###### Abstract  
The objective of this study was to develop an artificial intelligence (AI)-driven model for the detection of helmet usage among workers in tractor and agricultural machinery factories with the aim of enhancing occupational safety. A transfer learning approach was employed, utilizing nine pre-trained neural networks for the extraction of deep features. The following neural networks were employed: MobileNetV2, ResNet50, DarkNet53, AlexNet, ShuffleNet, DenseNet201, InceptionV3, Inception-ResNetV2, and GoogLeNet. Subsequently, the extracted features were subjected to iterative neighborhood component analysis (INCA) for feature selection, after which they were classified using the k-nearest neighbor (kNN) algorithm. The classification outputs of all networks were combined through iterative majority voting (IMV) to achieve optimal results. To evaluate the model, an image dataset comprising 662 images of individuals wearing helmets and 722 images of individuals without helmets sourced from the internet was constructed. The proposed model achieved an accuracy of 90.39%, with DenseNet201 producing the most accurate results. This AI-driven helmet detection model demonstrates significant potential in improving occupational safety by assisting safety officers, especially in confined environments, reducing human error, and enhancing efficiency. O m 2024 14 11278 11.11278 11. The necessity of helmet use in occupational health and safety is underscored as a pivotal concern, particularly in high-risk work environments. The deployment of helmets is of paramount importance in safeguarding worker safety, as it markedly diminishes the likelihood of head and brain injuries [11]. Artificial intelligence (AI) is a field of study that has been the subject of considerable attention in recent years, with a particular focus on its potential to enhance productivity in agricultural contexts [12]. However, it is also a technology that has the potential to revolutionize the field of occupational health and safety [13]. Despite the effective use of AI and machine learning methods in various sectors, studies focusing on occupational health and safety remain limited. Artificial intelligence (AI) technologies have a significant impact on the detection and analysis of risks in the workplace. The utilization of sophisticated algorithms enables AI to process extensive datasets and discern intricate patterns. Such capabilities may be deployed to identify potential hazards and risks in the workplace. To illustrate, AI-based image recognition models can monitor the correct use of protective equipment by employees, such as helmets, goggles, or ear protection, and issue warnings if such equipment is absent [14]. Artificial intelligence can be employed as a valuable tool to enhance workplace safety, reduce risks, and prevent accidents. These technological advancements can make a significant contribution to enhancing workplace safety and efficiency [15]. In particular, the absence of AI-based applications designed to guarantee worker safety represents a significant gap in the existing literature, particularly in specific fields, such as tractor factories [16]. There is a paucity of AI-based models that support occupational health and safety in specialized industries, such as facilities producing tractors and agricultural machinery. In a recent study, [PERSON] et al. (2021) [17] employed machine learning algorithms utilizing microelectromechanical system (MEMS)-based sensors to ascertain the extent of helmet utilization on construction sites. The objective of their research was to develop a real-time, automated helmet detection model to enhance workplace safety and compliance monitoring. Similarly, [PERSON] et al. (2020) [18] employed deep learning techniques to detect personal protective equipment in real time, thereby demonstrating the potential of AI-based visual recognition models in identifying workers' use of helmets and other safety equipment.