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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

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

Performance

Inputs

A blob with a BGR image in the format: [B, C=3, H=512, W=896], where:

  • B – batch size
  • C – number of channels
  • H – image height
  • W – image width

Outputs

The output is a blob with the shape [B, C=4, H=512, W=896]. 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.