# Image-Based Structural Health Monitoring: A Systematic Review
## 4 Discussion
### Applications for Innovation
Using 2D image-based crack monitoring, preventative conservation of masonry structures at a cheap cost and in a non-invasive manner is achieved [93]. It is suggested that compared to other methods based on digital data collection and 3D photogrammetry is a good direction for innovation. In addition, once the database is set up, automation of crack detection as the basis for decision-making may be explored. The 2D image-based crack monitoring achieves the best resolution using the maximum telephoto lens. Meanwhile, the process of 3D displacement calculation using a motion capture system has three stages. First, structural coordinates must be defined using a wand practiced by [76]. Then, a motion capture system must be set up to be followed by calibration. From here, the structural coordinate system is transformed into x, y, and z directions for monitoring. MCS entails installing cameras, calibrating them, and attaching markers to a building [76]. Three-dimensional (3D) structural displacements are determined using a motion capture system (MCS) with high-level precision and sampling frequency. Since multiple cameras are used, capable of recording low and high-resolution images, big data is expected for the comparison of displacements which is the focus of monitoring. If a single building, of three stories, generates tons of information from recorded images and videos [76], a server to store and collect these is required. Among the potential big data management control is Hadoop- a widely used open-source software built for distributed data storage and processing [105; 106]. It is also feasible to decrease the amount of data before processing or gathering it. Compressive sensing [107], a modern technology brought to the research community, can minimize the volume of data transferred, stored, and processed during sampling. Recently, this methodology was applied to SHM datasets. Crack detection and segmentation, which is common from the set of literature included in this paper, employs a region convolutional neural network [108] for object detection and extends the faster RCNN framework's target detection capabilities by including an extra branch to achieve instance segmentation for each output proposal box via a fully connected (FC) layer [61]. The model is housed on a web server and is used to process picture data gathered and uploaded by the inspection robot [61]. The best feature for unmanned aerial vehicle developments is to replace human inspectors in conducting life-threatening onsite civil structure inspections. Since most areas may be difficult to access, a wireless connection shall be practiced. Improving the extent of wireless control is suggested. Completely contactless using cameras and computer vision [10] offers the most crucial information of measurement in SHM. Traditional data-driven SHM algorithms are less suitable for large structures and may not be ideal for vision-based SHM applications [1]. In order to address this issue, deep learning-based SHM techniques attempt to create completely automated extraction of features and hierarchical representation processes from the raw input data using stacked blocks of CNN layers with nonlinear mappings [6; 102]. Deep learning is one of the many new machines learning algorithms. Adopted from the works of [1; 103], Figure 4 shows a concept map for machine learning applications on big data analysis in image-based SHM applicable for the following concerns: (1) crack monitoring and inspection; (2) displacement measurement; (3) concrete damage detection and quantification; (4) condition assessment [109; 110]; (5) automation; (6) contactless application; (7) defect recognition and sizing; (8) cost-effectiveness; (9) image quality enhancement; and (10) material degradation.