Paper Info
Title
Semantic Segmentation With Unsupervised Domain Adaptation Under Varying Weather Conditions For Autonomous Vehicles
Abstract
Semantic information provides a valuable source for scene understanding around autonomous vehicles in order to plan their actions and make decisions. However, varying weather conditions reduce the accuracy of the semantic segmentation. We propose a method to adapt to varying weather conditions without supervision, namely without labeled data. We update the parameters of a deep neural network (DNN) model that is pre-trained on the known weather condition (source domain) to adapt it to the new weather conditions (target domain) without forgetting the segmentation in the known weather condition. Furthermore, we don't require the labels from the source domain during adaptation training. The parameters of the DNN are optimized to reduce the distance between the distribution of the features from the images of old and new weather conditions. To measure this distance, we propose three alternatives: W-GAN, GAN and maximum-mean discrepancy (MMD). We evaluate our method on various datasets with varying weather conditions. The results show that the accuracy of the semantic segmentation is improved for varying conditions after adaptation with the proposed method.
Year
DOI
Venue
2020
10.1109/LRA.2020.2978666
IEEE ROBOTICS AND AUTOMATION LETTERS
Keywords
DocType
Volume
Meteorology, Adaptation models, Image segmentation, Semantics, Gallium nitride, Sensors, Training, Intelligent transportation systems, semantic scene understanding, learning and adaptive systems
Journal
5
Issue
ISSN
Citations 
2
2377-3766
0
PageRank 
References 
Authors
0.34
0
2
Name
Order
Citations
PageRank
Özgür Erkent1113.20
Christian Laugier213314.37