Underwater ranging with laser and camera based on Mask R-CNN algorithm

Bin Xie; Qingshan Ma; Biao Han; Lin Ma

doi:10.1117/12.2651758

20 January 2023 Underwater ranging with laser and camera based on Mask R-CNN algorithm

Bin Xie, Qingshan Ma, Biao Han, Lin Ma

Proceedings Volume 12561, AOPC 2022: Atmospheric and Environmental Optics; 1256107 (2023) https://doi.org/10.1117/12.2651758
Event: Applied Optics and Photonics China 2022 (AOPC2022), 2022, Beijing, China

Abstract

Ocean engineering has recently given a lot of attention to a laser and vision-based underwater range technique. This technique involves the use of lasers to create spot patterns on an object's surface that are then captured on camera. The range might then be determined from the patterns by mapping the correlation between camera spot patterns and real distance. However, the laser paths create "beams" in the camera as a result of backscattering underwater. This impacts the ranging accuracy and makes it challenging to determine the spot position. The Mask R-CNN algorithm-based approach is proposed in this research as a solution to this issue. First, an underwater visual laser ranging system was constructed using a camera, four lasers, and deep learning training with the Mask R-CNN algorithm to recognize and segment the spots in the image. The link between it and the target's distance is then determined by mathematical fitting, using the perimeter of the region the light spot occupies as the geometric feature quantity. Finally, the measured pattern in the camera predicts the object's distance. The findings demonstrate that the measurement accuracy is at the centimeter level, which is beneficial and advantageous for precise underwater ranging.

Citation Download Citation

Bin Xie, Qingshan Ma, Biao Han, and Lin Ma "Underwater ranging with laser and camera based on Mask R-CNN algorithm", Proc. SPIE 12561, AOPC 2022: Atmospheric and Environmental Optics, 1256107 (20 January 2023); https://doi.org/10.1117/12.2651758

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