## 2 Materials and Methods
### Image Typology Classification to Identify Specific Workflow  
The first challenge addressed in this work focuses on the classification of metallographic images. This kind of categorization is essential, as many of the subsequent analytical and processing operations depend on correct classification. The assigned class directly influences the selection of an appropriate processing pipeline with its specific analysis algorithms. Consequently, the classification stage serves as a critical decision-making layer, guiding the system toward specific procedures tailored to the features extracted from each image.  
The image classification process is a well-established field, extensively documented in a broad range of scientific literature. For instance, [PERSON] et al. [24] provide a comprehensive overview of traditional and modern classification techniques, highlighting their applicability to diverse domains. In recent years, the emergence of DL has significantly enhanced classification performance, particularly through the use of lightweight and efficient neural network architectures. One notable example is MobileNetV3, which has demonstrated strong accuracy-efficiency trade-offs in various image classification tasks, as reported by [PERSON] et al. [25].  
Image classification techniques have been applied across a variety of domains. In the context of social media, the growing ubiquity of user-generated content has caused studies on human behavior and user interaction patterns. For instance, [PERSON] et al. [26] use image classification for disaster response, enabling the rapid identification of relevant visual content during emergencies. In the medical field, DL-based image classification has proven effective in diagnostic support systems, as demonstrated by [PERSON] et al. [27].  
In addition, the industrial sector has also benefited significantly from the integration of AI in visual inspection and classification tasks. For example, [PERSON] et al. [28] applied hyperspectral image classification for quality control in food packaging processes. Similarly, [PERSON] et al. [29] explored its use in analyzing the freshness of fruits, while [PERSON] et al. [30] demonstrated its effectiveness in detecting defects in manufactured components. These studies collectively highlight the versatility and impact of image classification.  
In our research, specifically in the first challenge, we identified that the image classification tasks must focus on the following:  
1. Identification of the material type. This classification allows the selection of appropriate analysis protocols tailored to the specific morphology of graphite (i.e., nodular, vermicular, or lamellar).
2. Detection of etching status. This classification task is generated to distinguish between etched and non-etched metallographic images, which significantly influences the type of image processing and segmentation techniques to be applied.
3. Assessment of usability of the image. It is possible to obtain and process a low-quality image that will not yield accurate results for the AI models. Therefore, this classification task ensures that only images of sufficient quality and clarity are used.  
In addition, the system will alert users that the calculated measurements could be distorted and their accuracy may not meet expectations.
4. Acceleration of the overall pipeline. By identifying the potential presence of specific events or features in the metallographic image, such as the existence of carbides, the system can make early decisions that optimize resource allocation and avoid unnecessary computation.