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Fastaer det: Fast aerial embedded real-time detection

Wolf, Stefan ORCID iD icon 1; Sommer, Lars; Schumann, Arne
1 Karlsruher Institut für Technologie (KIT)


Automated detection of objects in aerial imagery is the basis for many applications, such as search and rescue operations, activity monitoring or mapping. However, in many cases it is beneficial to employ a detector on-board of the aerial platform in order to avoid latencies, make basic decisions within the platform and save transmission bandwidth. In this work, we address the task of designing such an on-board aerial object detector, which meets certain requirements in accuracy, inference speed and power consumption. For this, we first outline a generally applicable design process for such on-board methods and then follow this process to develop our own set of models for the task. Specifically, we first optimize a baseline model with regards to accuracy while not increasing runtime. We then propose a fast detection head to significantly improve runtime at little cost in accuracy. Finally, we discuss several aspects to consider during deployment and in the runtime environment. Our resulting four models that operate at 15, 30, 60 and 90 FPS on an embedded Jetson AGX device are published for future benchmarking and comparison by the community.

Verlagsausgabe §
DOI: 10.5445/IR/1000137035
Veröffentlicht am 12.09.2021
DOI: 10.3390/rs13163088
Zitationen: 6
Web of Science
Zitationen: 5
Zitationen: 6
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Anthropomatik und Robotik (IAR)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 2072-4292
KITopen-ID: 1000137035
Erschienen in Remote Sensing
Verlag MDPI
Band 13
Heft 16
Seiten 3088
Schlagwörter aerial object detection; deep learning based detection; embedded platforms; runtime optimization
Nachgewiesen in Web of Science
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