填补空白:使用合成低空空中图像来增加运营设计领域覆盖范围
Joachim Rüter1, Theresa Maienschein1, Sebastian Schirmer1
1German Aerospace Center (DLR), Institute of Flight Systems, 38108 Braunschweig, Germany.
Sensors (Basel, Switzerland)
|February 24, 2024
概括
合成数据可以通过扩展其操作设计领域 (ODD) 来增强无人机系统 (UAS) 的机器学习 (ML) 模型. 当真实世界的数据不足以实现安全的自动飞行时,增强特别有效.
科学领域:
- 航空航天工程 航空航天工程
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 无人机系统 (UAS) 的安全和自主飞行依赖于强大的环境感知.
- 机器学习 (ML) 提供了先进的视觉感知,但由于数据依赖性质而面临认证障碍.
- 欧盟航空安全局 (EASA) 提供有关机器学习挑战的指导,例如数据管理和学习保证.
研究的目的:
- 调查合成数据的实用性,以补充基于机器学习的视觉对象检测的真实世界数据集.
- 评估合成数据是否可以增加机器学习系统的操作设计领域 (ODD) 覆盖范围.
- 评估合成数据生成技术,以改善航空中的ML模型性能.
主要方法:
- 使用EASA推的方法生成合成数据:增强,拼接和模拟环境.
- 增强了现实世界的数据集与合成数据来训练更快的R-CNN物体检测模型.
- 专注于在地面上检测人类,以评估着陆地点的安全性.
主要成果:
- 合成数据生成技术在增加ODD覆盖范围的适用性方面有所不同.
- 增强证明特别有前途的增强数据集与有限的现实世界的数据.
- 该研究提供了关于在航空领域生成和利用ML的合成数据的见解.
结论:
- 合成数据可以有效地增强现实世界的数据集,以改善ML感知系统的ODD覆盖范围.
- 这项研究通过解决数据要求,有助于在航空领域采用ML.
- 这些发现支持为UAS应用开发可认证的基于ML的系统.
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