## 4 Methodology
### Overview of the Deep Learning Models Used for Classification  
In this study, we focus on CNNs [55] due to their proven effectiveness in image classification tasks, particularly in computer vision applications like defect detection and object recognition. CNNs are designed to automatically and adaptively learn the spatial hierarchies of features from input images, making them highly suitable for the detailed analysis required in our application. The inherent features of CNNs, such as local connectivity, weight sharing, and the use of multiple layers, allow for the extraction of both low-level and high-level features, facilitating accurate and efficient image classification.  
Figure 2: System diagram utilized by _DJI Tello EDU_ drone and local computer.  
While CNNs have demonstrated superiority over traditional Artificial Neural Networks (ANNs) [56] in handling image data, their performance in comparison to Recurrent Neural Networks (RNNs) [57] and Vision Transformers (ViTs) [58] for the proposed application also merits discussion. RNNs, which are typically used for sequential data and time series analysis, are less effective in capturing spatial dependencies within an image. Therefore, they are not the optimal choice for our application, which requires the analysis of static images with complex spatial features.  
Vision Transformers (ViTs), on the other hand, represent a novel approach in image classification, leveraging self-attention mechanisms to capture global context. Although ViTs have shown promising results, they often require larger datasets and more computational resources for training compared to CNNs. In our educational setting, where computational resources and labeled data may be limited, CNNs offer a more practical and accessible solution.  
The choice of CNNs over ANNs and RNNs is also justified by their architectural advantages. ANNs, with their fully connected layers, lack the spatial hierarchy that CNNs exploit. This makes ANNs less effective for tasks involving image data, where spatial relationships between pixels are crucial. RNNs, designed for sequential data processing, excel in tasks such as natural language processing, but do not leverage the spatial structure of images effectively.  
Furthermore, the historical development and success of CNN architectures, such as AlexNet, VGG, ResNet, DenseNet, and Xception, provide a robust foundation for our work. These architectures have been extensively validated across various image-classification benchmarks, and their design principles align well with the requirements of our defect-detection task. By employing CNNs, we leverage their strengths in feature extraction and pattern recognition, ensuring a reliable and efficient inspection process for pedestal fan blades.  
In summary, the selection of CNNs is based on their superior ability to handle spatial hierarchies in image data, their proven success in numerous computer vision applications, and the practical considerations of computational resources and data availability in our educational setting.  
The subsequent sections will discuss each of these models in detail, highlighting their architectures and the specific advantages they bring to our application.