Article (Scientific journals)
Striving for Less: Minimally-Supervised Pseudo-Label Generation for Monocular Road Segmentation
Robinet, François; Akl, Yussef; Ullah, Kaleem et al.
2022In IEEE Robotics and Automation Letters, 7 (4), p. 10628 - 10634
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Keywords :
free space estimation; unsupervised learning; semi-supervised learning; Image segmentation; road; Feature extraction
Abstract :
[en] Identifying traversable space is one of the most important problems in autonomous robot navigation and is primarily tackled using learning-based methods. To alleviate the prohibitively high annotation-cost associated with labeling large and diverse datasets, research has recently shifted from traditional supervised methods to focus on unsupervised and semi-supervised approaches. This work focuses on monocular road segmentation and proposes a practical, generic, and minimally-supervised approach based on task-specific feature extraction and pseudo-labeling. Building on recent advances in monocular depth estimation models, we process approximate dense depth maps to estimate pixel-wise road-plane distance maps. These maps are then used in both unsupervised and semi-supervised road segmentation scenarios. In the unsupervised case, we propose a pseudo-labeling pipeline that reaches state-of-the-art Intersection-over-Union (IoU), while reducing complexity and computations compared to existing approaches. We also investigate a semi-supervised extension to our method and find that even minimal labeling efforts can greatly improve results. Our semi-supervised experiments using as little as 1% and 10% of ground truth data, yield models scoring 0.9063 and 0.9332 on the IoU metric respectively. These results correspond to a comparative performance of 95.9% and 98.7% of a fully-supervised model's IoU score, which motivates a pragmatic approach to labeling.
Research center :
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Services and Data management research group (SEDAN)
Disciplines :
Computer science
Author, co-author :
Robinet, François ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SEDAN
Akl, Yussef ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Ullah, Kaleem ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SEDAN
Nozarian, Farzad
Müller, Christian
Frank, Raphaël ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SEDAN
External co-authors :
yes
Language :
English
Title :
Striving for Less: Minimally-Supervised Pseudo-Label Generation for Monocular Road Segmentation
Publication date :
October 2022
Journal title :
IEEE Robotics and Automation Letters
ISSN :
2377-3766
Publisher :
Institute of Electrical and Electronics Engineers, New York, United States - New York
Volume :
7
Issue :
4
Pages :
10628 - 10634
Peer reviewed :
Peer Reviewed verified by ORBi
Focus Area :
Computational Sciences
FnR Project :
FNR13301060 - Machine Learning For Risk Assessment In Semi-autonomous Vehicles, 2018 (01/10/2018-31/08/2022) - François Robinet
Available on ORBilu :
since 04 November 2022

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