Decoux Benoit, Khemmar Redouane, Ragot Nicolas, Venon Arthur, Grassi-Pampuch Marcos, Mauri Antoine, Lecrosnier Louis, Pradeep Vishnu
Normandie University, Unirouen, Esigelec, Irseem, 76000 Rouen, France.
J Imaging. 2022 Aug 7;8(8):216. doi: 10.3390/jimaging8080216.
In smart mobility, the semantic segmentation of images is an important task for a good understanding of the environment. In recent years, many studies have been made on this subject, in the field of Autonomous Vehicles on roads. Some image datasets are available for learning semantic segmentation models, leading to very good performance. However, for other types of autonomous mobile systems like Electric Wheelchairs (EW) on sidewalks, there is no specific dataset. Our contribution presented in this article is twofold: (1) the proposal of a new dataset of short sequences of exterior images of street scenes taken from viewpoints located on sidewalks, in a 3D virtual environment (CARLA); (2) a convolutional neural network (CNN) adapted for temporal processing and including additional techniques to improve its accuracy. Our dataset includes a smaller subset, made of image pairs taken from the same places in the maps of the virtual environment, but from different viewpoints: one located on the road and the other located on the sidewalk. This additional set is aimed at showing the importance of the viewpoint in the result of semantic segmentation.
在智能移动性领域，图像语义分割是全面了解环境的一项重要任务。近年来，在道路上的自动驾驶车辆领域，针对这一主题已经开展了许多研究。一些图像数据集可用于学习语义分割模型，从而取得了非常好的性能。然而，对于其他类型的自主移动系统，比如人行道上的电动轮椅（EW），却没有特定的数据集。本文提出的贡献有两个方面：（1）在3D虚拟环境（CARLA）中，从人行道上的视角提出一个新的街道场景外部图像短序列数据集；（2）一种适用于时间处理并包含其他提高其准确性技术的卷积神经网络（CNN）。我们的数据集包括一个较小的子集，它由在虚拟环境地图中相同地点但从不同视角拍摄的图像对组成：一个位于道路上，另一个位于人行道上。这个额外的数据集旨在展示视角在语义分割结果中的重要性。
