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Updated: Aug 15, 2026
09:22
Quantitatively Measuring In situ Flows using a Self-Contained Underwater Velocimetry Apparatus (SCUVA)
Published on: October 31, 2011
LG vortex-beam speckle-based underwater surface roughness measurement via a lightweight classification-regression
Optics Express
|August 14, 2026
Summary
A new lightweight network, LECR-Net, accurately measures underwater surface roughness using Laguerre-Gaussian (LG) vortex-beam speckle images. This method achieves high classification accuracy and low prediction error, making it ideal for underwater equipment.
Area of Science:
- Optical Engineering
- Materials Science
- Machine Learning
Background:
- Underwater surface roughness impacts flow friction, acoustic, and optical scattering.
- Accurate roughness assessment is vital for underwater equipment performance.
- Laguerre-Gaussian (LG) vortex beams offer structured illumination sensitive to surface scattering.
Purpose of the Study:
- To develop a lightweight and accurate method for underwater surface roughness measurement.
- To leverage LG vortex-beam speckle patterns for roughness characterization.
- To propose LECR-Net, an EfficientNetV2-based network for this task.
Main Methods:
- Development of LECR-Net, a compressed EfficientNetV2 backbone with attention mechanisms.
- Implementation of a conditional residual regression strategy for refined roughness prediction.
- Experimental construction of an underwater LG vortex-beam speckle dataset using steel samples.
Main Results:
- LECR-Net achieved 99.98% accuracy in classifying machining methods and roughness categories.
- Maximum relative error for roughness prediction was kept below 4%.
- The model size was reduced to 0.86 MB with 0.21 M parameters.
Conclusions:
- LECR-Net provides an accurate and lightweight solution for underwater surface roughness measurement.
- The proposed method effectively utilizes LG vortex-beam speckle imaging.
- The network's efficiency makes it suitable for deployment on underwater equipment.
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