## 6 Results and Discussion  
In this study, analyses and comparison of all three approaches were conducted [38]. Starting with the Cascade Classifier, the approach successfully detects roof damages at both Martin Hall and Argo Hall, which are the two UWF buildings analyzed in this study. For the analysis, the average detection rate and the average number of false-positive detections are calculated. Figure 5 depicts example results for this approach. Table 2 summarizes the average damage detection rate and average number of false-positive detections using the CC approach. Additionally, the precision and recall values are calculated. The precision is calculated as the number of true positives over the number of true positives and false positives. It is a measure of how valid the predictions made by the model are. False positives reduce the precision of the model. The recall is calculated as the number of true positives over the number of true positives and false negatives. It is a measure of the completeness of the model predictions, so false negatives reduce the recall value. In general, a precision-recall curve is created with different parameters (thresholds) for a single frame, and it allows a common base to compare the different algorithms. With changing threshold, it is expected to have decreased the precision while the recall is increasing. In this study, however, the best parameters are kept fixed, and the precision and recall plot with results from consecutive frames of the video footage is created. The precision-recall variation plots are depicted in Figures 6 and 7, for the Argo Hall video and the Martin Hall video, respectively. The expected results from the precision-recall plots are that the value should be close to 1 in the precision as well as in the recall in most of the frames. positive detection, and there is only one false-positive detection in the entire series of frames in results for Argo Hall, for those selected sections. The next approach analyzed is CNN. Figure 8 depicts example results for this approach. The average detection rate and the average number of false-positive detection results are given in Table 4, and according to those results, the proposed CNN-based detection algorithm performs better than the CC-based approach. The small false-detection average also shows that the proposed algorithm is accurate. Additionally, the precision and recall variation plots, for both buildings, demonstrate a high level of performance. The same  
Figure 6: Precision-recall for Argo Hall using the Cascade Classifier. Figure 7: Precision-recall for Martin Hall using the Cascade Classifier. \begin{table}
\begin{tabular}{c c c} \hline \hline  & **Average Detection Rate** &
\begin{tabular}{c} **Average Number of** \\ **False-Positive Detections** \\ \end{tabular} \\ \hline
**Argo Hall** & 48\% & 0.06 \\
**Martin Hall** & 55\% & 0.00 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Damage detection with the Cascade Classifier—selected sections. as it was carried out for the CC, the selected frames with a minimum to no motion blur effect are also analyzed, and the average detection rate results for those parts are 85% and 88% for Argo Hall and Martin Hall, respectively (Table 5). In addition, in results for Argo Hall, there is no false-positive detection; and there is only one false-positive detection in the entire series of frames in results for Martin Hall, for those selected sections. The precision-recall plots are depicted in Figures 9 and 10, for Martin Hall video and Argo Hall video, respectively. The last approach analyzed in this paper is the VQA. Figures 11 and 12 depict a few example results of this approach. As discussed previously, the same corresponding selected frames obtained from two different videos were used. However, differently from previous approaches, in order to identify the damage instances inside each frame, a few different questions were posed to the model.