Arrêt de service programmé du vendredi 10 juin 16h jusqu’au lundi 13 juin 9h. Pour en savoir plus
Accéder directement au contenu Accéder directement à la navigation
Article dans une revue

A comparative study of semantic segmentation of omnidirectional images from a motorcycle perspective

Abstract : The semantic segmentation of omnidirectional urban driving images is a research topic that has increasingly attracted the attention of researchers, because the use of such images in driving scenes is highly relevant. However, the case of motorized two-wheelers has not been treated yet. Since the dynamics of these vehicles are very different from those of cars, we focus our study on images acquired using a motorcycle. This paper provides a thorough comparative study to show how different deep learning approaches handle omnidirectional images with different representations, including perspective, equirectangular, spherical, and fisheye, and presents the best solution to segment road scene omnidirectional images. We use in this study real perspective images, and synthetic perspective, fisheye and equirectangular images, simulated fisheye images, as well as a test set of real fisheye images. By analyzing both qualitative and quantitative results, the conclusions of this study are multiple, as it helps understand how the networks learn to deal with omnidirectional distortions. Our main findings are that models with planar convolutions give better results than the ones with spherical convolutions, and that models trained on omnidirectional representations transfer better to standard perspective images than vice versa.
Type de document :
Article dans une revue
Liste complète des métadonnées

https://hal-u-picardie.archives-ouvertes.fr/hal-03654210
Contributeur : Louise Dessaivre Connectez-vous pour contacter le contributeur
Soumis le : jeudi 28 avril 2022 - 14:32:33
Dernière modification le : samedi 30 avril 2022 - 03:18:00

Identifiants

Citation

Ahmed Rida Sekkat, Yohan Dupuis, Paul Honeine, Pascal Vasseur. A comparative study of semantic segmentation of omnidirectional images from a motorcycle perspective. Scientific Reports, 2022, 12 (1), ⟨10.1038/s41598-022-08466-9⟩. ⟨hal-03654210⟩

Partager

Métriques

Consultations de la notice

1