# Recent Advancements in Non-Destructive Testing Techniques for Structural Health Monitoring
## 6 Camera-Based Technique  
In recent years, the use of computer vision techniques increased in the civil engineering sector for structural health monitoring [14]. The primary use of this method is to monitor the displacement of structures in different environments. The system uses either one or more cameras, a zoom lens, and a computer to capture the collected data. The use of real-time processing software enables to measure displacement time histories and display results on the computer screen in real-time and automatically save data to the computer. Alternatively, the images can be captured, and data analysis can be performed at a later date [1].  
[PERSON] and [PERSON] [58] suggested a completely contactless SHM technique of real-life structures using cameras and computer vision. Their method was based on the fundamental camera principle shown in Figure 9. By knowing the distance between predefined points on the targeted structure in both the image and the world coordinate, a conversion ratio between pixels and engineering units was determined.  
A vision-based system for real-time displacement measurement of high-rise buildings was investigated by [PERSON] et al. [59] and [PERSON] and [PERSON] [60] using a multiple vision-based displacement measurement. The rust defect-recognition method based on color and texture feature, which combines the Fourier transform and color image processing, was proposed by [PERSON] et al. [59] for steel structures assessment. Automatic process and analysis of large volumes of images from different angles were studied as a vision-based technique for crack identification by [PERSON] and [PERSON] [61]. FFT algorithm combined with digital image correlation (DIC) data was recommended by [PERSON] et al. [62] in order to experimentally determine the damping ratio value in steel structures. Target-tracking DIC was used in [PERSON] and [PERSON] [63], who presented a large-scale laboratory application where DIC was utilized to monitor the response of a bridge structure and identify various modal properties, such as natural frequency, damping ratio, and mode shapes. [PERSON] et al. [64] proposed a novel vision-based displacement measurement by successive estimation of relative displacements and rotational angles throughout a large flexible structure. The technique was validated experimentally using two webcams installed on a flexible steel column structure. [PERSON] and [PERSON] [65] proposed a high-speed correlation system using Dantec Dynamics cameras to estimate the modal parameters of steel plates. They suggested an automated tool to conduct image processing directly from data format files. In order to overcome the limitations related to high-speed and high-resolution cameras, [PERSON] et al. [66] used consumer-grade cameras and combined them with three algorithms for structural system identification. Capturing images using the consumer-grade camera  
Figure 9: Camera principle [58].  
and combining them with a deep learning algorithm was examined by [PERSON] et al. [67] for crack detection in steel structures. The use of subpixel virtual visual sensors to obtain the modal shapes of structures and detect structural damage were experimentally studied by [PERSON] et al. [68].