An Autonomous Illumination System for Vehicle Documentation Based on Deep Reinforcement Learning Article Swipe
Lampros Leontaris
,
Nikolaos Dimitriou
,
Dimosthenis Ioannidis
,
Konstantinos Votis
,
Dimitrios Tzovaras
,
Elpiniki I. Papageorgiou
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/access.2021.3081736
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/access.2021.3081736
A common problem for machine vision applications is uncontrolled illumination conditions that cause undesired artifacts on sensorial data. For instance, quality inspection using color cameras, while having wide industrial application, requires manual illumination adjustment and is severely affected by external lighting sources and the physical properties of the inspected object. To overcome this problem, we propose an autonomous illumination solution, that adjusts illumination via a Deep Reinforcement Learning (DRL) agent following a goal-oriented reward that takes into account image entropy and specularity. The system is validated in a challenging vehicle documentation use case where vehicle images are captured under various lighting conditions using a camera and an in-house built illumination system. The DRL agent learns to control illumination levels directly from high-dimensional visual inputs by mapping the interactions from the environment to the reward-driven control actions of the illumination system, targeting an optimal illumination zone even under the appearance of abrupt illumination changes in the environment. © 2013 IEEE.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2021.3081736
- https://ieeexplore.ieee.org/ielx7/6287639/9312710/09435376.pdf
- OA Status
- gold
- Cited By
- 9
- References
- 68
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3162200879
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3162200879Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2021.3081736Digital Object Identifier
- Title
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An Autonomous Illumination System for Vehicle Documentation Based on Deep Reinforcement LearningWork title
- Type
-
articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-01-01Full publication date if available
- Authors
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Lampros Leontaris, Nikolaos Dimitriou, Dimosthenis Ioannidis, Konstantinos Votis, Dimitrios Tzovaras, Elpiniki I. PapageorgiouList of authors in order
- Landing page
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https://doi.org/10.1109/access.2021.3081736Publisher landing page
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https://ieeexplore.ieee.org/ielx7/6287639/9312710/09435376.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://ieeexplore.ieee.org/ielx7/6287639/9312710/09435376.pdfDirect OA link when available
- Concepts
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Computer science, Documentation, Reinforcement learning, Artificial intelligence, Human–computer interaction, Computer vision, Operating systemTop concepts (fields/topics) attached by OpenAlex
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9Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 3, 2023: 3, 2022: 2Per-year citation counts (last 5 years)
- References (count)
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68Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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