road-segmentation-adas-0001

Use Case and High-Level Description

This is a segmentation network to classify each pixel into four classes: BG, road, curb, mark.

Example

_images/road-segmentation-adas-0001.png

Specification

Metric

Value

Image size

896x512

GFlops

4.770

MParams

0.184

Source framework

PyTorch*

Accuracy

The quality metrics calculated on 500 images from “Mighty AI” dataset that was converted for four class classification task are:

Label

IOU

ACC

mean

0.844

0.901

BG

0.986

0.994

road

0.954

0.974

curbs

0.727

0.831

marks

0.708

0.806

  • IOU=TP/(TP+FN+FP)

  • ACC=TP/GT

  • TP - number of true positive pixels for given class

  • FN - number of false negative pixels for given class

  • FP - number of false positive pixels for given class

  • GT - number of ground truth pixels for given class

Inputs

A blob with a BGR image and the shape 1, 3, 512, 896 in the format B, C, H, W, where:

  • B – batch size

  • C – number of channels

  • H – image height

  • W – image width

Outputs

The output is a blob with the shape 1, 4, 512, 896 in the format B, C, H, W. It can be treated as a four-channel feature map, where each channel is a probability of one of the classes: BG, road, curb, mark.

Use Case and High-Level Description

This is a segmentation network to classify each pixel into four classes: BG, road, curb, mark.

Example

_images/road-segmentation-adas-0001.png

Specification

Metric

Value

Image size

896x512

GFlops

4.770

MParams

0.184

Source framework

PyTorch*

Accuracy

The quality metrics calculated on 500 images from “Mighty AI” dataset that was converted for four class classification task are:

Label

IOU

ACC

mean

0.844

0.901

BG

0.986

0.994

road

0.954

0.974

curbs

0.727

0.831

marks

0.708

0.806

  • IOU=TP/(TP+FN+FP)

  • ACC=TP/GT

  • TP - number of true positive pixels for given class

  • FN - number of false negative pixels for given class

  • FP - number of false positive pixels for given class

  • GT - number of ground truth pixels for given class

Inputs

A blob with a BGR image and the shape 1, 3, 512, 896 in the format B, C, H, W, where:

  • B – batch size

  • C – number of channels

  • H – image height

  • W – image width

Outputs

The output is a blob with the shape 1, 4, 512, 896 in the format B, C, H, W. It can be treated as a four-channel feature map, where each channel is a probability of one of the classes: BG, road, curb, mark.

Legal Information

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