## 3 Results and Discussion
\begin{table}
\begin{tabular}{c c c c} \hline
**No** & **Method** & **Accuracy** & **Balanced Accuracy** \\ \hline
1 & MobileNetV2 + INCA + kNN & 87.57 & 87.62 \\ \hline
2 & ResNet50 + INCA + kNN & 86.71 & 86.74 \\
3 & DarkNet53 + INCA + kNN & 86.92 & 86.97 \\
4 & AlexNet + INCA + kNN & 84.90 & 84.96 \\
5 & DarkNet19 + INCA + kNN & 86.49 & 86.54 \\
6 & DenseNet201 + INCA + kNN & 88.37 & 88.46 \\
7 & InceptionV3 + INCA + kNN & 86.49 & 86.57 \\
8 & InceptionResNetV2 + INCA + kNN & 87.43 & 87.44 \\
9 & ResNet18 + INCA + kNN & 85.33 & 85.26 \\ \hline \end{tabular}
\end{table} 
Table 1: Classifier-based results (%). \begin{table}
\begin{tabular}{c c c} \hline
**No** & **Accuracy** & **Balanced Accuracy** \\ \hline
1 & 90.10 & 90.21 \\
2 & 89.23 & 89.47 \\
3 & 90.17 & 90.29 \\
4 & 90.17 & 90.37 \\
5 & 89.81 & 89.94 \\
6 & 89.88 & 90.07 \\
7 & 90.39 & 90.53 \\ \hline \end{tabular}
\end{table} 
Table 2: Results of weighted vectors (%). where high accuracy is critical for enhancing occupational safety. Thus, the two approaches complement each other in terms of application: the EGS-YOLO model is more suited for real-time monitoring in fast-paced environments like construction sites, while our model provides a robust solution for accurate detection in more controlled, factory-based settings. Both models contribute valuable insights to the field of AI-driven helmet detection, addressing different safety requirements based on the specific operational context. The confusion matrix for the results is presented in Figure 5.  
The proposed AI-based helmet detection model comprises a model designed to ascertain the status of helmet-wearing among individuals engaged in work activities within tractor factories. The dataset employed for model evaluation comprises images of individuals with and without helmets. The model commences with the derivation of feature vectors from nine distinct pre-trained neural networks, followed by the identification of the most salient features through the application of iterative neighborhood component analysis (INCA). Subsequently, the selected feature vectors are classified using the kNN method, thereby generating the results. Subsequently, these results are combined using the IMV algorithm in order to achieve the highest possible performance. The proposed model exhibited an accuracy rate of 90.39% on the collected dataset. The application of INCA for feature selection is a unique approach that is not widely documented in the existing safety helmet detection literature. For example, the EGS-YOLO model [45] focuses on the real-time detection of helmets using modified YOLO architecture but does not incorporate feature selection methods like INCA, which could potentially increase its accuracy in complex backgrounds. The results of this study show a noticeable improvement over similar models discussed in the literature. The model developed by [PERSON] and [PERSON] [46] achieves a mean average precision (mAP) of 92.87% for helmet detection using UAV imagery, which is slightly higher than our top accuracy rate. However, our model operates under different environmental conditions where UAV-based imaging is not feasible, demonstrating its utility in stationary, indoor settings where different types of agricultural machinery operate. The current study extends the functionality of typical CNN-based models by integrating feature selection directly into the classification pipeline, which is a significant enhancement over models such as those discussed by [PERSON] et al., where the focus is primarily on integrating different AI models without optimizing feature selection [21]. Our approach not only identifies the most relevant features but also reduces computational overhead, making it suitable for real-time applications where processing power is limited. Figure 5: Confusion matrix of the proposed model. Herein, there are 620 true helmeted, 42 false unhelmeted, 91 false helmeted, and 631 true unhelmeted predictions where helmeted positive and unhelmeted define negative classes.