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vehicle-detection-adas-0002

Use Case and High-Level Description

This is a vehicle detection network based on an SSD framework with tuned MobileNet v1 as a feature extractor.

Example

Specification

Metric

Value

Average Precision (AP)

90.6%

Target vehicle size

40 x 30 pixels on Full HD image

Max objects to detect

200

GFlops

2.798

MParams

1.079

Source framework

Caffe*

For Average Precision metric description, see The PASCAL Visual Object Classes (VOC) Challenge.

Tested on a challenging internal dataset with 3000 images and 12585 vehicles to detect.

Inputs

Image, name: data, shape: 1, 3, 384, 672 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.

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)

  • 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:

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