## 2 Investigated Material Structures: Theoretical Background and Related Works
The opposing aspects are mainly about applying techniques and the results' accuracy. These findings suggest that combining AI, advanced sensors, and non-invasive methods will represent the future of materials and structure testing. It can be said that currently, various types of neural network architectures are used, which, due to their specific properties, are suitable for different types of specific tasks (Table 1). When comparing GoogleNet with different deep learning methods, the main facts can be briefly summarized as follows: GoogleNet, as a CNN, improves upon traditional CNNs with its Inception module, which enables multidimensional feature extraction in a single layer. It is more efficient and accurate for image-based tasks but has a more complex architecture. ResNet's residual connections solve the vanishing gradient problem, enabling very deep networks. While ResNet achieves higher accuracy in deep architectures, GoogleNet is more computationally efficient and suitable for resource-constrained tasks. VGG (Visual Geometry Group) employs a simple architecture but requires a high number of parameters. GoogleNet is more efficient with fewer parameters, making it better suited for tasks requiring computational efficiency, while VGG remains popular for transfer learning. Transformers excel at sequential tasks such as NLP (natural language processing) and have been adapted for vision applications. GoogleNet is more efficient for traditional computer vision tasks, while Transformers dominate in sequential and emerging vision applications. RNNs and LSTMs effectively process sequential data, such as text or time series, but are \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Network Type** & **Advantages** & **Disadvantages** & **Application** \\ \hline \multirow{2}{*}{CNN} & Automatic feature extraction, robustness & Requires a lot of data, \multirow{2}{*}{} & \multirow{2}{*}{Computer vision} \\  & & & sensitive to architecture \\ \multirow{2}{*}{ANN} & \multirow{2}{*}{Versatility, simplicity} & \multirow{2}{*}{Inefficient for structured data} & \multirow{2}{*}{
\begin{tabular}{c} Predictions, \multirow{2}{*}{classification} \\ \end{tabular} } \\  & & & \\ \multirow{2}{*}{KNN} & Simplicity, no \multirow{2}{*}{} & \multirow{2}{*}{Unsuitable for large datasets} & \multirow{2}{*}{Classification} \\  & training needed & & \\ \multirow{2}{*}{Faster R-CNN} & Accuracy, speed & Computationally intensive & Object detection \\ \multirow{2}{*}{FPN} & \multirow{2}{*}{Multi-scale detection} & Higher computational & Segmentation, small \\  & & demand & objects \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Overview table: summary of features and applications of various neural network types. less applicable to vision tasks. GoogleNet is better suited for spatial data, such as images. Faster R-CNN and FPN are specialized frameworks for object detection and segmentation. GoogleNet is more straightforward and faster for image classification, while Faster R-CNN and FPN excel in multi-scale object detection. GANs (Generative Adversarial Networks) focus on data generation, such as image synthesis, whereas GoogleNet is optimized for the classification and analysis of existing data. It can be said that GoogleNet revolutionized CNN architecture by introducing the Inception module, a versatile component that combines convolutions of multiple filter sizes in parallel within a single layer. This adaptability allows the network to learn features at different scales, improving its performance on tasks such as image classification and object recognition. Additionally, GoogleNet significantly reduced computational complexity compared to earlier architectures like AlexNet and VGG by using fewer parameters, thanks to its Inception modules and global average pooling layers instead of fully connected layers [23; 24; 25].