The displacement response of structures is an important parameter in structural health monitoring (SHM). Displacement responses can be applied in both structural performance monitoring and structural dynamic characteristics monitoring. To overcome the shortcomings of traditional contact sensors, a vision-based multi-point structural displacement measurement system equipped with an inexpensive surveillance camera and a consumer camera was developed herein. In addition, to reduce the computing time of target tracking, an improved region-matching algorithm based on the prior knowledge of structural deformation was proposed. Numerical results revealed that the improved region-matching algorithm could save computing time without reducing location accuracy. Moreover, static and dynamic loading tests were conducted on a scale model of a suspension bridge to validate the effectiveness of the proposed vision-based measurement system. Displacement responses and modal parameters obtained from the vision-based measurement system were compared with those of traditional contact sensors, and a satisfactory consistency was obtained. Hence, the proposed vision-based measurement system could be a cost-effective alternative to conventional displacement sensors and accelerometers for SHM.

Computer vision is becoming one of the most popular remote-sensing techniques and has been used widely in displacement monitoring and damage identification of in-service bridges. Nevertheless, several obstacles, including limited sampling rate, insufficient resolution for remote measurement, and error induced by camera tilting, restrict the application of vision-based approaches in structural health monitoring (SHM). The combination of a traditional SHM system and a modern remote-sensing technique can significantly improve the accuracy and reliability of the monitoring system. To make full use of data collected in the traditional SHM system and computer vision technique and overcome their shortcomings, we presented an improved bridge displacement estimation approach for SHM purposes by fusing camera-based and acceleration measurements. First, we estimated the scaling factor, which transfers pixel displacement to real displacement under tilt photogrammetry, by the acceleration reconstructed and camera-based displacements in the same frequency band without the actual size of the structure or the measurement parameters. Then, we extracted the low-frequency displacement from the vision-based measurement, and we fused the high-frequency displacement that was reconstructed from the acceleration measurement to achieve high-accuracy displacement estimation. The efficiency of this method was validated through dynamic load tests on a suspension model bridge in the laboratory and field tests on a highway and subway cable-stayed bridge.

In light of the challenges posed by intricate algorithms, subpar recognition accuracy, and prolonged recognition duration in current machine vision for bridge structure monitoring, this paper presents an innovative method for recognizing and extracting structural edges based on the Gaussian difference method. Initially, grayscale processing enhances the image’s information content. Subsequently, a Region of Interest (ROI) is identified to streamline further processing steps. Following this, Gaussian check images at different scales are processed, capitalizing on the observation that edges show reduced correspondence to the Gaussian kernel. Then, the structure image’s edges are derived using the difference algorithm. Lastly, employing the scale factor, the algorithm translates the detected edge displacement within the image into the precise physical displacement of the structure. This method enables continuous monitoring of the structure and facilitates the assessment of its safety status. The experimental results affirm that the proposed algorithm adeptly identifies and extracts the structural edge’s geometric characteristics with precision. Furthermore, the displacement information derived from the scale factor closely aligns with the actual displacement, validating the algorithm’s effectiveness.