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  <doc>
    <id>6534</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>228</pageFirst>
    <pageLast>233</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace/>
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    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
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    <title language="eng">Luminance Simulation in CARLA under Cloud Coverage - Model Validation and Implications</title>
    <abstract language="eng">To decrease the number of kilometers driven during the development of autonomous cars or driving assistance systems, performant simulation tools are necessary. Currently, domain distance effects between simulation and reality are limiting the successful application of rendering engines in data-driven perception tasks. In order to mitigate those domain distance effects, simulation tools have to be as close to reality as possible for the given task. For optical sensors like cameras, the luminance of the scene is essential. We provide within this paper a method to measure the luminance of rendered scenes within CARLA, an often used open-source simulation environment. Thereby, it is possible to validate the environment and weather models by taking real-world measurements with photometric sensors or with the help of open-source weather data, published e.g. by the German federal service for weather data (DWD - "Deutscher Wetterdienst"). Employing our proposed luminance measurement, the domain gap resulting from the simulation can be specified, which makes it possible to evaluate the statements about the safety of the automated driving system determined within the simulation. We show that the ratio between global and diffuse radiation modeled by the default atmosphere models within CARLA are under limited conditions similar to real-world measurements taken by the DWD. Nevertheless, we show, that the ratio’s temporal variability in real-world situations is not modeled by CARLA.</abstract>
    <parentTitle language="eng">2023 IEEE International Workshop on Metrology for Automotive (MetroAutomotive), -30 June 2023, Modena, Italy</parentTitle>
    <identifier type="isbn">979-8-3503-2187-6</identifier>
    <identifier type="doi">10.1109/MetroAutomotive57488.2023.10219098</identifier>
    <enrichment key="opus.import.date">2023-10-16T20:23:06+00:00</enrichment>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Fabian Ulreich</author>
    <author>Elisabeth Moser</author>
    <author>Florian Olbrich</author>
    <author>Martin Ebert</author>
    <author>Rudolf Bierl</author>
    <author>André Kaup</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Atmospheric measurements</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Atmospheric modeling</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Cameras</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>carla</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Cloud computing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>cloud coverage</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Data models</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>luminance</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>meteorology</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Simulation</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Soft sensors</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>weather</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Wind</value>
    </subject>
    <collection role="institutes" number="FakANK">Fakultät Angewandte Natur- und Kulturwissenschaften</collection>
    <collection role="persons" number="bierlsappz">Bierl, Rudolf (Prof. Dr.) - Sensorik-ApplikationsZentrum</collection>
    <collection role="othforschungsschwerpunkt" number="16317">Sensorik</collection>
    <collection role="institutes" number="">Sensorik-Applikationszentrum (SappZ)</collection>
  </doc>
  <doc>
    <id>6294</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1</pageFirst>
    <pageLast>6</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">In-depth Benchmarking of Transfer Learning Techniques for Improved Bottle Recognition</title>
    <abstract language="eng">An immense diversity in bottle types requires high accuracy during sorting for recycling purposes by breweries. This extremely complex and time-consuming procedure can result in enormous additional costs for them. This paper presents transfer learning-based algorithms for classifying beer bottle brands using camera images, applicable in individual sorting solutions for different use cases. The problem is tackled using customised EfficientNet, InceptionResNet and VGG models along with an augmented dataset. In addition, a detailed analysis of different model and parameter combinations is performed, enabling tailor-made technologies for specific conditions and resource limitations. In accompanying validations and subsequent tests, a test accuracy of 100% in the recognition of beer brands could be achieved, proving the proposed method fully contributes to the solution of the problem.</abstract>
    <parentTitle language="eng">2023 IEEE 13th International Conference on Pattern Recognition Systems (ICPRS), Guayaquil, Ecuador, 04-07 July 2023</parentTitle>
    <identifier type="isbn">979-8-3503-3337-4</identifier>
    <identifier type="doi">10.1109/ICPRS58416.2023.10178995</identifier>
    <enrichment key="BegutachtungStatus">peer-reviewed</enrichment>
    <enrichment key="opus.doi.autoCreate">false</enrichment>
    <enrichment key="opus.urn.autoCreate">true</enrichment>
    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Dominik Bittner</author>
    <author>Ricky-Ricardo Hendricks</author>
    <author>Luca Horn</author>
    <author>Jürgen Mottok</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Analytical models</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Costs</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Transfer learning</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Benchmark testing</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Cameras</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Pattern recognition</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Recycling</value>
    </subject>
    <collection role="institutes" number="FakEI">Fakultät Elektro- und Informationstechnik</collection>
    <collection role="othforschungsschwerpunkt" number="16311">Digitalisierung</collection>
    <collection role="institutes" number="">Laboratory for Safe and Secure Systems (LAS3)</collection>
  </doc>
  <doc>
    <id>3728</id>
    <completedYear/>
    <publishedYear>2015</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>1855</pageFirst>
    <pageLast>1861</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <type>conferenceobject</type>
    <publisherName>IEEE</publisherName>
    <publisherPlace/>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Least squares pose estimation of cylinder axes from multiple views using contour line features</title>
    <abstract language="eng">In this paper, a new method for a minimum-error pose estimation of cylinder axes based on apparent contour line features from multiple views is presented. Novel model equations for both single and particularly multiple views are derived, and based upon these, an iterative algorithm for least squares fitting the model to imaged cylinder contour line features is introduced. The good performance and fast convergence of the proposed algorithm is shown by solving exemplary fitting problems.</abstract>
    <parentTitle language="eng">IECON 2015 - 41st Annual Conference of the IEEE Industrial Electronics Society, 9-12 Nov. 2015, Yokohama, Japan</parentTitle>
    <identifier type="doi">10.1109/IECON.2015.7392371</identifier>
    <enrichment key="opus.import.date">2022-04-28T18:57:25+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
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    <licence>Keine Lizenz - Es gilt das deutsche Urheberrecht: § 53 UrhG</licence>
    <author>Mark Becke</author>
    <author>Thomas Schlegl</author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Cameras</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Feature extraction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>GEOMETRY</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Image reconstruction</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Mathematical model</value>
    </subject>
    <collection role="institutes" number="FAKMB">Fakultät Maschinenbau</collection>
    <collection role="othforschungsschwerpunkt" number="16312">Energie und Mobilität</collection>
  </doc>
</export-example>
