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.



Name: input, shape: [1x3x300x300] - An input image in the format [BxCxHxW], where:

Expected color order is BGR.


The net outputs blob with shape: [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:

Legal Information

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