Vision-based displacement monitoring of long-distance bridges is highly vulnerable to camera ego-motion caused by wind, ground vibration, and instability of the camera support system. As a result, camera disturbances are directly mixed into the measured image motion and can be mistaken for structural displacement. This issue becomes more apparent when phase-based motion estimation is used, because its high sensitivity to small vibrations also makes it responsive to minute camera disturbances. This paper presents a camera motion compensation method for long-distance bridge monitoring that integrates phase-based motion estimation with IMU-camera calibration. The camera and IMU are calibrated using high-precision phase-based measurements of image motion from a stationary reference target, allowing synchronized inertial measurements to be mapped to camera-induced pixel motion. The estimated camera-motion component is then removed from the measured displacement to improve the reliability of structural response estimation. Experimental validation demonstrates that the proposed framework enhances the accuracy and stability of phase-based displacement measurement under practical long-distance monitoring conditions.

During the service life of bridges, early-stage defects such as cracks are inevitable. Among them, fine cracks with a width of approximately 0.2 mm are critical control indicators in structural safety assessment and durability evaluation, and bridge inspection codes explicitly require their detection and quantification. However, bridge towers and other tall components contain extensive areas that are difficult to access through manual inspection. Although non-contact inspection based on unmanned aerial vehicles (UAV) offers significant advantages in safety and efficiency, reliably identifying 0.2 mm cracks at practical inspection distances remains a key engineering challenge.

To address this issue, this study focuses on developing a data acquisition strategy for UAV inspection that is aligned with the 0.2 mm crack threshold specified in bridge inspection codes. Particular attention is given to the influence of imaging distance and illumination conditions on crack recognizability. An already damaged beam was selected as the experimental subject, and a DJI Matrice 4T aircraft was employed to collect crack images under systematically controlled distance conditions. The flight path was arranged perpendicular to the beam surface, with constant gimbal orientation and imaging angle to minimize pose-related variability.

Image acquisition started at 2.5 m from the target surface and increased in 0.5 m intervals, reaching a maximum distance of 45.0 m. A total of 280 crack images were collected under clear afternoon conditions. Target cracks with widths of approximately 0.2 mm were identified using a crack width comparison gauge. Pixel-level annotations were performed using an open-source image annotation tool, and crack recognition was evaluated through a semantic segmentation task implemented with a standard version of the You Only Look Once 11(YOLO11) model, without architectural modification. This choice was made to avoid dependence on model-specific enhancements and to better ensure the general applicability of the proposed data acquisition strategy across commonly used detection frameworks.

Illumination effects were preliminarily analyzed by applying synthetic brightness adjustments to the collected images. Under these controlled adjustments, the segmentation recall remained above 0.80 and the mean Intersection over Union (mIoU) remained above 0.65, suggesting limited sensitivity to moderate brightness variation. However, this conclusion is restricted to simulated brightness changes and does not account for complex real-world lighting factors such as shadows, glare, and non-uniform illumination.

The influence of imaging distance was systematically evaluated. Results indicate that 0.2 mm cracks can be reliably recognized when the imaging distance does not exceed 25.5 m. Based on camera imaging principles, the corresponding ground sampling distance was calculated to be approximately 1.29 mm per pixel. Given known camera parameters, the maximum permissible imaging distance for satisfying the 0.2 mm detection requirement can be inversely derived. The findings provide practical guidance for planning inspection flights and selecting appropriate imaging distances in bridge crack detection tasks subject to code-specified crack width limits.

This paper investigates the role of phase-based motion magnification in a computer-vision-based methodology for marker-free fatigue crack assessment introduced in earlier work. The approach uses dense optical flow and frequency-domain analysis to capture periodic crack-breathing motion. The resulting spatial “amplitude maps” highlight regions dominated by crack-induced cyclic displacement and enable crack visualisation. We present a focused case study examining how motion magnification influences the quality and interpretability of these amplitude maps and under which conditions its use is justified.

Experiments are conducted on a cantilever beam with a fatigue crack subjected to harmonic excitation at a known frequency. High-speed camera recordings are acquired across a range of excitation amplitudes, and motion magnification is applied as an optional pre-processing step. For each configuration, dense optical flow is computed and amplitude maps at the excitation frequency are formed using either the optical-flow magnitude or directional components. Maps obtained with and without magnification are compared in terms of crack-related pattern clarity and background artefacts.

The results show that motion magnification substantially enhances crack visibility when amplitude maps are derived from the optical-flow magnitude at low excitation amplitudes, extending the regime in which the crack-breathing pattern is discernible. In contrast, for directional optical-flow components, which already provide clear crack visualisation, magnification yields little or no benefit and may degrade map quality at larger displacements. The study demonstrates that motion magnification is a task- and regime-dependent option rather than a default stage in amplitude-map-based crack-assessment pipelines.

Existing video motion magnification methods commonly decompose video frames into multiple spatial frequency bands using pyramid representations, such as the phase-based magnification method and the fast Riesz-pyramid approach. These pyramid-based decompositions, in combination with Gabor or Hilbert transforms, extract the instantaneous phase of images across different orientations. Motion magnification is then achieved by amplifying the phase variations between consecutive frames, followed by image reconstruction. However, due to the inherent limitations of the Gabor and Hilbert transforms, these approaches can only effectively capture instantaneous phase information in low- and mid-frequency ranges, while high-frequency components are not synchronously magnified. This mismatch often results in reconstruction artifacts.

To overcome this limitation, this paper proposes a wavelet-based video motion magnification framework. The proposed method employs a spatial Butterworth filter to decompose video frames into low-, mid-, and high-frequency bands, and leverages the wavelet transform to extract high-frequency phase information for synchronous magnification. By enhancing the representation of high-frequency components, the method effectively suppresses artifacts and improves visual fidelity. Experimental results demonstrate that the proposed approach achieves artifact-free motion magnification with superior accuracy and visual quality compared to existing methods.