# Image-Based Structural Health Monitoring: A Systematic Review
## 4 Discussion
### Roles and Importance of Components and Parameters
#### 4.3.3 Pilotless Operations  
The need for strong, autonomously managed SHM to improve the dependability of such buildings and their surroundings is growing as the demand to construct more skyscrapers and tall bridges in metropolitan areas increase across the world. An autonomous SHM system based on UAV has been investigated by [72]. To provide a comprehensive perspective of the building, the UAV's photographs of the construction site were pieced together. A well-known speeded-up robust feature (SURF)-based feature detection technique was used to complete the picture stitching process. SURF produces a huge amount of features, which are initially condensed using a random sample consensus approach. The photos are then aligned for final stitching once the appropriate changes have been done. A real-world, UAV-based, image-based SHM application on the structure's rear column may be utilized to determine displacement by comparing recent and old photographs of the structural location.  
Additionally, data collecting is the most challenging and important stage in any SHM system, especially in multiple measurement locations that are often at extremely high altitudes or in entirely inaccessible places, and which are frequently connected with time-consuming, expensive, and, to some extent, dangerous sensor installation and cable wiring. Despite major drawbacks, noncontact vision-based measurement methods have recently been seen as a mostly feasible choice and to address these constraints, ref. [73] recommended and tested an improved noncontact displacement measurement procedure that employed a UAV and CV algorithms in a small steel tower. The proposed system can address several issues with current vision-based techniques for locating a stationary place for the camcorder and reducing the inaccuracy brought on by the long-distance distance seen between the video camera and the measurement location because UAVs can transport cameras into hard-to-reach areas. The UAV, which was controlled by the schematic framework of the system, had a camera connected to it to film the measurement location. Then, displacements at that position were calculated using a key-point vision-based measuring approach. Additionally, the translations of the UAV were extracted from background references. An autonomous strategy based on Canny edge detection and Hough transform may be used to identify the components between the picture and engineering unit for each individual image frame, in order to address the problem of rapidly changing values of measurements [73]. Following the removal of the UAV movements from the displacement data, the real deformations of the measurement location will be measured.  
Finally, the concept of integrating unmanned aerial vehicles (UAVs) and image processing systems enables real-time monitoring of various types of [11]. UAVs such as drones are capable of monitoring structures on large scale, which allows full field mapping of large civil structures. The multi rotor UAV (MUAV) system is used for inspection because of its excellent maneuvering capabilities, and is equipped with color imaging sensors for image diagnosis algorithms. Various image-oriented inspection algorithms, such as the Hat transform approach and HSV thresholding, are also used for effective and rapid SHM. Crack detection algorithms rely on grayscale images by extracting dark objects from a light background. Hat transform is a process of extracting small elements and details from given images, and HSV thresholding uses color-based filters like HSV, which yields nearly optimal results in identifying the cracks. With that said, the integration of MUAV and image-oriented inspection algorithms is convenient and reliable for full-field SHM of various types of structures.