YOLO-ET: A Machine Learning model for detecting, localising and classifying anthropogenic contaminants and extraterrestrial microparticles optimised for mobile processing systems

IF 1.9 4区 物理与天体物理 Q2 ASTRONOMY & ASTROPHYSICS
L.J. Pinault , H. Yano , K. Okudaira , I.A. Crawford
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Abstract

Imminent robotic and human activities on the Moon and other planetary bodies would benefit from advanced in situ Computer Vision and Machine Learning capabilities to identify and quantify microparticle terrestrial contaminants, lunar regolith disturbances, the flux of interplanetary dust particles, possible interstellar dust, β-meteoroids, and secondary impact ejecta. The YOLO-ET (ExtraTerrestrial) algorithm, an innovation in this field, fine-tunes Tiny-YOLO to specifically address these challenges. Designed for coreML model transference to mobile devices, the algorithm facilitates edge computing in space environment conditions. YOLO-ET is deployable as an app on an iPhone with LabCam® optical enhancement, ready for space application ruggedisation. Training on images from the Tanpopo aerogel panels returned from Japan’s Kibo module of the International Space Station, YOLO-ET demonstrates a 90% detection rate for surface contaminant microparticles on the aerogels, and shows promising early results for detection of both microparticle contaminants on the Moon and for evaluating asteroid return samples. YOLO-ET’s application to identifying spacecraft-derived microparticles in lunar regolith simulant samples and SEM images of asteroid Ryugu samples returned by Hayabusa2 and curated by JAXA’s Institute of Space and Astronautical Sciences indicate strong model performance and transfer learning capabilities for future extraterrestrial applications.

YOLO-ET:用于检测、定位和分类人为污染物和地外微颗粒的机器学习模型,针对移动处理系统进行了优化
先进的现场计算机视觉和机器学习能力可识别和量化微粒子地面污染物、月球碎屑扰动、行星际尘埃粒子通量、可能的星际尘埃、β流星体和二次撞击喷出物,这将使即将在月球和其他行星体上开展的机器人和人类活动受益匪浅。YOLO-ET(地外)算法是该领域的一项创新,它对 Tiny-YOLO 进行了微调,以专门应对这些挑战。该算法专为将 coreML 模型传输到移动设备而设计,有助于在太空环境条件下进行边缘计算。YOLO-ET 可作为一个应用程序部署在带有 LabCam® 光学增强功能的 iPhone 上,可用于空间应用的加固。YOLO-ET 对从日本国际空间站 "希望"(Kibo)舱返回的 Tanpopo 气凝胶面板的图像进行了训练,结果表明气凝胶表面污染物微粒的检测率达到 90%,在检测月球上的微粒污染物和评估小行星返回样本方面都取得了可喜的早期成果。YOLO-ET 在识别月球碎屑模拟样本中的航天器衍生微粒方面的应用,以及日本宇宙航空研究开发机构太空和宇航科学研究所对隼鸟2号返回的小行星龙宫样本的扫描电镜图像的研究表明,YOLO-ET 具有强大的模型性能和迁移学习能力,可用于未来的地外应用。
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来源期刊
Astronomy and Computing
Astronomy and Computing ASTRONOMY & ASTROPHYSICSCOMPUTER SCIENCE,-COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS
CiteScore
4.10
自引率
8.00%
发文量
67
期刊介绍: Astronomy and Computing is a peer-reviewed journal that focuses on the broad area between astronomy, computer science and information technology. The journal aims to publish the work of scientists and (software) engineers in all aspects of astronomical computing, including the collection, analysis, reduction, visualisation, preservation and dissemination of data, and the development of astronomical software and simulations. The journal covers applications for academic computer science techniques to astronomy, as well as novel applications of information technologies within astronomy.
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