Reference : Refining Weakly-Supervised Free Space Estimation Through Data Augmentation and Recurs...
Parts of books : Contribution to collective works
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/50812
Refining Weakly-Supervised Free Space Estimation Through Data Augmentation and Recursive Training
English
Robinet, François mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SEDAN]
Frank, Raphaël [> >]
2022
Artificial Intelligence and Machine Learning
Springer International Publishing
30--45
Yes
978-3-030-93842-0
[en] road segmentation ; free space segmentation
[en] Free space estimation is an important problem for autonomous robot navigation. Traditional camera-based approaches rely on pixel-wise ground truth annotations to train a segmentation model. To cover the wide variety of environments and lighting conditions encountered on roads, training supervised models requires large datasets. This makes the annotation cost prohibitively high. In this work, we propose a novel approach for obtaining free space estimates from images taken with a single road-facing camera. We rely on a technique that generates weak free space labels without any supervision, which are then used as ground truth to train a segmentation model for free space estimation. We study the impact of different data augmentation techniques on the performances of free space predictions, and propose to use a recursive training strategy. Our results are benchmarked using the Cityscapes dataset and improve over comparable published work across all evaluation metrics. Our best model reaches 83.64% IoU (+2.3%), 91.75 Precision (+2.4%) and 91.29% Recall (+0.4%). These results correspond to 88.8% of the IoU, 94.3% of the Precision and 93.1% of the Recall obtained by an equivalent fully-supervised baseline, while using no ground truth annotation. Our code and models are freely available online.
Interdisciplinary Centre for Security, Reliability and Trust (SnT) > Services and Data management research group (SEDAN)
Fonds National de la Recherche - FnR
Researchers ; Professionals ; Students
http://hdl.handle.net/10993/50812
10.1007/978-3-030-93842-0_2
FnR ; FNR13301060 > François Robinet > MASSIVE > Machine Learning For Risk Assessment In Semi-autonomous Vehicles > 01/10/2018 > 31/08/2022 > 2018

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