TL;DR: This study investigates and compares three different algorithms for real-time displacement extraction, evaluating their performance under controlled conditions through synthetic video sequences with exactly known imposed motion, and identifies their strengths and limitations.
Vision-based displacement measurement has gained considerable attention in Structural Health Monitoring (SHM), where the need to detect small structural motions with non-contact instrumentation has driven the development of subpixel estimation algorithms capable of achieving resolution well below the nominal pixel size. This study investigates and compares three different algorithms for real-time displacement extraction, evaluating their performance under controlled conditions through synthetic video sequences with exactly known imposed motion. Particular attention is devoted to the systematic errors inherent to each algorithm and to how these are influenced by external factors typical of SHM real-world applications, namely optical blur and target size. The analysis is carried out using AprilTag markers as targets identifying the points whose displacements are tracked. The presented study assesses each algorithm in terms of accuracy, robustness, and computational efficiency, with the goal of identifying their strengths and limitations, thus, offering practical guidance for their application in real-world monitoring scenarios.
TL;DR: This article focuses on the radar-only computer-vision task of classifying rotors without complementary costly instrumentation, and measured radargrams from field experiments are complemented with the novel synthetic dataset SiWiRoRa as well as further open imagery.
Tower-radar computer vision (TRCV) represents an emerging application-oriented field of study. Here, image-type measurements are acquired from radar transceivers bound to the mast of wind power turbines and these radargrams subsequently get analyzed using data-driven algorithms.…
TL;DR: A framework for CVSHM, which allows for robust detection, localization, and assessment of the damage even for highly contaminated displacement data, is proposed and tested using realistic synthetic videos representing vibrating truss structure.
Computer-vision-based structural health monitoring (CVSHM) enables contactless displacement measurement at multiple locations on the vibrating structure. Additionally, such a measurement can be realized from a certain distance from the monitored infrastructure. It provides a poss…
Dam displacement monitoring is imperative to assess the operational status and structural safety of dams under various environmental conditions and operational loads. Although most of the dam structures are instrumented with robust in-situ sensing systems, long-term field monitor…
Visual spectrum cameras have become increasingly popular for non-contact full-field structural dynamics measurements, enabling displacement and deformation identification through techniques such as Digital Image Correlation. However, obtaining strain information from kinematic me…
TL;DR: An AI-driven system that can provide comprehensive structural health diagnostics from a single input structural image, using six parallel computer-vision pipelines with experimentally validated real-time inference performance, forms a strong base toward automated, scalable, and data-driven structural health monitoring.
Context / Content: Civil infrastructure and heritage structures deteriorate with time due to environmental exposure, material aging, moisture ingress, pollution, and biological growth. Traditional inspection relies heavily on manual assessment, which is slow, risky, and subjectiv…
Recent advances in Physics-Informed Neural Networks (PINNs) have opened new possibilities for integrating structural dynamics and data-driven learning in Structural Health Monitoring (SHM). This work presents a physics-informed framework for input load estimation and virtual sens…