## 3 Results and Discussion  
The proposed model was evaluated in terms of its accuracy and balanced accuracy metrics. Additionally, the outcomes of the feature selection process and the classification results based on the convolutional neural network (CNN) architectures employed are provided in detail, offering insights into the model's effectiveness. The proposed model initially extracted nine feature vectors, each comprising 1000 features. Subsequently, INCA was employed to identify the most efficacious features for addressing the issue. The number of selected features in accordance with the CNNs utilized is illustrated in Figure 4.  
The convolutional neural networks (CNNs) corresponding to the numbers on the \(x\)-axis are as follows: the CNNs are ordered from 1 to 9, with 1 corresponding to MobileNetV2, 2 to ResNet50, 3 to DarkNet53, 4 to AlexNet, 5 to ShuffleNet, 6 to DenseNet201, 7 to InceptionV3, 8 to InceptionResNetV2, and 9 to GoogLeNet. Figure 4: Number of features selected by INCA for each CNN used. Figure 3: The confusion matrix used to compute the classification performances. Herein, we have used the helmeted class as a positive class and the unhelmeted class as a negative class. By using the depicted parameters, the classification performances have been computed. Green for true and orange for false classifications. As illustrated in Figure 4, INCA selected a distinct number of features for each feature set, resulting in feature vectors of varying dimensions for each CNN. INCA selected the top 105 features from the feature vector generated by DarkNet53, whereas it selected 573 features for ResNet18. As previously stated, the proposed model produces 16 results, categorized into two distinct groups: weighted and classifier-based. The results are presented in Tables 1 and 2 for the reader's convenience. As illustrated in Table 1, the kNN results indicate that DenseNet201 yielded the most optimal outcomes, with an accuracy of 88.37% and a balanced accuracy of 88.46%. The aforementioned outputs were then employed as inputs for IMV, with the objective of calculating the weighted results which are presented in Table 2.  
As demonstrated in Tables 1 and 2, the highest accuracy and balanced accuracy rates were calculated to be 90.39% and 90.59%, respectively. These results exceed the 86.28% accuracy rate reported by [PERSON] and [PERSON] (2022) [10], which encompassed a diverse range of work environments, including construction. In comparison to other safety helmet detection methods, such as the EGS-YOLO model developed by [PERSON] et al. (2024) [45], our study presents a distinct approach tailored to the specific context of agricultural machinery factories. While both studies share the common objective of enhancing occupational safety through the detection of helmet usage, the focus, methodologies, and application environments differ significantly. The EGS-YOLO study emphasizes real-time performance improvements for dynamic environments like construction sites. By modifying YOLOv7 through the introduction of GhostModule, SE blocks, and the EIOU loss function, the model achieved a mAP of 91.1% with a 13.3% reduction in inference time compared to YOLOv7. This makes the EGS-YOLO method highly effective for real-time detection, where speed and computational efficiency are paramount. In contrast, our approach employs transfer learning with nine pre-trained neural networks, combined with iterative neighborhood component analysis (INCA) for feature selection and k-nearest neighbor (kNN) classification. By utilizing iterative majority voting (IMV) across multiple networks, our model prioritizes accuracy, achieving 90.39%, with DenseNet201 yielding the best performance. While real-time detection is not the primary focus, the model demonstrates significant potential in controlled environments, such as agricultural machinery factories,