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A deep learning approach to crater detection

Blumenkamp, Jan (2019) A deep learning approach to crater detection. Bachelorarbeit, Universität Bremen.

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Kurzfassung

Detecting craters can be useful for a broad variety of tasks, such as monitoring the development of celestial bodies over time or for navigational purposes. Deep learning as a tool for solving complex tasks in image processing became increasingly popular in the recent years. While there are a few works targeting crater detections with deep learning, most focus on monitoring celestial bodies, for which the requirements are different than for the purpose of navigation, where less computing power is available and additional crater parameters must be recovered from the image. In this work, a deep learning approach to crater detection is developed, implemented and evaluated. Due to the absence of proper training data a way to generate the training data with the Planet and Asteroid Natural Scene Generation Utility (PANGU) simulator is implemented. With this simulator a data set consisting of 20000 images and crater labels was generated. A segmentation approach is then used to create probability distributions for crater rims and centers which is then post-processed with conventional methods by using domain knowledge. The researched approaches are evaluated in terms of precision, recall and general accuracy. The different crater parameters are evaluated by categorizing the detections into true positives, false positives and false negatives which are then analyzed separately. Furthermore, the execution time of both approaches is compared. Lastly, the approach is evaluated by applying it to images taken on recent missions to the moon and other celestial bodies.

elib-URL des Eintrags:https://elib.dlr.de/129587/
Dokumentart:Hochschulschrift (Bachelorarbeit)
Titel:A deep learning approach to crater detection
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Blumenkamp, Janjan.blumenkamp (at) dlr.deNICHT SPEZIFIZIERTNICHT SPEZIFIZIERT
Datum:26 Juni 2019
Referierte Publikation:Nein
Open Access:Nein
Seitenanzahl:58
Status:veröffentlicht
Stichwörter:deep learning, optical navigation
Institution:Universität Bremen
Abteilung:Fachbereich 03 - Mathematik und Informatik
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Technik für Raumfahrtsysteme
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R SY - Technik für Raumfahrtsysteme
DLR - Teilgebiet (Projekt, Vorhaben):R - Optische Navigation auf hybrider Avionikarchitektur
Standort: Bremen
Institute & Einrichtungen:Institut für Raumfahrtsysteme > Navigations- und Regelungssysteme
Hinterlegt von: Krüger, Hans
Hinterlegt am:18 Okt 2019 11:11
Letzte Änderung:18 Okt 2019 11:11

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