CARLA 模拟器生成的可见光图像及其语义分割组成的城市交通数据集

Data Pub Date : 2023-12-24 DOI:10.3390/data9010004
Sergio Bemposta Rosende, David San José Gavilán, Javier Fernández-Andrés, Javier Sánchez-Soriano
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引用次数: 0

摘要

本文介绍了一个城市空中交通图像及其语义分割数据集,用于训练计算机视觉算法,其中以基于卷积神经网络的算法最为突出。本文介绍了创建完整数据集的过程,包括图像的获取、车辆、行人和人行横道的标注以及数据集结构和内容的描述(共有 8694 张图像,包括可见图像和与语义分割相对应的图像)。这些图像是在智能交通管理领域使用 CARLA 模拟器生成的(但与使用固定航空摄像机或多旋翼无人机获得的图像类似)。所提供的数据集可用于提高视觉和道路交通管理系统的性能,尤其是在检测错误或危险操作方面。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Urban Traffic Dataset Composed of Visible Images and Their Semantic Segmentation Generated by the CARLA Simulator
A dataset of aerial urban traffic images and their semantic segmentation is presented to be used to train computer vision algorithms, among which those based on convolutional neural networks stand out. This article explains the process of creating the complete dataset, which includes the acquisition of the images, the labeling of vehicles, pedestrians, and pedestrian crossings as well as a description of the structure and content of the dataset (which amounts to 8694 images including visible images and those corresponding to the semantic segmentation). The images were generated using the CARLA simulator (but were like those that could be obtained with fixed aerial cameras or by using multi-copter drones) in the field of intelligent transportation management. The presented dataset is available and accessible to improve the performance of vision and road traffic management systems, especially for the detection of incorrect or dangerous maneuvers.
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