## 3 Results
### Studies Implementing Vision Systems (VSS)
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Author** & **Target** & **SHM Category** & **Study Objectives** & **Methods** & **Camera Features** & **Test Scenarios** & **Main Results** \\ \hline  & & & & Framework to & & & & \\  & & & estimate full-scale & & & & \\  & & & civil infrastructure & & & & \\  & & vibration frequencies & & Single-camera: & & & \\  & & from UAV recorded & & & & & \\  & & videos using & & & & & \\  & & automatic detection & & experimental test), & & & \\  & & & of regions of interest, & & camera 3840 \(\times\) & (indoor) & \\  & 2019 [75] & & & KLT to track motion, & 2160 px, 30 fps (for & In-field: & \\  & & & high pass filtering to & in-field test), & & \\  & & remove effects of & & both mounted on a & suspension & \\  & & & hovering, and & UAV. & bridge & \\  & & & marker shapes to & & (outdoor) & 1.6\% and MAC \(>\) 0.928. \\  & & & compensate for UAV & & & \\  & & & rotations. & & & \\ \hline  & & & & & & \\  & & & & & & \\  & & & & & & \\  & & Targetless & & & & \\  & & vision-based solution & & & & minimum (2.2\%) and \\  & & for measuring & & Single-camera: & Experimental: & \\  & & dynamic vibrations & & Sony PXW-F5 4K & reinforced & \\  & & estimation & due to bidirectional & & XDCAM (1920 \(\times\) & concrete & \\  & & low-level excitations & & 1080 px, 50 fps) & column model & \\  & 2019 [76] & & & using a & & mounted on a & and shaking \\  & & & consumer-grade & tripod 4 m away. & tables (indoor) & \\  & & & camera to derive & & & \\  & & displacements. & & & & \\  & & & & & \\ \hline  & & & A vision-based & & & \\  & & solution to estimate & & & & \\  & & lateral and vertical & & & \\  & & displacements of & & High Speed Ximea & Experimental: & \\  & & estimation & using a high-speed & LEDs & MQ042 MG-C (2048 & bridge model & \\  & 2018 [77] & & placements) and active & & \(\times\) 1024 px, 180 fps & (indoor) & frequencies in lateral \\  & & & LEDs to cope with & & with lens. & \\  & & & insufficient incident & & & \\ \hline  & & & A non-invasive & & & \\  & & method for & & & \\  & & measuring cracks in & & & & \\  & & the cultural heritage & & & \\  & & sector, taking care of & & & \\  & & the aesthetic & & & \\  & & appearance, using & & & \\  & & small adhesive labels & & Single camera: & \\  & & and a high-resolution & & \\  & 2018 [78] & & estimation & & \\  & 2018 [78] & heritage & & & \\  & & estimation & & & \\  & & their relative distance & & & \\  & & through advanced & & away from the tags. & (indoor) & \\  & & least-squares fitting & & & \\  & & of quadratic curves & & & \\  & & and surface & & & \\  & algorithms for the & & & \\  & & Gaussian objective & & & \\  & function. & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: _Cont._  
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Author** & **Target** & **SHM Category** & **Study Objectives** & **Methods** & **Camera Features** & **Test Scenarios** & **Main Results** \\ \hline \multirow{9}{*}{[PERSON] et al., 2018 [79]} & \multirow{9}{*}{Walls} & Damage detection (cracks on surface) & System based on an autonomous UAV & & & & & Cracks in concrete surfaces were detected with high accuracy \\  & & & geo-tagging method for structural damage & CV and DL (pre- (2304 \(\times\) 1296 px & \begin{tabular}{c}In-field: \\ cracks on real \\ \end{tabular}  & \begin{tabular}{c}In-field: \\ cracks on real \\ \end{tabular}  & \begin{tabular}{c}(96.6\%), sensitivity \\ (91.9\%), and specificity \\ (97.9\%) using video \\ data collected by the autonomous UAV and the previously \\ trained deep \\ convolutional neural \\ network. \\ \hline \multirow{9}{*}{[PERSON] et al., 2018 [80]} & \multirow{9}{*}{Containment} & \multirow{9}{*}{ \begin{tabular}{c} Damage detection \\ (cracks on surface) \\ \end{tabular}  } & \multirow{9}{*}{ \begin{tabular}{c} Multi-camera: \\ two Canon EOS 5D \\ concrete surfaces, \\ using image optical \\ flow from a stereo \\ system and subpixel \\ information to \\ analyze slight \\ displacements on the \\ concrete surface. \\ \end{tabular}