## 5 Results and Discussion
### Results of Training and Validation
#### 5.1.3 IOU Level Analysis  
Tables 7 and 8 present the AP50 results for the different backbone architectures in the Faster R-CNN (ResNet 101, ResNet 152, and MobileNetV3) and in YOLOv10 (ViT, Swin Transformer, and PVT). Meanwhile, Figure 18 compares each augmentation's IoU performance against the original baseline. Examining these outcomes across both object detection ( Faster R-CNN) and instance segmentation (YOLOv10) revealed clear patterns in how each technique affects model performance. In the Faster R-CNN, the original (unaugmented) baseline achieved an average IOU of 74.67. Contrast augmentation boosted this figure to 77.12--a gain of approximately +2.45--and shearing augmentation produced a similar  
Figure 17: Difference between each technique and baseline (AP50:95).  
increase, reaching 77.23 (+2.56). Conversely, using the sum of all transformations resulted in a value of 74.30, which is \(-\)0.37 relative to the original, indicating that indiscriminately combining every augmentation can dilute the effectiveness of top-performing methods like contrast or shearing augmentation.  
A parallel trend emerged for YOLOv10, whose baseline was 75.82. Here, contrast augmentation again provided the largest improvement, reaching 80.41--a +4.59 rise--while shearing augmentation followed at 79.56 (+3.74). In contrast, the \"sum of all\" approach, at 75.12, was \(-\)0.70 relative to the baseline, further demonstrating that merging every augmentation can be counterproductive. Moreover, translation and scale augmentation typically produced the steepest drops, notably reducing the Faster R-CNN value to 71.75 and YOLOv10 value to 72.20 using translation augmentation, thus emphasizing that large geometric shifts can misalign key features in building images.  
Collectively, applying all augmentations at once did not outperform the best single augmentation. Deeper or transformer-based backbones (e.g., ResNet 152 and PVT) tend to benefit more from effective augmentations, thanks to their robust feature extraction capabilities, whereas lighter backbones (e.g., MobileNetV3) can be more sensitive to aggressive transformations. Crucially, the same augmentations were most effective in both bounding-box detection (Faster R-CNN) and pixel-wise segmentation (YOLOv10), suggesting that sharpening building edges and structural details--particularly via contrast augmentation--plays a pivotal role in enhancing model performance across different prediction paradigms.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Technique**} & \multicolumn{4}{c}{**Faster R-CNN**} \\ \cline{2-5}  & **ResNet-101** & **ResNet-152** & **MobileNetV3** & **Average** \\ \hline Original & 76.32 & 73.12 & 74.56 & 74.67 \\ Brightness & 72.45 & 70.32 & 71.12 & 71.30 \\ Contrast & 78.56 & 75.67 & 77.12 & 77.12 \\ Perspective & 74.12 & 72.56 & 75.34 & 74.01 \\ Rotation & 75.67 & 71.45 & 73.29 & 73.47 \\ Scale & 71.89 & 69.45 & 76.45 & 72.60 \\ Shearing & 77.12 & 75.89 & 78.67 & 77.23 \\ Translation & 70.56 & 71.23 & 73.45 & 71.75 \\ Sum of techniques & 73.90 & 76.12 & 72.89 & 74.30 \\ Average & 74.51 & 72.87 & 74.77 & 74.05 \\ \hline \hline \end{tabular}
\end{table}
Table 7: Comparison of IOU results for object detection (Faster R-CNN).  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multirow{2}{*}{**Technique**} & \multicolumn{4}{c}{**YOLOv10**} \\ \cline{2-5}  & **ViT** & **Swin Transformer** & **PVT** & **Average** \\ \hline Original & 78.12 & 73.45 & 75.89 & 75.82 \\ Brightness & 74.23 & 71.12 & 72.90 & 72.75 \\ Contrast & 80.56 & 78.21 & 82.45 & 80.41 \\ Perspective & 75.89 & 72.56 & 77.12 & 75.19 \\ Rotation & 77.34 & 72.45 & 74.89 & 74.89 \\ Scale