The instrument, which the researchers call a computer vision-based measurement monitoring (CVMM) system, works on a principle reminiscent of a surveyor’s theodolite. A 20-centimeter chessboard target is fixed to the point of interest on the dam crest, while the camera sits at a stable vantage point on the opposite riverbank. OpenCV algorithms running in Python detect the corners of the chessboard in each frame, calibrate for lens distortion, and convert pixel shifts into real-world displacement using the mathematics of planar homography and camera projection matrices. Because the true size of the chessboard is known, the system can even estimate the distance between the camera and the target from how large the board appears in the image, recovering motion along the depth axis as well as horizontally and vertically.

Accuracy was established through careful laboratory characterization. On a three-axis manual stage, the static standard deviation of the measurements was just 0.058, 0.042, and 0.067 pixels in the X, Y, and Z axes respectively, across more than 2,500 data points per axis. When the chessboard was moved by exactly one centimeter, the system reproduced the motion with an accuracy of 0.01 centimeters, where a single pixel corresponded to roughly 0.16 centimeters at the test distance. Long-range trials at 15, 20, 35, and 50 meters showed average standard deviations of 0.01 to 0.02 centimeters up to 35 meters, degrading to 0.06 centimeters beyond 50 meters, which led the team to recommend installation distances under 35 meters for their industrial lens with 10x optical zoom.

Crucially, the system does more than track displacement. Running at 100 frames per second—nearly the maximum 120 frames per second supported by the camera module—the CVMM captures vibrational motion that conventional time-lapse photography would average away. Displacement time series can be differentiated to yield velocity, and then decomposed spectrally. The researchers applied the Fast Fourier Transform to isolate dominant frequencies, the Short-Time Fourier Transform to see how those frequencies evolve in time, and finally the Hilbert-Huang Transform, a technique designed for nonlinear, non-stationary signals that breaks the record down into intrinsic mode functions and computes instantaneous frequency and energy for each one.

To validate the vibration measurements, the team mounted a chessboard alongside a commercial Raspberry Shake 4D seismograph on a QUANSER shake table and drove the table through a series of controlled oscillations at constant 1-centimeter amplitude but varying accelerations, from 0.8 to 200 gal. The image acquisition frame rate achieved 98.76 percent of its 100-frames-per-second target, and image recognition succeeded 99.70 percent of the time. After a median filter smoothed out spike noise, spectral analysis showed that the primary frequencies detected by the CVMM aligned closely both with the shake table’s input settings and with the independent seismograph record. The maximum amplitude error was 0.13 centimeters, with an average of 0.09 centimeters, confirming that a camera tracking a checkerboard can genuinely function as a non-contact vibrometer across a range from 0.1 to 50 Hz.

The decisive test came on the Lantao river in Nantou County, Taiwan, where the researchers built an artificial earth dam 27.8 meters wide and 3.0 meters high from a mix of boulders, gravel, and sand typical of landslide-formed debris dams. The chessboard was planted on the dam crest near a pre-cut breaching channel, and the CVMM instrument was set up 35.32 meters away on the right riverbank, a distance verified with a theodolite. Water was then fed into the reservoir until it overtopped the crest and carved the dam apart. Over 1,249 seconds of monitoring, the target slid 14.64 centimeters horizontally toward the developing breach and settled 26.77 centimeters vertically before it finally collapsed with the failing dam.

The time-frequency analysis of this field data revealed a striking pattern. As the breach developed between 15:16 and 15:20, vibration energy surged across three distinct frequency bands: a high-frequency band from 20 to 50 Hz, a medium band from 10 to 20 Hz, and a low band below 10 Hz. Following established findings that high-frequency ground vibrations arise from small particles like sand and gravel colliding while low frequencies reflect the movement of larger rocks, the team concluded that the dominant energy came from the washing out and collision of finer sediments as overflow eroded the crest. The vertical axis carried more energy than the horizontal—maximum instantaneous energies of 2,500, 350, and 50 square centimeters per second squared in the high, medium, and low bands of the Y axis, compared with 1,800, 200, and 30 on the X axis—consistent with settlement being the more violent process.

Most importantly for disaster response, the Hilbert spectra showed a sharp rise in instantaneous energy beginning around 15:16, roughly two minutes before the overflow at 15:18 and the full breach at 15:20. Because the first intrinsic mode function carried over 84 percent of the total energy, the researchers proposed an early-warning threshold of 1,000 square centimeters per second squared in that high-frequency band, a level that was exceeded more than 36 times during the initial breach phase. Combined with a simpler displacement indicator—settlement exceeding 5 centimeters at the moment of overtopping—these thresholds could give downstream communities precious minutes of advance notice. LiDAR scans taken before and after the test independently confirmed the elevation changes the camera system had tracked in real time.

The economics are equally compelling. The entire CVMM instrument costs roughly 1,000 to 2,000 US dollars depending on configuration, about one-fifth the price of conventional monitoring packages such as total stations, GNSS receivers, or LiDAR units. If the chessboard target is destroyed in a failure event, replacing it is trivial. The system also delivers three data streams simultaneously—imagery, displacement, and vibration—where each traditional device provides only one, and it requires no physical contact with a structure that may be on the verge of collapse, addressing a fundamental weakness of contact sensors like the Raspberry Shake, which cannot safely be deployed on a failing slope.

The authors are candid about the limitations. The energy threshold was derived from a single field test on one soil type and will need validation across different dam materials. Computer vision remains vulnerable to heavy rain, poor night lighting, atmospheric turbulence, and camera shake, although previous work by the same group using a weatherproof enclosure and a solar-powered LED chessboard achieved 24-hour monitoring with errors near 0.1 centimeter at 50 meters. No Raspberry Shake was deployed on the dam crest itself for direct comparison during the breach, and coupling the vibrational signatures to pore pressure changes and internal failure mechanisms will require additional instruments such as piezometers. Even so, the demonstration stands as a proof of concept that a modest camera, a printed checkerboard, and clever signal processing can transform dam safety monitoring—turning an ordinary machine vision pipeline into an early-warning sentinel for one of the deadliest failure modes in mountainous terrain.

Subject of Research: Computer vision-based monitoring of dam breach displacement and vibration for early warning

Article Title: Computer vision-based system of vibration monitoring for early-warning signals in dam breach

Article References: Chen, I.-H., Chen, S.-C., & Yang, R.-J. (2026). Computer vision-based system of vibration monitoring for early-warning signals in dam breach. Environmental Earth Sciences, 85(15), Article 392. https://doi.org/10.1007/s12665-026-13117-7

Image Credits: AI Generated

DOI: 10.1007/s12665-026-13117-7

Keywords: computer vision, dam breach, vibration monitoring, displacement monitoring, Hilbert-Huang Transform, OpenCV, Raspberry Pi, early warning, time-frequency analysis, earth dam, landslide dam, structural monitoring