vehicle-detection-adas-binary-0001

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 and using binary layer for speedup. This detecector was created by binarization the vehicle-detection-adas-0002

Example

vehicle-detection-adas-binary-0001.png

Specification

Metric Value
Average Precision (AP) 89.2%
Target vehicle size 40 x 30 pixels on Full HD image
Max objects to detect 200
GFlops 0.75
GI1ops 2.048
MParams 1.079
Source framework PyTorch*

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.

Performance

Inputs

Name: input, shape: [1x3x384x672] - An input image in the format [BxCxHxW], where:

Outputs

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.

The binary network was tuned from vehicle-detection-adas-0002 model