vehicle-license-plate-detection-barrier-0106

Use Case and High-level Description

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

Example

./assets/vehicle-license-plate-detection-barrier-0106.jpeg

Specification

Metric

Value

Mean Average Precision (mAP)

99.65%

AP vehicles

99.88%

AP plates

99.42%

Car pose

Front facing cars

Min plate width

96 pixels

Max objects to detect

200

GFlops

0.349

MParams

0.634

Source framework

TensorFlow*

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

Inputs

Image, name: input, shape: 1, 3, 300, 300 in the format B, C, H, W, where:

  • B - batch size

  • C - number of channels

  • H - image height

  • W - image width

Expected color order is BGR.

Outputs

The net outputs blob with shape: 1, 1, 200, 7 in the format 1, 1, N, 7, where N is the number of detected bounding boxes. Each detection 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 (1 - vehicle, 2 - license plate)

  • 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

Use Case and High-level Description

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

Example

./assets/vehicle-license-plate-detection-barrier-0106.jpeg

Specification

Metric

Value

Mean Average Precision (mAP)

99.65%

AP vehicles

99.88%

AP plates

99.42%

Car pose

Front facing cars

Min plate width

96 pixels

Max objects to detect

200

GFlops

0.349

MParams

0.634

Source framework

TensorFlow*

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

Inputs

Image, name: input, shape: 1, 3, 300, 300 in the format B, C, H, W, where:

  • B - batch size

  • C - number of channels

  • H - image height

  • W - image width

Expected color order is BGR.

Outputs

The net outputs blob with shape: 1, 1, 200, 7 in the format 1, 1, N, 7, where N is the number of detected bounding boxes. Each detection 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 (1 - vehicle, 2 - license plate)

  • 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

Legal Information

[*] Other names and brands may be claimed as the property of others.