Rapid measurement of cable micro-vibration using learning-based motion magnification and an improved-line tracking algorithm

This study explores the feasibility of learning-based motion magnification (LMM) for revealing cable motion under micro-vibrations. LMM enhances micro-vibrations without requiring a predefined target frequency band, while producing fewer bilateral artifacts around the cable. Therefore, this paper proposes a novel motion magnification-based method (LMMLT) for rapid measurement of cable vibration displacement under small-amplitude motion. LMMLT combines the LMM algorithm with improved line tracking technology (ILTT). Low image contrast under varying illumination is addressed using an enhanced TA-DeepLabV3plus model, which segments magnified frames and generates binary cable masks. ILTT then extracts subpixel displacements from the binary image sequence. Vibration frequencies are obtained through Fourier analysis of the extracted pixel displacements and used to estimate cable tension. The effectiveness of the LMMLT is verified by laboratory experiments and field tests on a cable-stayed bridge. ILTT reduces the runtime from 2.60 s/frame to 76 ms/frame at 640 × 480 pixels. The maximum cable force estimation error of the LMMLT approach is 1.75%. Field tests further demonstrate that the proposed method accurately identifies cable vibration under environmental excitations, with estimated frequencies highly consistent with accelerometer measurements and theoretical values.