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Ballast Water Tank Dataset|船舶检查数据集|无人机监测数据集
收藏github2024-03-26 更新2024-05-31 收录
下载链接：
https://github.com/ntnu-arl/ballast_water_tank_dataset
下载链接
链接失效反馈资源简介：
该数据集是从三艘船的压载水舱中收集的，使用RMF-Owl无人机和手持传感器Mjolnir进行数据采集。数据集包括自主探索和检查任务的飞行数据，以及额外的手动飞行数据。
创建时间：
2024-01-25
原始信息汇总
数据集概述
数据集名称
Ballast Water Tank Dataset
数据集来源
数据集来源于三艘船的压载水舱，分别标记为“FPSO1”, “FPSO2”, 和 “Oil Tanker” (OT)。
数据收集平台
- RMF-Owl: 一种碰撞容忍的空中机器人。
- Mjolnir: 手持传感器设置。
传感器配置
| 传感器 | RMF-Owl | Mjolnir (Sensor Stick) |
|---|---|---|
| LiDAR | Ouster OS0-64 (Rev-D) | Ouster OS0-64 (Rev-D) |
| Camera | Flir Blackfly S 0.4MP Color | Flir Blackfly S 0.4MP Mono (x2) |
| IMU | Vectornav VN100 | Vectornav VN100 |
| Radar | - | Texas Instruments IWR6843AOP-EVM Radar |
任务概览
RMF-Owl
| No. | Ship | Section | Autonomous | Multi-level | Duration (s) |
|---|---|---|---|---|---|
| 1 | FPSO1 | side | Yes | No | 225 |
| 2 | FPSO1 | side | Yes | No | 300 |
| 3 | FPSO1 | side | Yes | No | 154 |
| 4 | FPSO1 | side | No | Yes | 450 |
| 5 | FPSO2 | side | Yes | No | 300 |
| 6 | FPSO2 | side | Yes | No | 380 |
| 7 | FPSO2 | bilge | Yes | No | 336 |
| 8 | FPSO2 | double bottom | Yes | No | 200 |
| 9 | FPSO2 | side | No | Yes | 258 |
| 10 | FPSO2 | bilge, double bottom | No | No | 275 |
| 11 | OT | side | Yes | No | 214 |
| 12 | OT | side | Yes | No | 216 |
| 13 | OT | side | Yes | No | 354 |
| 14 | OT | side | Yes | No | 360 |
| 15 | OT | side | Yes | No | 370 |
Mjolnir
| No. | Ship | Section | Autonomous | Multi-level | Duration (s) |
|---|---|---|---|---|---|
| 1 | FPSO1 | side | N/A | No | 268 |
| 2 | FPSO1 | side | N/A | No | 373 |
| 3 | FPSO1 | side | N/A | No | 357 |
| 4 | FPSO1 | side | N/A | No | 395 |
数据隐私
为保护隐私，图像中的人脸已被模糊处理。
AI搜集汇总
数据集介绍
构建方式
该数据集源自三艘船舶的压载水舱，分别为FPSO1、FPSO2和油轮（OT）。数据采集采用了两种平台：RMF-Owl，一种抗碰撞的空中机器人，以及手持传感器装置Mjolnir。RMF-Owl的数据包括自主探索和检查任务的飞行记录，以及额外的手动飞行数据。Mjolnir则通过手持方式进行数据采集。传感器配置包括LiDAR、相机、IMU和雷达，确保了数据的多样性和全面性。
特点
该数据集的显著特点在于其多源传感器数据的融合，涵盖了LiDAR、相机、IMU和雷达等多种传感器类型。此外，数据集包含了自主飞行和手动采集两种模式，提供了丰富的数据多样性。数据隐私方面，所有包含人脸的图像均进行了模糊处理，确保了数据的安全性和合规性。
使用方法
使用该数据集时，研究者可以利用RMF-Owl和Mjolnir的数据进行多传感器融合分析，以提升对压载水舱环境的理解和建模。数据集的多样性使得其在机器人导航、环境感知和故障检测等领域具有广泛的应用潜力。研究者需注意，由于数据隐私保护，所有包含人脸的图像已进行模糊处理，使用时应遵守相关隐私保护规定。
背景与挑战
背景概述
在海洋工程与机器人技术的交叉领域，Ballast Water Tank Dataset的创建标志着对船舶压载水舱检测技术的重大进步。该数据集由三个匿名船舶（FPSO1、FPSO2和Oil Tanker）的压载水舱数据组成，通过RMF-Owl无人机和手持传感器Mjolnir收集。RMF-Owl是一种碰撞容忍型空中机器人，其数据包括自主探索和检查任务的飞行记录，以及额外的人工飞行数据。该数据集的创建不仅提升了对压载水舱内部环境的理解，还为相关领域的研究提供了宝贵的资源，特别是在船舶维护和安全检测方面。
当前挑战
Ballast Water Tank Dataset在构建过程中面临多项挑战。首先，数据收集环境复杂，压载水舱内部结构多样且空间有限，对无人机的自主导航和传感器精度提出了高要求。其次，数据隐私问题也是一个重要考量，所有图像中的人脸均需进行模糊处理。此外，数据集的多样性和代表性也是一个挑战，确保从不同船舶和不同部分收集的数据能够全面反映压载水舱的实际情况。最后，数据集的标注和处理需要高度专业化的知识，以确保数据的准确性和可用性。
常用场景
经典使用场景
在海洋工程领域，Ballast Water Tank Dataset 主要用于船舶压载水舱的自主检测与监控。通过搭载RMF-Owl无人机和手持传感器Mjolnir，该数据集记录了多艘船舶压载水舱的详细数据，包括LiDAR、相机和IMU等传感器信息。这些数据为研究船舶结构健康监测、自主导航和环境感知提供了宝贵的资源。
衍生相关工作
基于Ballast Water Tank Dataset，研究者们开展了多项相关工作，包括改进自主导航算法、优化多传感器数据融合技术以及开发新的目标识别模型。这些研究不仅提升了数据集的应用价值，还为海洋工程领域的技术创新提供了新的思路和方法。
数据集最近研究
最新研究方向
在船舶工程领域，Ballast Water Tank Dataset的最新研究方向主要集中在利用先进的机器人技术和传感器系统进行船舶压载水舱的自动化检测与监控。通过结合RMF-Owl无人机和Mjolnir手持传感器的多模态数据，研究人员致力于开发高效的自主导航和环境感知算法，以提升船舶维护和安全性能。此外，该数据集的应用还扩展到海洋环境保护和法规遵从性研究，特别是在压载水处理和排放监测方面，具有重要的实际意义和科学价值。
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