Use Case and High-level Description

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



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.


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.


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

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