# Image-Based Structural Health Monitoring: A Systematic Review
## 4 Discussion
### Applications for Innovation  
A machine learning-based SHM model, created to learn on its own, combines technical developments in sensors with regard to data collecting and battery life, high-speed internet, and cloud computing [92]. There are three types of ML-based SHM models: supervised, unsupervised, and improved learning [1]. Using a training set of data, supervised learning may be used to train an ML model. The model is categorized as classification if the algorithm's outputs are autonomous or categorical data, and there is no way to train the dataset for unsupervised learning. The 2D image-based crack monitoring approach was discovered to be suitable, affordable, traceable, and mindful of the building's usage [93]. When used to correct indoor flaws in historically significant structures with minimal social projection, 2D image-based technology is precise and affordable. To provide a framework for storing and displaying information about the built environment, ref. [94] studied the potential of combining image-based documentation and augmented reality. Efficiency is therefore guaranteed both on and off-site. To determine whether the building has sensors and to locate them, a tablet or perhaps other portable electronic devices may be used. Additionally, a user will be able to interact with the AR comments via on-click circumstances to display pertinent facts and/or information. By utilizing the advantages of augmented reality, a user may easily examine and update information about a structure while on-site. Image-based SHM employs a method for damage detection and diagnosis that includes data from a system collected in various structural states. Image-based SHM is a modern technology that has improved over the past decades; however, there is always room for improvement. As a result, delays in processing, transferring, and archiving data must be shortened. In addition, accuracy must be tested to perceive human-like judgment and acceptance. The Internet is a major concern for most of the systems mentioned above. An uninterrupted connection must be achieved by future researchers and if not prevented, one must create a built-in function that stores and process the data internally for a given time. Only when the connection is booted-up, up will the system send again all necessary information to its users, or data center. The succeeding segments adhere to the additional innovative application for image-based SHM. Digital Camera Detection SHM  
The DCM SHM is an affordable image-based SHM. This can be used like a normal digital camera. This uses 2D crack monitoring and big data processing for storage and higher resolution. It can detect the structural health of a building by taking a picture of the infrastructure. It has a wide lens camera for bigger buildings and with a push of a button, you can see the structural condition of the infrastructure. Eveglass Augmented Reality SHM  
The EAR SHM uses an eyeglass with a microchip that contains big data for scanning processing thus, it can also take a photo for longer inspection. Using the EAR, we can see the real-time damage or condition of infrastructure. This has Bluetooth for transferring files and Wi-Fi for easy transfer. The flashlight to image SHM is like a flashlight, wherein the device can scan part-by-part of the structure; this has Wi-Fi for easy transfer to a laptop. The laptop has a machine learning system that can easily read the scanned parts and turn them into an image. With the aid of cutting-edge algorithms, inverse dynamics theories, and actual infrastructure observations, new advancements in sensor and information technology enable the interpretation of structural state. The main approach in conducting structural evaluation and local material sampling frameworks has been through performance evaluation methods [95]. However, not everyone can perform multiple tests and experiments due tolimited access to equipment, and resources. When it comes to health monitoring, numerical investigation, and data fusion are often utilized in the creation and assessment.