Vision-Based Rebar Diameter Identification via KLT Optical Flow Feature Tracking and Vibration Frequency Inversion
DOI:
https://doi.org/10.54097/0mzt1t33Keywords:
Computer Vision, KLT Algorithm, Vibration Frequency, Rebar Dimension Identification, Euler–Bernoulli Beam Theory, Non-contact MeasurementAbstract
Accurate identification of rebar dimensions is critical for quality control and structural safety in civil engineering. Traditional inspection methods rely on manual measurement or contact-based sensors, which are labor-intensive and inefficient for large-scale or automated applications. This paper presents a vision-based non-contact method for rebar dimension identification by analyzing vibration frequency. The method uses a high-speed camera to capture rebar free vibration, applies the Kanade–Lucas–Tomasi (KLT) algorithm to extract displacement signals, and identifies the dominant frequency via Fast Fourier Transform (FFT). Based on Euler–Bernoulli beam theory, the rebar diameter is then inversely estimated from the identified frequency. Two case studies were conducted for validation. A ruler experiment first confirmed that the KLT-FFT framework can reliably extract vibration frequencies under controlled conditions, with the inversely estimated Young’s modulus showing a maximum error of only 2.89%. Subsequent rebar experiments demonstrated that the calculated diameters closely match measured values, verifying the feasibility of frequency-based dimension identification. An additional study on camera motion compensation showed that the method remains robust under in-plane translational disturbances. The proposed approach offers advantages including non-contact operation, low cost, and automation potential, providing a practical basis for rapid rebar dimension assessment in engineering applications.
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