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Optimization of Multi-LED Setups for Underwater Robotic Vision Systems

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Pattern Recognition. ICPR International Workshops and Challenges (ICPR 2021)

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Abstract

In deep water conditions, vision systems mounted on underwater robotic platforms require artificial light sources to illuminate the scene. The particular lighting configurations significantly influence the quality of the captured underwater images and can make their analysis much harder or easier. Nowadays, classical monolithic Xenon flashes are gradually being replaced by more flexible setups of multiple powerful LEDs. However, this raises the question of how to arrange these light sources, given different types of seawater and-depending-on different flying altitudes of the capture platforms. Hence, this paper presents a rendering based coarse-to-fine approach to optimize recent multi-light setups for underwater vehicles. It uses physical underwater light transport models and target ocean and mission parameters to simulate the underwater images as would be observed by a camera system with particular lighting setups. This paper proposes to systematically vary certain design parameters such as each LED’s orientation and analyses the rendered image properties (such as illuminated image area and light uniformity) to find optimal light configurations. We report first results on a real, ongoing AUV light design process for deep sea mission conditions.

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Acknowledgements

This publication has been funded by the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) Projektnummer 396311425, through the Emmy Noether Programme.

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Correspondence to Yifan Song .

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Song, Y., Sticklus, J., Nakath, D., Wenzlaff, E., Koch, R., Köser, K. (2021). Optimization of Multi-LED Setups for Underwater Robotic Vision Systems. In: Del Bimbo, A., et al. Pattern Recognition. ICPR International Workshops and Challenges. ICPR 2021. Lecture Notes in Computer Science(), vol 12662. Springer, Cham. https://doi.org/10.1007/978-3-030-68790-8_30

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  • DOI: https://doi.org/10.1007/978-3-030-68790-8_30

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-68789-2

  • Online ISBN: 978-3-030-68790-8

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