Ullrich, Monika (2016) Combined Deep and Active Learning for Online 3D Object Recognition. DLR-Interner Bericht. DLR-IB-RM-OP-2016-364. Masterarbeit. Technische Universität München. 75 S.
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Kurzfassung
Deep learning methods have received lots of attention in research on 3D object recognition. Due to the lack of training data, many researchers use pre-trained Convolutional Neural Networks (CNNs) and either extract the output of one of the last layers as features or fine-tune the networks on their data. We achieve superior results with a method that fine-tunes a CNN before feature extraction for RGB data. Combined with extracted features from depth data and reducing the features’ dimensionalities, we improve the state-of-the-art accuracy on the University of Washington RGB-D Object dataset [Lai+11], using a support vector machine (SVM). Furthermore, we evaluate the impact of different learning rates (LRs) when fine-tuning a CNN. Our results show that the selection of a suitable LR is crucial to the success of a network. Instead of SVM as a classifier, we also use the Mondrian forest (MF), an online classifier, which can be updated over time as soon as more data is available.
elib-URL des Eintrags: | https://elib.dlr.de/110280/ | ||||||||
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Dokumentart: | Berichtsreihe (DLR-Interner Bericht, Masterarbeit) | ||||||||
Titel: | Combined Deep and Active Learning for Online 3D Object Recognition | ||||||||
Autoren: |
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Datum: | 2016 | ||||||||
Referierte Publikation: | Nein | ||||||||
Open Access: | Nein | ||||||||
Seitenanzahl: | 75 | ||||||||
Status: | veröffentlicht | ||||||||
Stichwörter: | 3D object recognition, vector machine, CNN, LR, Mondrian forest, SVM | ||||||||
Institution: | Technische Universität München | ||||||||
Abteilung: | Department of Informatics | ||||||||
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 - Vorhaben Multisensorielle Weltmodellierung (alt) | ||||||||
Standort: | Oberpfaffenhofen | ||||||||
Institute & Einrichtungen: | Institut für Robotik und Mechatronik (ab 2013) | ||||||||
Hinterlegt von: | Schlögl, Birgit | ||||||||
Hinterlegt am: | 10 Jan 2017 09:43 | ||||||||
Letzte Änderung: | 10 Jan 2017 09:43 |
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