Survey on Unsupervised Domain Adaptation for Semantic Segmentation for Visual Perception in Automated Driving

04/24/2023
by   Manuel Schwonberg, et al.
0

Deep neural networks (DNNs) have proven their capabilities in many areas in the past years, such as robotics, or automated driving, enabling technological breakthroughs. DNNs play a significant role in environment perception for the challenging application of automated driving and are employed for tasks such as detection, semantic segmentation, and sensor fusion. Despite this progress and tremendous research efforts, several issues still need to be addressed that limit the applicability of DNNs in automated driving. The bad generalization of DNNs to new, unseen domains is a major problem on the way to a safe, large-scale application, because manual annotation of new domains is costly, particularly for semantic segmentation. For this reason, methods are required to adapt DNNs to new domains without labeling effort. The task, which these methods aim to solve is termed unsupervised domain adaptation (UDA). While several different domain shifts can challenge DNNs, the shift between synthetic and real data is of particular importance for automated driving, as it allows the use of simulation environments for DNN training. In this work, we present an overview of the current state of the art in this field of research. We categorize and explain the different approaches for UDA. The number of considered publications is larger than any other survey on this topic. The scope of this survey goes far beyond the description of the UDA state-of-the-art. Based on our large data and knowledge base, we present a quantitative comparison of the approaches and use the observations to point out the latest trends in this field. In the following, we conduct a critical analysis of the state-of-the-art and highlight promising future research directions. With this survey, we aim to facilitate UDA research further and encourage scientists to exploit novel research directions to generalize DNNs better.

READ FULL TEXT

page 1

page 2

page 3

page 6

page 7

page 11

page 17

page 31

research
12/06/2021

Unsupervised Domain Adaptation for Semantic Image Segmentation: a Comprehensive Survey

Semantic segmentation plays a fundamental role in a broad variety of com...
research
05/21/2020

Unsupervised Domain Adaptation in Semantic Segmentation: a Review

The aim of this paper is to give an overview of the recent advancements ...
research
10/23/2020

Domain Adaptation in LiDAR Semantic Segmentation

LiDAR semantic segmentation provides 3D semantic information about the e...
research
10/06/2021

Shallow Features Guide Unsupervised Domain Adaptation for Semantic Segmentation at Class Boundaries

Although deep neural networks have achieved remarkable results for the t...
research
04/24/2023

Augmentation-based Domain Generalization for Semantic Segmentation

Unsupervised Domain Adaptation (UDA) and domain generalization (DG) are ...
research
02/13/2023

Semantic Image Segmentation: Two Decades of Research

Semantic image segmentation (SiS) plays a fundamental role in a broad va...
research
04/28/2021

Randomized Histogram Matching: A Simple Augmentation for Unsupervised Domain Adaptation in Overhead Imagery

Modern deep neural networks (DNNs) achieve highly accurate results for m...

Please sign up or login with your details

Forgot password? Click here to reset