Reference : Refining Weakly-Supervised Free Space Estimation through Data Augmentation and Recurs...
Scientific congresses, symposiums and conference proceedings : Paper published in a book
Engineering, computing & technology : Computer science
Computational Sciences
http://hdl.handle.net/10993/48622
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 mailto [University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SEDAN >]
In press
Proceedings of BNAIC/BeneLearn 2021
Yes
No
International
33rd Benelux Conference on Artificial Intelligence and 30th Belgian-Dutch Conference on Machine Learning
10-11-2021 to 12-11-2021
University of Luxembourg
[en] weak supervision ; Free space ; data augmentation ; recursive training
[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)
Researchers ; Professionals ; Students ; General public
http://hdl.handle.net/10993/48622
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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