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Timdb/AE4317-cyberzoo-tudelft|无人机数据集|图像识别数据集
收藏hugging_face2024-04-29 更新2024-06-12 收录
下载链接：
https://hf-mirror.com/datasets/Timdb/AE4317-cyberzoo-tudelft
下载链接
链接失效反馈资源简介：
该数据集包含在代尔夫特理工大学Cyberzoo飞机大厅拍摄的图像，其中82%为真实图像，18%为模拟器图像。数据集通过多次测试会话收集，包括实际飞行和手持拍摄。数据标注使用了单目深度图生成工具Depth-Anything，具体标注过程在提供的链接中有详细说明。数据集可用于训练卷积神经网络，用于代尔夫特理工大学Cyberzoo中微型飞行器的避障。数据集结构包括训练集（占数据的90%）和测试集（占数据的10%），每个图像都有嵌入的元数据标签，指示无人机应旋转或飞行的方向。
该数据集包含在代尔夫特理工大学Cyberzoo飞机大厅拍摄的图像，其中82%为真实图像，18%为模拟器图像。数据集通过多次测试会话收集，包括实际飞行和手持拍摄。数据标注使用了单目深度图生成工具Depth-Anything，具体标注过程在提供的链接中有详细说明。数据集可用于训练卷积神经网络，用于代尔夫特理工大学Cyberzoo中微型飞行器的避障。数据集结构包括训练集（占数据的90%）和测试集（占数据的10%），每个图像都有嵌入的元数据标签，指示无人机应旋转或飞行的方向。
提供机构：
Timdb
原始信息汇总
数据集概述
数据集信息
- 大小分类: 10K<n<100K
- 数据集大小: 622057858.415 字节
- 下载大小: 618329781 字节
特征
- image: 图像类型
- left: 整数类型
- forward: 整数类型
- right: 整数类型
分割
- 训练集:
- 示例数量: 12489
- 字节数: 561073580.031
- 测试集:
- 示例数量: 1388
- 字节数: 60984278.384
配置
- 默认配置:
- 训练集路径: data/train-*
- 测试集路径: data/test-*
数据集结构
- 训练集占比: 90%
- 测试集占比: 10%
- 标签: "left", "forward", "right"
AI搜集汇总
数据集介绍
构建方式
该数据集由Delft University of Technology的Cyberzoo环境中采集的图像构成，涵盖了实际飞行和手持拍摄的多种测试场景。数据集包括82%的真实图像和18%的模拟器图像。标签生成采用单目深度图技术，通过[Depth-Anything](https://github.com/LiheYoung/Depth-Anything)工具实现，具体标签过程详见[此笔记本](https://github.com/Timdnb/CNN-for-Micro-Air-Vehicles/blob/main/Dataset_generation.ipynb)。
特点
数据集的显著特点在于其图像来源的多样性，结合了真实环境和模拟器生成的图像，确保了训练模型的广泛适用性。此外，数据集的标签精细且明确，包括'left'、'forward'和'right'三个方向标签，为无人机避障任务提供了精确的指导信息。
使用方法
该数据集主要用于训练卷积神经网络，以实现Delft University of Technology的Cyberzoo环境中微型飞行器的障碍物规避。用户可通过访问[此仓库](https://github.com/Timdnb/CNN-for-Micro-Air-Vehicles)获取完整的训练流程。数据集结构包括90%的训练集和10%的测试集，每张图像的标签嵌入在元数据中，便于直接用于模型训练和评估。
背景与挑战
背景概述
在无人机技术迅速发展的背景下，Delft University of Technology的Cyberzoo实验室致力于微型飞行器（Micro Air Vehicles, MAVs）的障碍规避研究。Timdb/AE4317-cyberzoo-tudelft数据集由Tim den Blanken主导创建，旨在为训练卷积神经网络（CNN）提供高质量的图像数据。该数据集包含了在Cyberzoo内实际飞行和手持拍摄的图像，其中82%为真实图像，18%为模拟器图像。通过使用单目深度图进行数据标注，该数据集为MAVs在复杂环境中的自主导航提供了宝贵的资源。
当前挑战
该数据集在构建过程中面临的主要挑战包括：1) 数据标注的准确性，依赖于单目深度图的生成，这要求高精度的图像处理技术；2) 数据集的平衡性，确保训练集和测试集的分布合理，以避免模型过拟合或欠拟合。此外，数据集的应用场景——MAVs的障碍规避，本身就是一个复杂且动态变化的领域问题，要求模型具备高度的实时性和鲁棒性。
常用场景
经典使用场景
在航空工程领域，Timdb/AE4317-cyberzoo-tudelft数据集被广泛应用于训练卷积神经网络（CNN），以实现微型飞行器（MAV）在Delft理工大学Cyberzoo中的障碍物规避。该数据集通过结合实际飞行和手持拍摄的图像，为模型提供了丰富的视觉输入，从而提升了其在复杂环境中的导航能力。
衍生相关工作
基于Timdb/AE4317-cyberzoo-tudelft数据集，研究人员开发了多种先进的深度学习模型，用于微型飞行器的自主导航和障碍物规避。例如，一些研究团队利用该数据集训练了多模态融合模型，结合视觉和惯性传感器数据，进一步提升了飞行器的导航精度。此外，该数据集还激发了其他相关领域的研究，如图像处理和计算机视觉，推动了整个航空工程领域的技术进步。
数据集最近研究
最新研究方向
在无人机自主导航与避障领域，Timdb/AE4317-cyberzoo-tudelft数据集因其独特的图像数据和精细的标注而备受关注。该数据集不仅包含了实际飞行中的图像，还融入了模拟器生成的图像，为研究者提供了丰富的数据资源。当前，该数据集的前沿研究主要集中在利用卷积神经网络（CNN）进行微型飞行器（MAV）的障碍物规避。通过结合深度学习技术，研究者们致力于提升无人机在复杂环境中的自主导航能力，这对于无人机在实际应用中的安全性和可靠性具有重要意义。此外，该数据集的标注过程采用了单目深度图生成技术，进一步提升了数据的质量和应用价值。
以上内容由AI搜集并总结生成
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