## 5 Results and Discussion
### Model Evaluation in Test Images  
The best-performing model at the AP50 level was PVT with brightness augmentation, achieving an average AP50 of 73.12 on the validation set. This approach was chosen because, in non-PPE contexts, AP50 is typically more crucial than AP50:95 for practical detection purposes. Nevertheless, this same configuration did not produce the highest AP50:95; indeed, PVT combined with all augmentations reached an AP50:95 of 40.92, exceeding the brightness-only variant. Figure 11 illustrates the class-specific performance of the best model, with Figure 11a (Validation) and Figure 11b (Test) showing closely aligned results and indicating no signs of overfitting.  
From the test set findings, non-helmet detection tends to yield lower accuracy in this study for several reasons. First, helmets are relatively small and often obscured by a worker's body position or other site elements, making them harder to detect or classify. Second, helmets vary in shape, size, and color more than some other PPE items (e.g., vests), reducing the model's ability to rely on consistent visual cues. Th  
Figure 11: Class-wise performance on validation and test set.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{Techniques} & \multicolumn{6}{c}{PVT} \\ \cline{2-7}  & Non-Helmet & Non-Mask & Non-Glove & Non-Vest & Non-Shoes & Average \\ \hline Original & 36.95 & 38.74 & 38.32 & 38.86 & 39.48 & 38.47 \\ Brightness & 39.29 & 41.31 & 41.07 & 41.26 & 41.17 & 40.82 \\ Contrast & 37.93 & 39.73 & 39.38 & 39.88 & 40.28 & 39.44 \\ Perspective & 37.07 & 38.88 & 38.55 & 38.96 & 39.44 & 38.58 \\ Rotation & 37.13 & 38.93 & 38.58 & 39.01 & 39.55 & 38.64 \\ Scale & 38.14 & 40.01 & 39.54 & 40.12 & 40.59 & 39.68 \\ Shearing & 37.76 & 39.58 & 39.14 & 39.66 & 40.21 & 39.27 \\ Translation & 37.05 & 38.84 & 38.43 & 38.95 & 39.48 & 38.55 \\ A sum of techniques & 39.39 & 41.28 & 40.96 & 41.49 & 41.48 & 40.92 \\ Average & 37.86 & 39.70 & 39.33 & 39.80 & 40.19 & 39.37 \\ \hline \hline \end{tabular}
\end{table}
Table 5: _Cont._be partially covered by shadows or other obstructions, especially in busy or poorly lit construction environments. Finally, the dataset contains fewer examples of non-helmet instances compared to other non-PPE categories, which can limit the model's exposure to distinctive \"no-helmet\" features during training.  
Figures 12 and 13 present sample images illustrating high and low detection accuracies, respectively. In Figure 12a, two workers are shown in a partially completed interior space with unfinished walls, exposed ceilings, and scattered construction materials. The scene also includes construction tools and safety markers, such as a traffic cone, emphasizing that it is an active job site. The model correctly detects a missing mask and glove on the left worker and a missing test and glove on the right worker. Figure 12b depicts four workers on an outdoor construction site where concrete is being poured. This environment appears more open-air, with scaffolding and wet concrete underfoot. All the missing PPE items in this scenario are accurately identified by the bounding boxes, highlighting the model  
Figure 13a shows two workers on a high-rise construction site with rebar, plywood, and other building materials in the foreground. Tall buildings dominate the background, suggesting an urban environment. The individual in the foreground wears a helmet and mask but lacks a safety vest and gloves, while the worker behind him is incorrectly flagged for missing PPE--he is not detected as lacking a vest, even though he actually does not have one. Figure 13b provides a closer view of a worker using a demolition tool on a concrete surface. Here, the tool is erroneously identified as \"non-glove\". Nonetheless, the bounding boxes correctly indicate \"non-glove\" and \"non-shoes\", highlighting that the worker is not wearing protective gloves or appropriate footwear during a high-risk task that generates substantial debris.