels

Max objects to detect

200

GFlops

0.271

MParams

0.547

Source framework

TensorFlow*

Average Precision (AP) is defined as an area under the precision/recall curve. Validation dataset is BIT-Vehicle.

Input

Original Model

An input image, name: input, shape: 1, 256, 256, 3, format: B, H, W, C, where:

  • B - batch size

  • H - image height

  • W - image width

  • C - number of channels

Expected color order: RGB. Mean values: [127.5, 127.5, 127.5], scale factor for each channel: 127.5

Converted Model

An input image, name: input, shape: 1, 256, 256, 3, format: B, H, W, C, where:

  • B - batch size

  • H - image height

  • W - image width

  • C - number of channels

Expected color order is BGR.

Output

Original Model

The net outputs a blob with the shape: 1, 1, 200, 7 in the format 1, 1, N, 7, where N is the number of detected bounding boxes. For each detection, the description has the format: [image_id, label, conf, x_min, y_min, x_max, y_max], where:

  • image_id - ID of the image in the batch

  • label - predicted class ID

  • conf - confidence for the predicted class

  • (x_min, y_min) - coordinates of the top left bounding box corner

  • (x_max, y_max) - coordinates of the bottom right bounding box corner.

Converted Model

The net outputs a blob with the shape: 1, 1, 200, 7 in the format 1, 1, N, 7, where N is the number of detected bounding boxes. For each detection, the description has the format: [image_id, label, conf, x_min, y_min, x_max, y_max], where:

  • image_id - ID of the image in the batch

  • label - predicted class ID

  • conf - confidence for the predicted class

  • (x_min, y_min) - coordinates of the top left bounding box corner

  • (x_max, y_max) - coordinates of the bottom right bounding box corner.

Download a Model and Convert it into OpenVINO™ IR Format

You can download models and if necessary convert them into OpenVINO™ IR format using the Model Downloader and other automation tools as shown in the examples below.

An example of using the Model Downloader:

omz_downloader --name <model_name>

An example of using the Model Converter:

omz_converter --name <model_name>

Demo usage

The model can be used in the following demos provided by the Open Model Zoo to show its capabilities: