Semantic Segmentation With Unsupervised Domain Adaptation Under Varying Weather Conditions for Autonomous Vehicles
| Autor: | Christian Laugier, Ozgur Erkent |
|---|---|
| Přispěvatelé: | Robots coopératifs et adaptés à la présence humaine en environnements dynamiques (CHROMA), Inria Grenoble - Rhône-Alpes, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-CITI Centre of Innovation in Telecommunications and Integration of services (CITI), Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon, Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA) |
| Rok vydání: | 2020 |
| Předmět: |
Control and Optimization
Computer science Biomedical Engineering 02 engineering and technology 010501 environmental sciences 01 natural sciences Domain (software engineering) [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG] Artificial Intelligence 0202 electrical engineering electronic engineering information engineering [INFO.INFO-RB]Computer Science [cs]/Robotics [cs.RO] Learning and Adaptive Systems Segmentation Intelligent Transportation Systems ITS Adaptation (computer science) 0105 earth and related environmental sciences Measure (data warehouse) Artificial neural network business.industry Mechanical Engineering [INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] Pattern recognition Image segmentation Computer Science Applications Human-Computer Interaction Control and Systems Engineering 020201 artificial intelligence & image processing Semantic Scene Understanding Computer Vision and Pattern Recognition Artificial intelligence business |
| Zdroj: | IEEE Robotics and Automation Letters IEEE Robotics and Automation Letters, IEEE 2020, pp.1-8. ⟨10.1109/LRA.2020.2978666⟩ IEEE Robotics and Automation Letters, 2020, pp.1-8. ⟨10.1109/LRA.2020.2978666⟩ |
| ISSN: | 2377-3774 2377-3766 |
| DOI: | 10.1109/lra.2020.2978666 |
| Popis: | International audience; 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. |
| Databáze: | OpenAIRE |
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