Bridge inspection and monitoring are usually used to evaluate the status and integrity of bridge structures to ensure their safety and reliability. Computer vision (CV)-based methods have the advantages of being low cost, simple to operate, remote, and non-contact, and have been widely used in bridge inspection and monitoring in recent years. Therefore, this paper reviews three significant aspects of CV-based methods, including surface defect detection, vibration measurement, and vehicle parameter identification. Firstly, the general procedure for CV-based surface defect detection is introduced, and its application for the detection of cracks, concrete spalling, steel corrosion, and multi-defects is reviewed, followed by the robot platforms for surface defect detection. Secondly, the basic principle of CV-based vibration measurement is introduced, followed by the application of displacement measurement, modal identification, and damage identification. Finally, the CV-based vehicle parameter identification methods are introduced and their application for the identification of temporal and spatial parameters, weight parameters, and multi-parameters are summarized. This comprehensive literature review aims to provide guidance for selecting appropriate CV-based methods for bridge inspection and monitoring.

Most research on computer vision (CV)-based vibration measurement is limited to the determination of discrete or coarse mode shapes of the structure. The continuous edge of the structure in images has rich optical features, and thus, by identifying and tracking the movement of the structure’s edge, it is possible to determine high-resolution full-field mode shapes of the structure without a preset target. The present study proposes a CV-based method of full-field mode shape identification using the subpixel edge detection and tracking techniques. Firstly, the Canny operator is applied on each frame of the structure vibration video to extract the pixel-level edges, and the improved Zernike orthogonal moment (ZOM) subpixel edge detection technique is adopted to relocate the precise structure edges. Then, all the detected edge points are tracked to obtain the full-field dense displacement time history that is subsequently used to determine the structure frequencies and compute full-field mode shapes by combining the covariance driven stochastic subspace identification (SSI-COV) with the hierarchical cluster analysis. Finally, the proposed method is verified on the aluminum cantilever beam in the laboratory and the Humen Bridge in the field. The results show that the proposed method is able to detect more precise structure edges and identify the full-field displacement and mode shapes of structures without the need for installing artificial targets on the structure in advance, which provides valuable information for the structural condition assessment, especially for structures with small-amplitude vibrations.