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.

Performance

Inputs

name: "input" , shape: [1x3x300x300] - An input image in the format [BxCxHxW], where:
- B - batch size
- C - number of channels
- H - image height
- W - image width
Expected color order is BGR.

Outputs

The net outputs a blob with the shape: [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]
- 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.

Legal Information

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