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Verlagsausgabe
DOI: 10.5445/IR/1000085982
Veröffentlicht am 04.10.2018

Deep Learning based Vehicle Detection in Aerial Imagery

Sommer, Lars

Abstract (englisch):
Detecting vehicles in aerial images is an important task for many applications like traffic monitoring or search and rescue work. In recent years, several deep learning based frameworks have been proposed
for object detection. However, these detection frameworks were developed and optimized for datasets that exhibit considerably differing characteristics compared to aerial images, e.g. size of objects to detect. In this report, we demonstrate the potential of Faster R-CNN, which is one of the state-of-theart detection frameworks, for vehicle detection in aerial images. Therefore, we systematically investigate the impact of adapting relevant parameters. Due to the small size of vehicles in aerial images, the most improvement in performance is achieved by using features of shallower layers to localize vehicles. However, these features offer less semantic and contextual information compared to features of deeper layers. This results in more false alarms due to objects with similar shapes as vehicles. To account for that, we further propose a deconvolutional module that up-samples features of deeper layers and combines these features w ... mehr


Zugehörige Institution(en) am KIT Lehrstuhl IES Beyerer: Interaktive Echtzeitsysteme (Lehrstuhl IES Beyerer: Interaktive Echtzeitsysteme)
Publikationstyp Proceedingsbeitrag
Jahr 2018
Sprache Englisch
Identifikator ISBN: 978-3-7315-0779-6
ISSN: 1863-6489
URN: urn:nbn:de:swb:90-859829
KITopen ID: 1000085982
Erschienen in Proceedings of the 2017 Joint Workshop of Fraunhofer IOSB and Institute for Anthropomatics, Vision and Fusion Laboratory. Ed.: J. Beyerer
Verlag KIT Scientific Publishing, Karlsruhe
Seiten 83-97
Serie Karlsruher Schriften zur Anthropomatik / Lehrstuhl für Interaktive Echtzeitsysteme, Karlsruher Institut für Technologie ; Fraunhofer-Inst. für Optronik, Systemtechnik und Bildauswertung IOSB Karlsruhe ; 34
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