The use of Deep Learning (DL) algorithms has improved the performance of vision-based space applications in recent years. However, generating large amounts of annotated data for training these DL algorithms has proven challenging. While synthetically generated images can be used, the DL models trained on synthetic data are often susceptible to performance degradation, when tested in real-world environments. In this context, the Interdisciplinary Center of Security, Reliability and Trust (SnT) at the University of Luxembourg has developed the 'SnT Zero-G Lab', for training and validating vision-based space algorithms in conditions emulating real-world space environments. An important aspect of the SnT Zero-G Lab development was the equipment selection. From the lessons learned during the lab development, this article presents a systematic approach combining market survey and experimental analyses for equipment selection. In particular, the article focus on the image acquisition equipment in a space lab: background materials, cameras and illumination lamps. The results from the experiment analyses show that the market survey complimented by experimental analyses is required for effective equipment selection in a space lab development project.
翻译:近年来,深层学习算法的使用改善了基于愿景的空间应用的绩效,然而,为培训这些DL算法生成了大量附加说明的数据,证明具有挑战性;虽然合成生成的图像可以使用,但经过合成数据培训的DL模型在现实环境中进行测试时往往容易出现性能退化;在这方面,卢森堡大学安全、可靠性和信任跨学科中心(SnT)开发了“SnT Zero-G实验室”,用于在模拟真实世界空间环境的条件下培训和验证基于愿景的空间算法。SnT Zero-G实验室开发的一个重要方面是设备选择。从实验室开发期间吸取的经验教训来看,本文介绍了一种系统化的方法,将市场调查与设备选择实验分析结合起来。特别是,文章侧重于空间实验室的图像获取设备:背景材料、照相机和照明灯。实验分析的结果显示,空间实验室开发项目的有效设备选择需要通过实验分析进行市场调查。