## 2 Materials and Methods
The principal characteristics of Darknet53 are as follows: This model serves as the foundation for YOLOv3, and it effectively extracts features through the use of 53 convolutional layers. Figure 1: The visual materials utilized in the analytical process. Primarily, it serves as the foundation for object detection models, such as YOLOv3, and is also utilized in YOLOv7 and YOLOv8, where similar Darknet53 architectures are employed as the backbone [36]. AlexNet is a convolutional neural network (CNN) designed by [PERSON], [PERSON], and [PERSON]. It was instrumental in advancing deep learning for visual recognition. The AlexNet convolutional neural network (CNN) was a pioneering development in the field of deep learning for visual recognition. The AlexNet algorithmic framework incorporates several key features, including the following:  
The introduction of ReLU activations and dropout was a significant advancement. It employs a deep architectural structure comprising multiple convolutional layers. The original development of AlexNet was for image classification purposes, and it established the foundation for numerous subsequent deep learning studies and applications. The success of AlexNet provided the impetus for the development of other convolutional neural networks, which continue to employ its foundational methodologies [37]. DenseNet201 is a convolutional neural network developed by Google Brain. DenseNet201, developed by Facebook AI Research (FAIR), represents a member of the DenseNet family, distinguished by its densely connected layers. The key features of DenseNet201 are as follows:  
This enables each layer to receive features from all preceding layers. It facilitates the propagation of features while simultaneously reducing the number of parameters. DenseNet201 is a widely utilized network for image classification, segmentation and other vision tasks [38]. InceptionV3 is a convolutional neural network developed by Google researchers. It is part of the Inception family of networks, which are distinguished by their use of parallel processing through the incorporation of distinct \"modules\". It is a deep and wide convolutional neural network (CNN), similar to GoogLeNet. The key features of InceptionV3 are as follows:  
It employs parallel operations within each module, thereby facilitating efficient computation. The objective was to achieve high accuracy without excessive computational demands. It is widely employed in the fields of image classification, transfer learning, and diverse image processing tasks [39]. InceptionResNetV2 represents a further development of the Inception architecture. InceptionResNetV2, developed by Google, combines the strengths of the Inception architecture with ResNet's residual connections. However, it should be noted that this does not always guarantee superior performance over ResNet or InceptionNet. The model features the following:  
It combines the parallel module processing of Inception with the residual connections of ResNet. The design objective was to achieve high performance while maintaining computational efficiency. Similarly, as with InceptionV3, it is a prevalent tool in the domain of image classification and diverse vision tasks [40]. GoogLeNet: The GoogLeNet model features a deep and wide architecture with the following essential characteristics:  
The model incorporates the Inception module, which employs parallel operations to facilitate efficient computation. It contains a significantly reduced number of parameters in comparison to contemporary models, while maintaining the same level of accuracy. Initially designed for image classification, GoogLeNet has subsequently formed the basis for further deep vision research and applications [41]. This study proposes a next-generation hybrid deep feature-based image classification method. The principal objective of this image classification method is to illustrate the potential advantages that artificial intelligence can offer in the field of occupational health and safety.