Instance segmentation in fisheye images

Autor: Cyril Meurie, Clement Strauss, Olivier Lezoray, Remi Dufour
Přispěvatelé: Institut de Recherche Technologique Railenium, Laboratoire Électronique Ondes et Signaux pour les Transports (COSYS-LEOST ), Université de Lille-Université Gustave Eiffel, Equipe Image - Laboratoire GREYC - UMR6072, Groupe de Recherche en Informatique, Image et Instrumentation de Caen (GREYC), Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Normandie Université (NU)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)-Centre National de la Recherche Scientifique (CNRS), Projet Train Autonome, Centre National de la Recherche Scientifique (CNRS)-École Nationale Supérieure d'Ingénieurs de Caen (ENSICAEN), Normandie Université (NU)-Normandie Université (NU)-Université de Caen Normandie (UNICAEN), Normandie Université (NU)
Jazyk: angličtina
Rok vydání: 2020
Předmět:
Zdroj: IPTA 2020, International Conference on Image Processing Theory, Tools and Applications
IPTA 2020, International Conference on Image Processing Theory, Tools and Applications, Nov 2020, Paris, France. 6p
HAL
IPTA
Popis: IPTA 2020, International Conference on Image Processing Theory, Tools and Applications, Paris, FRANCE, 09-/11/2020 - 12/11/2020; In this paper we propose a data augmentation method for instance segmentation in fisheye images. A lot of progress has been made on the task of instance segmentation in the last few years, particularly for rectilinear images. In fisheye images, detection tasks have mostly been explored as a semantic segmentation task. Instance segmentation in fisheye images is challenging and has not yet been fully explored. There is also much interest in the development of deep neural networks that can handle both rectilinear and fisheye images. Indeed, this can be interesting to have control on computing resource requirements, of paramount importance for real-time systems e.g., in transportation systems. This paper aims to explore these two challenges using Mask R-CNN trained with a data augmentation method designed to provide good performance on both rectilinear and fisheye images. We show that performance on fisheye augmented images can be increased by 9% while only decreasing performance on rectilinear images by 2%, and that performance on wide angle fisheye cameras can be increased by 18.4% compared to the reference, which provides more benefits than a simple vertical flip augmentation.; In this paper we propose a data augmentation method for instance segmentation in fisheye images. A lot of progress has been made on the task of instance segmentation in the last few years, particularly for rectilinear images. In fisheye images, detection tasks have mostly been explored as a semantic segmentation task. Instance segmentation in fisheye images is challenging and has not yet been fully explored. There is also much interest in the development of deep neural networks that can handle both rectilinear and fisheye images. Indeed, this can be interesting to have control on computing resource requirements, of paramount importance for real-time systems e.g., in transportation systems. This paper aims to explore these two challenges using Mask R-CNN trained with a data augmentation method designed to provide good performance on both rectilinear and fisheye images. We show that performance on fisheye augmented images can be increased by 9% while only decreasing performance on rectilinear images by 2%, and that performance on wide angle fisheye cameras can be increased by 18.4% compared to the reference, which provides more benefits than a simple vertical flip augmentation.
Databáze: OpenAIRE
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