Machine Vision-Based Super-Resolution Reconstruction for High-Precision Structural Displacement Monitoring: Potential Application to Hydraulic Structures
You Yang1,2, Bo Chen1,2,*, Weiqi Liu3, Zekai Ma1,2
1 The National Key Laboratory of Water Disaster Prevention, Hohai University, Nanjing, China
2 College of Water Conservancy and Hydropower Engineering, Hohai University, Nanjing, China
3 POWERCHINA Chengdu Engineering Corporation Limited, Chengdu, China
* Corresponding Author: Bo Chen. Email: 
Computers, Materials & Continua https://doi.org/10.32604/cmc.2026.086073
Received 23 May 2026; Accepted 19 August 2026; Published online 10 September 2026
Abstract
This paper proposes a non-contact intelligent method using machine vision and super-resolution (SR) reconstruction. The method uses artificial targets, a high-order degradation model, and camera-based acquisition for displacement calculation. Building upon the (Real-Enhanced Super-Resolution Generative Adversarial Networks) Real-ESRGAN framework, this paper introduces a feature fusion attention mechanism to improve the Real-ESRGAN network and generator, enabling the reconstruction of image contours and fine details to enhance displacement calculation accuracy. Quantitative laboratory validation on a benchmark shake-table dataset demonstrates improved displacement-monitoring accuracy, while hydraulic-structure imagery is used only to demonstrate reconstruction performance. The results indicate potential applicability to hydraulic-structure monitoring; independent quantitative validation under operational hydraulic conditions remains future work. Compared with traditional super-resolution algorithms and target tracking methods, the improved Real-ESRGAN algorithm performs the best, with a coefficient of determination (R
2) of up to 0.9975, a mean absolute error (MAE) as low as 0.5552 mm, and residual errors controlled within 5 mm in the benchmark experiment. The reconstructed images preserve edge contours and fine details, supporting the use of super-resolution for marker-based structural displacement monitoring.
Keywords
Machine vision displacement monitoring; image super-resolution reconstruction; real-ESRGAN improvement; feature fusion attention mechanism; hydraulic structure safety monitoring
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