Embry-Riddle Coastline Dataset (ER-COAST)

The Embry-Riddle Coastline Dataset (ER-Coast) is designed for maritime machine learning research, including sensor fusion. ER-Coast introduces a diverse set of data using multiple sensing modalities. These include three LiDAR, three 8-megapixel color cameras, one 5.4-megapixel high dynamic range camera, two long-wave infrared cameras, and a GPS/IMU inertial navigation system. The captured data includes coastal waterways in the state of Florida during the day and at night. There are 36 separate sequences split across 4 days of collections, totaling over 5 hours of calibrated and timestamped data. A subset of this raw data has been annotated for the tasks of LiDAR semantic segmentation, image semantic segmentation, and image object detection. Intrinsic and extrinsic calibrations are available in the dissertation of https://commons.erau.edu/edt/741/, and the original calibration data has also been provided for users to generate their own calibrations. A paper with a focused discussion of ER-COAST will be released at a later date.

Embry-Riddle海岸线数据集（ER-Coast）专为海上机器学习研究（含传感器融合方向）设计。该数据集采用多种传感模态采集多样化数据，涵盖三台激光雷达（LiDAR）、三台800万像素彩色相机、一台540万像素高动态范围相机、两台长波红外相机，以及一套GPS/IMU惯性导航系统。所采集的数据覆盖佛罗里达州境内的沿海水域，涵盖昼夜不同时段。数据集包含4天采集任务中的36个独立序列，总时长超过5小时，所有数据均经过校准并带有时间戳。该数据集的原始数据子集已针对激光雷达语义分割、图像语义分割以及图像目标检测三类任务完成标注。内参与外参校准信息可在学位论文https://commons.erau.edu/edt/741/中获取，同时也向用户提供了原始校准数据，以供其自行生成所需的校准参数。后续将发布一篇专门针对ER-Coast数据集进行深入探讨的学术论文。

搜集汇总

背景与挑战

背景概述

Embry-Riddle Coastline Dataset (ER-COAST)是一个用于海事机器学习研究的多模态数据集，包含LiDAR、彩色相机、高动态范围相机、长波红外相机和GPS/IMU导航系统等多种传感器数据，覆盖佛罗里达州沿海水域的白天和夜间场景。数据集包含36个序列，总计超过5小时的校准和时间戳数据，部分数据已标注用于LiDAR语义分割、图像语义分割和图像目标检测任务，由美国海军研究办公室等部门资助。