Paper published in a book (Scientific congresses, symposiums and conference proceedings)
Refining Weakly-Supervised Free Space Estimation through Data Augmentation and Recursive Training
Robinet, François; Frank, Raphaël
2021In Proceedings of BNAIC/BeneLearn 2021
Peer reviewed
 

Files


Full Text
bnaic2022-camready.pdf
Author preprint (3.63 MB)
Download

All documents in ORBilu are protected by a user license.

Send to



Details



Keywords :
weak supervision; Free space; data augmentation; recursive training
Abstract :
[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.
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
Frank, Raphaël ;  University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT) > SEDAN
External co-authors :
no
Language :
English
Title :
Refining Weakly-Supervised Free Space Estimation through Data Augmentation and Recursive Training
Publication date :
12 November 2021
Event name :
33rd Benelux Conference on Artificial Intelligence and 30th Belgian-Dutch Conference on Machine Learning
Event organizer :
University of Luxembourg
Event date :
10-11-2021 to 12-11-2021
Audience :
International
Main work title :
Proceedings of BNAIC/BeneLearn 2021
Peer reviewed :
Peer reviewed
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 17 November 2021

Statistics


Number of views
212 (43 by Unilu)
Number of downloads
55 (9 by Unilu)

Scopus citations®
 
1
Scopus citations®
without self-citations
0

Bibliography


Similar publications



Contact ORBilu