Urban Scene Segmentation for Autonomous Vehicles using Multi-Domain Adaptation

Deployment This project is a Web API for Urban Scene Segmentation for Autonomous Car, using two models `Baseline` and `MTKT`, that can handle unsupervised domain adaptation (UDA) problem for multi-target datasets. In FastAPI documentation we propose our two functions that we use to segment labeled images and unlabeled images, and we make a web client that propose the Two functions also.

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