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Instance Segmentation with Unsupervised Adaptation to Different Domains for Autonomous Vehicles

Manuel Alejandro Diaz-Zapata 1 Özgür Erkent 1 Christian Laugier 1 
1 CHROMA - Robots coopératifs et adaptés à la présence humaine en environnements dynamiques
Inria Grenoble - Rhône-Alpes, CITI - CITI Centre of Innovation in Telecommunications and Integration of services
Abstract : Detection of the objects around a vehicle is important for a safe and successful navigation of an autonomous vehicle. Instance segmentation provides a fine and accurate classification of the objects such as cars, trucks, pedestrians, etc. In this study, we propose a fast and accurate approach which can detect and segment the object instances which can be adapted to new conditions without requiring the labels from the new condition. Furthermore, the performance of the instance segmentation does not degrade in detection of the objects in the original condition after it adapts to the new condition. To our knowledge, currently there are not other methods which perform unsupervised domain adaptation for the task of instance segmentation using non-synthetic datasets. We evaluate the adaptation capability of our method on two datasets. Firstly, we test its capacity of adapting to a new domain; secondly, we test its ability to adapt to new weather conditions. The results show that it can adapt to new conditions with an improved accuracy while preserving the accuracy of the original condition.
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Submitted on : Thursday, July 15, 2021 - 2:31:29 PM
Last modification on : Monday, May 16, 2022 - 4:46:03 PM


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  • HAL Id : hal-03041432, version 3



Manuel Alejandro Diaz-Zapata, Özgür Erkent, Christian Laugier. Instance Segmentation with Unsupervised Adaptation to Different Domains for Autonomous Vehicles. ICARCV 2020 - 16th International Conference on Control, Automation, Robotics and Vision, Dec 2020, Shenzen, China. pp.1-7. ⟨hal-03041432v3⟩



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