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@PHDTHESIS{Wenning:856819,
author = {Wenning, Marius Julian},
othercontributors = {Schuh, Günther and Burggräf, Peter},
title = {{K}ontextabhängig {A}nomaliedetektion zur visuellen
{H}inderniserkennung für automatisierte {PKW} im
{E}nd-of-{L}ine-{B}ereich; 1. {A}uflage},
volume = {37/2022},
school = {RWTH Aachen University},
type = {Dissertation},
address = {Aachen},
publisher = {Apprimus Verlag},
reportid = {RWTH-2022-11143},
isbn = {978-3-98555-121-7},
series = {Ergebnisse aus der Produktionstechnik},
pages = {XIII, 154 Seiten : Illustrationen, Diagramme},
year = {2022},
note = {Druckausgabe: 2022. - Auch veröffentlicht auf dem
Publikationsserver der RWTH Aachen University 2023;
Dissertation, RWTH Aachen University, 2022},
abstract = {For automated driving on company premises of car
manufacturers, the cars’ protective device needs to be
implemented using standard sensor equipment. Built-in mono
cameras cannot be used for obstacle detection yet, as
existing computer vision algorithms do not provide reliable
object detection. The analysis of the computer vision
algorithms leads to a specification of requirements for an
innovative obstacle detection algorithm. The method of
anomaly detection shows inherent advantages compared to
existing obstacle detection algorithms. The state of the art
includes promising methods. However, anomaly detection has
not been tested in the use case of vehicle automation so
far. Therefore, the objective of this thesis is to design a
suitable anomaly detection algorithm and to test it in the
use case of factory-automated cars. To this end, two data
sets are developed. Data from a simulated factory
environment is used to develop a performant anomaly
detection and to test it against state-of-the-art benchmark
algorithms in use case-specific test cases. Real data
enables validation of the simulation results and proves the
algorithm’s practicality. A detailed analysis reveals
implicit design rules that should be considered in the
development of an anomaly detection for obstacle detection.
The classification quality can be improved by taking into
account the spacial and temporal context of the processed
images. In comparison with state-of-the-art algorithms, the
anomaly detection shows a competitive classification quality
at only a fraction of required training data. In scenes with
high complexity due to illumination, dirt or smoke, the
anomaly detection is more robust than the semantic
segmentation and the depth estimation. Real world
experiments confirm the simulation results and prove
practical applicability.},
cin = {417210 / 417200},
ddc = {620},
cid = {$I:(DE-82)417210_20140620$ / $I:(DE-82)417200_20140620$},
pnm = {BMBF-01MV19002A - Verbundprojekt: AIMFREE - Agile Montage
von Elektrofahrzeugen durch freie Verkettung; Teilvorhaben:
Methoden, Modelle und Technologien zur Umsetzung der agilen,
frei verketteten Montage von Elektrofahrzeugen
(BMBF-01MV19002A)},
pid = {G:(DE-82)BMBF-01MV19002A},
typ = {PUB:(DE-HGF)11 / PUB:(DE-HGF)3},
doi = {10.18154/RWTH-2022-11143},
url = {https://publications.rwth-aachen.de/record/856819},
}
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