A Noncontact Technique for Wave Measurement Based on Thermal Stereography and Deep Learning

TL;DR

This paper introduces a noncontact wave measurement method combining thermal stereography and deep learning, overcoming optical challenges in water surface imaging and achieving high accuracy in wave field reconstruction.

Contribution

It presents a novel approach integrating thermal stereo imaging with deep learning for accurate, noncontact wave measurements, including a dataset synthesis and domain adaptation strategy.

Findings

Achieved less than 2.1% bias compared to wave probes.

Validated the method's effectiveness in wave flume experiments.

Demonstrated improved stereo matching using deep learning techniques.

Abstract

The accurate measurement of the wave field and its spatiotemporal evolution is essential in many hydrodynamic experiments and engineering applications. The binocular stereo imaging technique has been widely used to measure waves. However, the optical properties of indoor water surfaces, including transparency, specular reflection, and texture absence, pose challenges for image processing and stereo reconstruction. This study proposed a novel technique that combined thermal stereography and deep learning to achieve fully noncontact wave measurements. The optical imaging properties of water in the long-wave infrared spectrum were found to be suitable for stereo matching, effectively avoiding the issues in the visible-light spectrum. After capturing wave images using thermal stereo cameras, a reconstruction strategy involving deep learning techniques was proposed to improve stereo matching performance. A generative approach was employed to synthesize a dataset with ground-truth disparity from unannotated infrared images. This dataset was then fed to a pretrained stereo neural network for fine-tuning to achieve domain adaptation. Wave flume experiments were conducted to validate the feasibility and accuracy of the proposed technique. The final reconstruction results indicated great agreement and high accuracy with a mean bias of less than 2.1% compared with the measurements obtained using wave probes, suggesting that the novel technique effectively measures the spatiotemporal distribution of wave surface in hydrodynamic experiments.