## 2 Materials and Methods  
In this study, the dataset was obtained from open sources and tractor manufacturers. The dataset comprises images in the JPG and PNG formats. Two classes, named \"with helmet\" and \"without helmet\", were created within this dataset. To ensure the dataset's suitability for real-world applications, images were captured from various angles and positions, thereby reflecting real-world scenarios (Figure 1). The composition of the dataset, comprising images taken from actual work environments, clearly demonstrates the feasibility of the proposed method for real-time use. The images used in this study were obtained from agricultural machinery and tractor manufacturing facilities. It is important to note that each manufacturing facility may have different conditions, adherence to safety protocols, and compliance rates, which could vary based on the specific machinery produced. As researchers, we recognize that it is not feasible to control these variables across all manufacturers, which may introduce some variability in the dataset. The dataset comprises 662 images of helmeted individuals and 722 images of unhelmeted individuals. Although the dataset size is limited, it was selected to reflect real-world scenarios, incorporating images with varying angles and lighting conditions. In this study, a novel transfer learning-based model was developed. The model comprises four principal stages: feature extraction, feature selection, classification, and weighted voting. The initial stage of the developed model is feature extraction, which employs pre-trained deep convolutional neural networks (CNNs). The convolutional neural networks (CNNs) utilized were previously trained on a dataset, ImageNet1k, comprising approximately 1.3 million images of 1000 distinct objects. The choice of nine pre-trained networks was made to ensure diverse feature extraction capabilities, as these networks are known for their strengths in different contexts, such as object detection, efficiency, and depth. The MobileNetV2 model is a convolutional neural network designed by Google researchers with the objective of achieving greater efficiency, making it suitable for use in mobile and embedded vision applications. MobileNetV2, designed by Google researchers, is a convolutional neural network with a focus on efficiency, rendering it suitable for deployment in mobile and embedded vision applications. The main features of the network are as follows [34]. The model has been optimized for both computational efficiency and model size without a significant reduction in performance. This architectural style is optimal for on-device applications where computing resources are constrained. The ResNet50 network is a convolutional neural network designed for efficiency. It is composed of 50 layers and utilizes \"skip\" or \"residual\" connections. ResNet50 was developed by researchers at Microsoft and formed part of the ResNet family of architectures, which are characterized by 50 layers and \"skip\" or \"residual\" connections. ResNet50 represents a foundational convolutional neural network (CNN), serving as a reference for subsequent models. Indeed, almost all subsequent CNNs incorporate features from the ResNet architecture. The key features of ResNet are as follows:  
* The model incorporates shortcut connections that skip one or more layers. The shortcuts or bypasses facilitate the fusion of information between the input and output. * The network was designed to eliminate the vanishing gradient problem that is common in deep networks. ResNet50 is a widely utilized network for image classification, object detection, and a range of other vision tasks [35]. The Darknet53 network is as follows: Darknet53 is a 53-layer network that serves as the foundation for YOLOv3, a widely used object detection model. It is renowned for its optimal balance of performance and efficiency.