## 5 Results and Discussion
### Impact of Augmentation on Class-Specific Level: AP50:95  
Table 5 presents the class-specific AP50:95 outcomes associated with various augmentation strategies in non-PPE detection tasks. Several notable patterns emerge. First, non-helmet detection remains consistently lower than that of other classes (e.g., non-mask or non-shoes) across ViT, Swin Transformer, and PVT, indicating that helmet recognition poses a greater challenge than identifying other PPE forms. Second, brightness augmentation yields the most pronounced gains in both the average AP50:95 and per-class performance. For instance, PVT improves from 38.47 to 40.82 with brightness, while ViT rises from 37.25 to 39.13. Third, scale augmentation also proves effective, often ranking second to brightness in boosting detection--especially for non-shoes, where variations in size and perspective are significant. In contrast, translation and perspective augmentations offer more modest improvements, possibly because minor positional or viewpoint shifts do not fully address key differences in color or scale. Finally, combining all techniques consistently produces the highest mean scores, suggesting that aggregating multiple transformations offers a more comprehensive simulation of real-world diversity and thus strengthens model generalization. Overall, these findings underscore the nuanced role of augmentation in refining class-specific detection accuracy, while also highlighting the particular difficulty of reliably detecting helmets.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Techniques**} & \multicolumn{6}{c}{**ViT**} \\ \cline{2-7}  & **Non-Helmet** & **Non-Mask** & **Non-Glove** & **Non-Vest** & **Non-Shoes** & **Average** \\ \hline Original & 35.84 & 37.52 & 37.12 & 37.73 & 38.04 & 37.25 \\ Brightness & 37.92 & 39.54 & 39.23 & 39.66 & 39.30 & 39.13 \\ Contrast & 36.77 & 38.52 & 38.21 & 38.65 & 38.75 & 38.18 \\ Perspective & 36.01 & 37.64 & 37.32 & 37.82 & 38.31 & 37.42 \\ Rotation & 36.19 & 37.84 & 37.46 & 37.97 & 38.49 & 37.59 \\ Scale & 37.05 & 38.72 & 38.40 & 39.01 & 39.07 & 38.45 \\ Shearing & 36.88 & 38.54 & 38.16 & 38.63 & 38.94 & 38.23 \\ Translation & 35.82 & 37.41 & 37.06 & 37.62 & 38.29 & 37.24 \\ A sum of techniques & 38.24 & 39.77 & 39.43 & 40.18 & 40.13 & 39.55 \\ Average & 36.75 & 38.39 & 38.04 & 38.59 & 38.81 & 38.12 \\ \hline \multicolumn{7}{c}{Swin Transformer} \\ \cline{2-7}  & Non-Helmet & Non-Mask & Non-Glove & Non-Vest & Non-Shoes & Average \\ \hline Original & 36.65 & 38.47 & 38.12 & 38.58 & 39.00 & 38.16 \\ Brightness & 38.93 & 40.89 & 40.57 & 40.92 & 40.84 & 40.43 \\ Contrast & 37.62 & 39.41 & 39.04 & 39.50 & 40.03 & 39.12 \\ Perspective & 36.79 & 38.61 & 38.25 & 38.71 & 39.04 & 38.28 \\ Rotation & 36.86 & 38.71 & 38.36 & 38.82 & 39.05 & 38.36 \\ Scale & 37.84 & 39.72 & 39.48 & 39.86 & 39.85 & 39.35 \\ Shearing & 37.54 & 39.23 & 38.92 & 39.40 & 40.01 & 39.02 \\ Translation & 36.78 & 38.61 & 38.24 & 38.77 & 38.95 & 38.27 \\ A sum of techniques & 39.22 & 41.21 & 40.85 & 40.99 & 41.33 & 40.72 \\ Average & 37.58 & 39.43 & 39.09 & 39.51 & 39.79 & 39.08 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Class-wise performance of each augmentation technique with different architectures (AP50:95).