# Image-Based Structural Health Monitoring: A Systematic Review
## 5 Conclusions  
Image-based SHM is a technological breakthrough designed to overcome the current uncertainties in civil engineering and construction. It is a multifaceted and complicated system. Prior studies were compiled to give a more thorough knowledge, appreciation, and application of Image-based SHM technologies.  
In this paper, the following was discovered:  
* Devices and systems are not commercially available because there is no single absolute method for Image-based SHM. One must design their own that is tailored to their specific needs. These needs include, but are not limited to: the monitoring of displacement of structures and their elements; the automation of crack detection; the identification of medium and high levels of corrosion; the discovery of subsurface damage; the promotion of development; the measurement of strain and temperature changes; increasing efficiency; the creation of 2D and 3D models; the development of complete contactless approaches and methods through machine learning and autonomous systems; and the development of cost-competitive alternatives.
* Image acquisition is carried out using: IP cameras connected to a computer on an ethernet switch; industrial and stereo cameras equipped with a light source for better image quality; drones with cameras controlled by a transmitter; and remote-controlled robots with stereo and monocular cameras.  
Figure 5: A method through which RFBNs function.  
* In image-based SHM, the collection, treatment, and storage of data vary. DIC, a computer vision tool, is well-known for: its ability to disclose deformation patterns on structures; analyzing deflection produced by static loads; measuring the seismic response of structures; and comparing external displacement fields between damaged and non-damaged circumstances.
* Deep and multiple damages can be detected using faster R-CNN, end-to-end defect detection network, guided wave imaging, and convolutional neural network.
* Unmanned aerial vehicles can be used to perform in-construction and in-service condition monitoring, allowing for life-cycle monitoring and the assessment of the structure.
* We presented the typical challenges in image-based SHM that future researchers and implementers may face. The solutions for these issues and the concept map for machine learning applications on big data analysis in image-based SHM were highlighted in Sections 4.4 and 4.5 of this study.
* In Section 4.6 of this work, the answer to the question: \"How should image-based SHM be implemented, and what are the requirements for success?\" is given.  
Conceptualization, J.M.G.P. and D.-K.K.; methodology, J.M.G.P. and D.-K.K.; software, J.M.G.P. and D.-K.K.; validation, J.M.G.P. and D.-K.K.; formal analysis, [PERSON] and D.-K.K.; resources, [PERSON] and D.-K.K.; writing--original draft preparation, [PERSON] and D.-K.K.; writing--review and editing, [PERSON] and D.-K.K.; visualization, [PERSON] and D.-K.K.; supervision, [PERSON]-K.K.; funding acquisition, D.-K.K. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Basic Science Research Program through the National Research Foundation (NRF) funded by the Korea Ministry of Education (No. 2016R1A6A1A03012812).  
Not applicable.  
The work of doctoral student [PERSON] has been fully supported by the Education and Research Center for ICT Integrated Safe Ocean Smart Cities (I-SOC) at Dong-A University supported by the Korean Ministry of Education (September 2020-August 2027).  
The authors declare no conflict of interest.