Urban Scene Segmentation for Autonomous Vehicles using Multi-Domain Adaptation

Predict Images with Coarse Segmentation in Cityscapes The RGB image in fig 17-a-1 is one of the 20000 images from the Cityscapes dataset. The ground truth of the RGB image is coarsely segmented, so as our model predicted the fine annotated segmentation that appears in figure 17-a-2. We propose that to use it as ground truth instead of coarse segmentation GT. Fig17-a-1

Coarse Segmentation

Fig17-a-2

Our Segmentation

MTKT Outperforms the Baseline clearly in some examples:

Fig17-b-1: Comparison between Baseline and MTKT on Cityscapes test dataset.

Fig17-b-2: Comparison between Baseline and MTKT on Mapillary test dataset.

Urban Scene Segmentation for Autonomous Vehicles Using Multi-Domain Adaptation

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Urban Scene Segmentation for Autonomous Vehicles using Multi-Domain Adaptation

Published on Feb 4, 2022

mohamed elmesawy

The project tackles the problem of multi-target domains/contexts for the self-driving car in urban cities, using the unsupervised domain adaptation (UDA) methodology. and compares two approaches: 1- `Baseline Model`: which deals with the problem of the multi targets domain as if it is a single target. 2- `MTKT Model`, which consists of multiple teachers [one for each target domain] that distill their knowledge to a single student which will be used as the main model in the inference time. Our project built on the latest SOTA paper of #Valeo France https://arxiv.org/abs/2108.06962 Also, we have developed a self-driving car agent implementing the Deep #Reinforcement algorithm #DQN inside #Carla Simulator.

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Urban Scene Segmentation for Autonomous Vehicles using Multi-Domain Adaptation by mohamed elmesawy - Issuu

Urban Scene Segmentation for Autonomous Vehicles using Multi-Domain Adaptation

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References

Future works

Problem Statement

Datasets

Literature and old Trials

Results

Methodology

Introduction

Deployment

Deep Reinforcement Learning – DQN in Carla