## 5 Results and Discussion
### Comparison with Other Detectors  
Several deep learning methods exist for building facade analysis, as noted by multiple studies [36; 37; 38; 39]. In this research, contrast augmentation emerged as the most effective technique, and several robust detectors were tested. Specifically, R-FCN and YOLOv5 were employed for object detection, while Mask R-CNN and YOLACT++--using various backbones--were evaluated for instance segmentation (Table 10). Among these, YOLOv5 with CSPDarkNet53 excelled at object detection, achieving an AP50 of 87.23 and FPS of 37.5, surpassing the Faster R-CNN and R-FCN. For instance segmentation, YOLOv10 with PVT produced the highest accuracy, reaching an AP50 of 87.23, AP50:95 of 67.12, IoU of 82.45, and an FPS of 33.7. Although deeper backbones generally offer higher precision, they often reduce speed; conversely, lighter networks maintain a higher FPS with modest accuracy trade-offs. Finally, transformer-based architectures, including ViT and PVT, demonstrated a high accuracy while sustaining competitive speeds, making them particularly useful for advanced building facade detection tasks.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Task** & **Detector** & **Architecture** & **AP50** & **AP50:95** & **IOU** & **FPS** \\ \hline \multirow{8}{*}{Object detection} & Faster & ResNet-101 & 85.49 & 62.33 & 78.56 & 28.3 \\  & R-CNN & ResNet-152 & 81.56 & 60.8 & 75.67 & 27.8 \\  & R-CNN & MobileNetv3 & 84.66 & 63.6 & 77.12 & 31.5 \\  & & ResNet-101 & 84.12 & 62.3 & 77.21 & 29.7 \\  & R-FCN & ResNet-152 & 80.56 & 59.4 & 74.89 & 28.7 \\  & & MobileNetv3 & 82.78 & 61.2 & 76.1 & 32.1 \\  & & ResNet-101 & 84.12 & 62.3 & 77.21 & 29.3 \\  & & CSPDarkNet53 & 87.23 & 66.12 & 81.02 & 37.5 \\  & & MobileNetv3 & 85.6 & 63.9 & 78.89 & 39.4 \\  & & Swin Transformer & 86.12 & 64.76 & 79.45 & 35.8 \\  & & ResNet-50 & 83.9 & 61.3 & 75.67 & 15.4 \\  & Mask & ResNet-101 & 85.25 & 62.78 & 77.1 & 14.2 \\  & R-CNN & ResNeXt-101 & 86 & 63.6 & 78.2 & 12.5 \\ Instance & & ResNet-50 & 82.78 & 60.1 & 74.67 & 22.3 \\ segmentation & YOLACT++ & ResNet-101 & 84.12 & 61.5 & 76.01 & 18.5 \\  & & ResNeXt-101 & 85.45 & 62.9 & 77.8 & 15.3 \\  & & ViT & 86.78 & 65.67 & 80.56 & 31.3 \\  & YOLOv10 & Swin Transformer & 84.12 & 63.78 & 78.21 & 32.5 \\  & & PVT & 87.23 & 67.12 & 82.45 & 33.7 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Results of applying the trained model to a test set.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Model** & **Augmentation** & \begin{tabular}{c} **Mean Detection** \\ **Score** \\ \end{tabular} & **Coherence** \\ \hline FasterRCNN\_ResNet101 & \begin{tabular}{c} Contrast \\ Shearing \\ \end{tabular} & 77.18 & 64.12 \\ FasterRCNN\_ResNet152 & \begin{tabular}{c} Contrast \\ Shearing \\ \end{tabular} & 80.22 & 62.53 \\ FasterRCNN\_MobileNetV3 & \begin{tabular}{c} Contrast \\ Shearing \\ \end{tabular} & 81.58 & 69.47 \\ YOLOv10\_ViT & \begin{tabular}{c} Contrast \\ Shearing \\ \end{tabular} & 81.67 & 68.41 \\ YOLOv10\_PVT & \begin{tabular}{c} Contrast \\ Shearing \\ \end{tabular} & 84.55 & 74.03 \\ YOLOv10\_Swin & \begin{tabular}{c} Contrast \\ Shearing \\ \end{tabular} & 85.12 & 77.65 \\ YOLOv10\_Swin & \
\begin{tabular}{c} Contrast \\ Shearing \\ \end{tabular} & 83.8 & 73.14 \\ Transformer &
\begin{tabular}{c} Shearing \\ \end{tabular} & 82.91 & 69.78 \\ \hline \hline \end{tabular}
\end{table}
Table 9: Grad-CAM analysis results: mean detection score and coherence.