## 5 Results and Discussion
### Results of Training and Validation
#### 5.1.2. AP50:95 Level Analysis  
Tables 5 and 6 display the AP50:95 results for the different backbone architectures in the Faster R-CNN (ResNet 101, ResNet 152, and MobileNetV3) and YOLOv10 (ViT, Swin Transformer, and PVT). Meanwhile, Figure 17 compares each augmentation's AP50 performance to that of the unaugmented baseline. Within the Faster R-CNN, the original baseline average of 59.97 (AP50:95) increased to 62.84 using contrast augmentation--a +2.87 gain--followed by shearing at 61.76 (+1.79). Larger geometric transformations, such as scale and translation, generally yielded lower \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 & 62.45 & 57.89 & 60.12 & 60.15 \\ Brightness & 58.12 & 54.67 & 56.23 & 56.34 \\ Contrast & 65.67 & 63.78 & 67.12 & 65.52 \\ Perspective & 59.45 & 55.89 & 61.56 & 58.97 \\ Rotation & 60.92 & 55.12 & 58.34 & 58.13 \\ Scale & 56.78 & 54.01 & 63.6 & 58.13 \\ Shearing & 63.89 & 62.67 & 69.34 & 65.3 \\ Translation & 55.34 & 53.78 & 57.56 & 55.56 \\ Sum of techniques & 57.67 & 60.12 & 56.23 & 58.01 \\ Average & 60.03 & 57.55 & 61.12 & 59.57 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of AP50:95 results for instance segmentation (YOLOv10). \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 & 84.23 & 79.56 & 82.45 & 82.08 \\ Brightness & 80.56 & 76.78 & 79.34 & 78.89 \\ Contrast & 86.78 & 84.12 & 87.23 & 86.04 \\ Perspective & 81.45 & 78.34 & 83.56 & 81.12 \\ Rotation & 82.98 & 77.23 & 80.76 & 80.32 \\ Scale & 79.45 & 76.12 & 85.67 & 80.41 \\ Shearing & 83.56 & 82.78 & 88.45 & 84.93 \\ Translation & 78.23 & 76.45 & 81.34 & 78.67 \\ Sum of techniques & 80.98 & 83.12 & 79.78 & 81.29 \\ Average & 82.02 & 79.39 & 83.18 & 81.53 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of AP50 results for instance segmentation (YOLOv10). \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 & 61.32 & 58.74 & 59.85 & 59.97 \\ Brightness & 59.5 & 55.21 & 57.58 & 57.43 \\ Contrast & 64.12 & 60.8 & 63.6 & 62.84 \\ Perspective & 58.32 & 55.69 & 60.03 & 58.01 \\ Rotation & 59.01 & 55.52 & 57.83 & 57.45 \\ Scale & 56.91 & 53.15 & 61.39 & 57.15 \\ Shearing & 60.24 & 59.9 & 65.13 & 61.76 \\ Translation & 55.02 & 53.78 & 58.8 & 55.87 \\ Sum of techniques & 57.84 & 62.23 & 56.6 & 58.89 \\ Average & 59.14 & 57.22 & 60.09 & 58.81 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of AP50:95 results for object detection (Faster R-CNN). scores, dropping the average to 57.15 and 55.87, respectively. Although combining all augmentations (sum of all) boosted certain backbones (e.g., ResNet 152 to 62.23), it did not exceed contrast augmentation's peak improvements. For YOLOv10, the baseline was 60.15, with contrast augmentation raising it to 65.52 (+5.37) and shearing augmentation close behind at 65.30 (+5.15). In contrast, combining all augmentations benefitted some configurations (e.g., ResNet 152 in the Faster R-CNN climbed to 62.23), but typically failed to surpass the single best augmentation, and in certain backbones, it even dipped below the baseline. Regarding backbone architectures, deeper or transformer-based models (e.g., ResNet 101 and PVT) generally achieved higher initial accuracy and handled moderate augmentations more effectively, leveraging enhanced edge clarity or luminance contrasts. Lighter networks (e.g., MobileNetV3 and Swin Transformer), on the other hand, can be more vulnerable to extensive geometric shifts, which sometimes undermine the benefits of otherwise helpful methods.