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Enhancing underwater situational awareness: RealSense camera integration with deep learning for improved depth perception and distance measurement.

Tolie, Hamidreza Farhadi; Ren, Jinchang; Hasan, Junayed; Kannan, Somasundar

Authors



Contributors

Henri Bouma
Editor

Yitzhak Yitzhaky
Editor

Hugo J. Kuijf
Editor

Abstract

This work presents a depth image refinement technique designed to enhance the usability of a commercial camera in underwater environments. Stereo vision-based depth cameras offer dense data that is well-suited for accurate environmental understanding. However, light attenuation in water introduces challenges such as missing regions, outliers, and noise in the captured depth images, which can degrade performance in computer vision tasks. Using the Intel RealSense D455 camera, we captured data in a controlled water tank and proposed a refinement technique leveraging the state-of-the-art Depth-Anything model. Our approach involves first capturing a depth image with the Intel RealSense camera and generating a relative depth image using the Depth-Anything model based on the recorded color image. We then apply a mapping between the Depth-Anything generated relative depth data and the RealSense depth image to produce a visually appealing and accurate depth image. Our results demonstrate that this technique enables precise depth measurement at distances of up to 1.2 meters underwater.

Citation

TOLIE, H.F., REN, J., HASAN, M.J. and KANNAN, S. 2024. Enhancing underwater situational awareness: RealSense camera integration with deep learning for improved depth perception and distance measurement. In Bouma, H., Prabhu, R., Yitzhaky, Y. and Kuijf, H.J. (eds.) Artificial intelligence for security and defence applications II: proceedings of the 2024 SPIE Security + defence, 16-20 September 2024, Edinburgh, UK. Proceedings of SPIE, 13206. Bellingham, WA; SPIE [online], paper 1320605. Available from: https://doi.org/10.1117/12.3030972

Presentation Conference Type Conference Paper (published)
Conference Name 2024 SPIE Security + defence
Start Date Sep 16, 2024
End Date Sep 20, 2024
Acceptance Date Nov 13, 2023
Online Publication Date Nov 13, 2024
Publication Date Dec 31, 2024
Deposit Date Jan 9, 2025
Publicly Available Date Jan 27, 2025
Print ISSN 0277-786X
Electronic ISSN 1996-756X
Peer Reviewed Peer Reviewed
Article Number 1320605
Series Title Proceedings of SPIE
Series Number 13206
Series ISSN 0277-786X; 1996-756X
Book Title Artificial intelligence for security and defence applications II: proceedings of the 2024 SPIE Security + defence, 16-20 September 2024, Edinburgh, UK
ISBN 9781510681200
DOI https://doi.org/10.1117/12.3030972
Keywords Depth image; Depth refinement; Depth-anything; RealSense cameras
Public URL https://rgu-repository.worktribe.com/output/2656339

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Copyright Statement
© 2024 Society of Photo‑Optical Instrumentation Engineers (SPIE)




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