README.md

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

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: Placeholder, shape: 1, 300, 300, 3 in the format B, H, W, C, where:

• B - batch size
• H - image height
• W - image width
• C - number of channels

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

Demo usage

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

• Object Detection C++ Demo
• Object Detection Python* Demo
• Security Barrier Camera C++ Demo

Legal Information

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