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)	98.62
AP vehicles	98.03
AP plates	99.21
Car pose	Front facing cars
Min plate width	96 pixels
Max objects to detect	200
GFlops	0.4
MParams	0.6
Source framework	TensorFlow

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

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 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

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