# Image-Based Structural Health Monitoring: A Systematic Review
## 4 Discussion
### Purpose and Applications of Image-Based Structural Health Monitoring
#### 4.1.4 To Promote Development, and Create 3D Models  
Safety around dams has long been a major social concern [60]. Dams that are not appropriately monitored or maintained will have a substantial impact on the security of private property and natural settings. Dam health monitoring systems are therefore essential for spotting anomalous behavior and eliminating or lessening its effects. For the non-contact distant measurement of structural reactions, camera and CV-based sensors have shown promise. Due to improvements in camera resolution and processing power, vision-based technologies have become recognized as an effective method for SHM. Crack identification on the pixel level is possible using a number of techniques. CNN-based frameworks provide quick and precise detection of infrastructure cracks. These algorithms are intended to estimate local damage; however, their small-scale features are insufficient for overall dam evaluation. In order to assess damage and undertake non-contact, optical-based measurements for disaster prevention, an image-based rendering approach has been developed for the construction of three-dimensional (3D) models of the structures. A three-dimensional (3D) reconstruction approach can be used for dam crisis monitoring and inspection based on unmanned aerial vehicle (UAV) photos [60]. By fitting the characteristics in numerous overlapping photos, the structure from the motion approach is used to build a high-precision 3D dam model with scene geometry. The bundle correction algorithm then uses the resulting model to generate 3D point positions and camera posture. Various position choices for adjusting to various conditions investigate the impact of ground control points on model accuracy. The suggested emergency monitoring model was assessed on two rock-fill dams: (1) a concrete dam inspection model; and (2) a damage detection technique on a small-scale dam model with loadable panels [60]. According to the findings, the suggested 3D dam reconstruction model based on UAV photographs may reach enough accuracy, resulting in a considerable boost in dam observation and inspection efficacy. Fitting to the claims of [8], significant efforts have been made over the last few decades toward image-based SHM approaches. Monitoring structure displacement reactions can give quantitative data for structural safety and maintenance evaluations. They went on to say that connecting the pixel to the physical location is essential for deriving structural displacements from acquired video pictures. When the camera's optical axis is at right angles to the surface of an object, all points on its surface have equivalent depths of field, which means they may all be equally scaled down onto the picture plane with only single matching scaling factor required. Generally speaking, there are two methods that may be used to compute the scaling factor: (1) SF1 based on the known physical dimension of the object surface and its corresponding image dimension in pixels; or (2) SF2 based on the intrinsic parameters of the camera as well as the extrinsic parameters between the camera and the object structure. On its early phase, image-based SHM that uses sensors to measure displacement is cheaper than accelerometers installed in civil infrastructures. For instance, a CV-based method for bridges which is based on the bridge's influence line was proposed by [62] to estimate a moving vehicle's tire loads and the displacement response of the bridge. Their novel method utilizes two cameras focused to taking a video of the entire bridge, while the other takes a photo of the vehicles entering. The cameras are then calibrated against lens distortion, and to obtain real-world reference points. From here, a 2D image coordinates are mapped using the pinhole camera model available in the OpenCV library. The full procedure in using influence lines to perform image-based SHM was documented by [62]. This non-contact technique can be applied on monitoring not just the integrity of the bridge.