Eastepp Maxwell, Faris Lauren, Ricks Kenneth
The University of Alabama, Tuscaloosa, AL 35487, United States.
Data Brief. 2022 Mar 22;42:108073. doi: 10.1016/j.dib.2022.108073. eCollection 2022 Jun.
UA_L-DoTT (University of Alabama's Large Dataset of Trains and Trucks) is a collection of camera images and 3D LiDAR point cloud scans from five different data sites. Four of the data sites targeted trains on railways and the last targeted trucks on a four-lane highway. Low light conditions were present at one of the data sites showcasing unique differences between individual sensor data. The final data site utilized a mobile platform which created a large variety of viewpoints in images and point clouds. The dataset consists of 93,397 raw images, 11,415 corresponding labeled text files, 354,334 raw point clouds, 77,860 corresponding labeled point clouds, and 33 timestamp files. These timestamps correlate images to point cloud scans via POSIX time. The data was collected with a sensor suite consisting of five different LiDAR sensors and a camera. This provides various viewpoints and features of the same targets due to the variance in operational characteristics of the sensors. The inclusion of both raw and labeled data allows users to get started immediately with the labeled subset, or label additional raw data as needed. This large dataset is beneficial to any researcher interested in machine learning using cameras, LiDARs, or both. The current dataset is publicly available at UA_L-DoTT.
阿拉巴马大学火车与卡车大型数据集（UA_L-DoTT）是一个由来自五个不同数据站点的相机图像和三维激光雷达点云扫描数据组成的集合。其中四个数据站点以铁路上的火车为目标，最后一个数据站点以四车道高速公路上的卡车为目标。其中一个数据站点存在低光照条件，这展示了各个传感器数据之间的独特差异。最后一个数据站点使用了移动平台，该平台在图像和点云中创建了各种各样的视角。该数据集包括93397张原始图像、11415个相应的带标签文本文件、354334个原始点云、77860个相应的带标签点云以及33个时间戳文件。这些时间戳通过POSIX时间将图像与点云扫描关联起来。数据是通过由五个不同的激光雷达传感器和一个相机组成的传感器套件收集的。由于传感器操作特性的差异，这提供了同一目标的各种视角和特征。原始数据和带标签数据的包含使用户可以立即从带标签的子集中开始，或者根据需要对额外的原始数据进行标注。这个大型数据集对任何对使用相机、激光雷达或两者进行机器学习感兴趣的研究人员都有益处。当前的数据集可在UA_L-DoTT上公开获取。
