Results

from Urban Scene Segmentation for Autonomous Vehicles using Multi-Domain Adaptation

Methodology

Predict the Images which have COARSE Segmentation in Cityscapes

The RGB image in fig 17-a-1 is one from the 20000 images from cityscapes dataset. The ground truth of the RGB image is coarsely segmented, so as our model predicted the fine annotated segmentation that's appear in the figure 17-a-2. We propose that to use it as ground truth instead of coarse segmentation GT

Coarse Segmentation

Fig17-a-1

Our Segmentation

Fig17-a-2

MTKT Outperforms the Baseline clearly in some examples such as

Fig17-b-1

Fig17-b-2

Comparison between BASELINE vs MTKT on Cityscapes Validation Datasets

We can notice the difference between two models, the MTKT performs better than baseline

Fig18-a

Our Segmentation of a Video example from Cityscapes

Our Contribution and Accepted Pull-Request on Valeo France Repo

There was a bug in the configuration file in the code of main value branch repo , and we pulled a request to merge on the main branch for Value France.

The Difference between our IoU, and their IoU

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Methodology

Predict the Images which have COARSE Segmentation in Cityscapes

Fig17-a-1

Fig17-a-2

MTKT Outperforms the Baseline clearly in some examples such as

Fig17-b-1

Fig17-b-2

Comparison between BASELINE vs MTKT on Cityscapes Validation Datasets

Fig18-a

Our Segmentation of a Video example from Cityscapes

Our Contribution and Accepted Pull-Request on Valeo France Repo

More articles from this publication:

Methodology

Introduction

Deployment

Deep Reinforcement Learning – DQN in Carla

References

Future works

Problem Statement

Datasets

Literature and old Trials

This article is from:

Urban Scene Segmentation for Autonomous Vehicles using Multi-Domain Adaptation