Chonnam National University Researchers Develop Novel Virtual Sensor Grid Method for Low-Cost, Yet Robust, Infrastructure Monitoring

Researchers utilize superpixels to enhance the robustness and accuracy of vision-based structural health monitoring

JEOLLANAM-DO PROVINCE, South Korea, Jan. 12, 2026 /PRNewswire/ -- Structural health monitoring (SHM) and condition monitoring are crucial for ensuring reliability and safety of engineering systems across aerospace, civil engineering, and industry. These systems are often assessed using vibration-based methods, where damage is detected by analyzing changes in a structure's vibration characteristics. Traditional methods typically employ contact-type sensors for this purpose. While effective, these methods face several limitations, including low spatial resolution, high costs, difficulties in sensor placement, and measurements that are restricted to small regions around each sensor.

Vision-based methods enable non-contact, full-field vibration measurement directly from video sequences, offering a promising alternative that is simple, low cost, offers high spatial resolutions, and suits structures with complex geometries or limited accessibility. Full-field motion estimation also enables assessment of the entire structure. However, many existing approaches struggle with large structural motions, low-texture surfaces, or lighting changes. Although recently developed phase-based optical flow methods improve robustness by estimating motion from phase information, they still rely on pixel-level data, making them computationally intensive, difficult to interpret, and vulnerable to noise, lighting fluctuations, and distortion.

To address these challenges, researchers led by Professor Gyuhae Park from the Department of Mechanical Engineering at Chonnam National University in South Korea developed a novel superpixel-based virtual sensor framework. "Our approach utilizes superpixels, clusters of neighboring pixels with similar vibrational and structural behavior, as virtual sensors for motion estimation," explains Prof. Park. "This creates an adaptable virtual sensor grid for any structure, enabling robust and accurate full-field vibration measurement without the need for physical markers or contact sensors." The study was made available online on September 30, 2025, and published in Volume 240 of Mechanical Systems and Signal Processing on November 01, 2025.

The proposed approach operates in three stages. First, pixel-level motion is estimated from video sequences using the phase nonlinearity-weighted optical flow algorithm, developed by the authors in a previous study. For each pixel, the algorithm extracts local motion from phase information and evaluates the reliability of the estimated displacements in different directions. Unreliable displacement components with high phase nonlinearity are discarded, and the remaining reliable components are integrated to produce a marker-free full-field displacement map.

Second, the overall confidence of the full displacement at each pixel is calculated, providing a built-in reliability assessment, a first among vision-based vibration measurement methods.

In the third stage, this confidence and the full field displacement map are used together to group pixels into superpixels, creating a virtual sensor grid. Depth information is incorporated to improve the grid-structure alignment. Finally, full-field displacement is calculated at the sensor level for damage detection.

Experimental validation on an air compressor system showed that this method achieves accuracy comparable to that of a laser Doppler vibrometer while enabling effective structural damage detection without physical markers or contact sensors. While individual pixels showed some variability, the superpixel-based virtual sensors effectively mitigated these effects.

"Vibration-guided superpixel segmentation enhances robustness and interpretability of structural diagnostics even in complex environments," explains Prof. Park. "Our approach makes full-field structural monitoring accessible, low-cost, and deployable using ordinary cameras supporting applications in infrastructure monitoring, aerospace and mechanical equipment diagnostics, smart cities, robotics, and digital twins."

Overall, this innovative method represents a major advancement for vision-based SHM and may help pave the way for its broader adoption.

Reference

Chonnam National University Website: https://global.jnu.ac.kr/jnumain_en.aspx

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SOURCE Chonnam National University