## 4 Discussion and Conclusions
As proposed in [77], deviations from the expected Hu moment signature of an isolated spheroidal nodule can illustrate the presence of merged particles. Comparing these descriptors with reference patterns of type V or VI nodules enables selective application of segmentation techniques, improving classification accuracy without unnecessary computational overhead. Regarding the use of YOLOv11 for chemically etched sample analysis and with the final objective of detecting and quantifying carbides, we employed the YOLOv11-nano variant, trained for 200 epochs, as a proof of concept to evaluate the feasibility and gain preliminary insights under limited computational resources. Carbide segmentation is particularly more complex than the other tasks performed in this research due to the variability in morphology and contrast, as well as spectral similarity with adjacent microstructural phases. Although the model did not fully converge within these 200 epochs, as evidenced in Section 3.3.2 and shown in Figure 16, the overall training dynamics and detection performance were promising. This suggests that the chosen training duration provides a reasonable balance between computational cost and model learning. In addition, the observed volatility in validation loss also reflects limitations in dataset size and complexity, although the DA process has been developed. In this way, and considering these encouraging results, further work must involve the development of a larger training phase for our YOLOv11. Firstly, the number of images can be improved by extracting more information from new reports generated by the specific department of our research center. Moreover, the performance of the detection  
Figure 23: An example involving type V graphite particles that are in contact and mistakenly classified as type III due to their combined shape. In such cases, segmentation techniques must be applied to separate the individual particles and ensure accurate classification. task must also be enhanced when other variants are employed (for instance, YOLOv11 small or base) and the number of epochs is extended (creating the new experiment using 300 or 500). Similarly, the new research work must incorporate the newest YOLO version, YOLOv12, which introduces architectural innovations and refined segmentation heads. All these changes in the carbide detection model generation must improve the convergence, robustness, and detection accuracy of this method. In addition, the inclusion of EfficientNetV2-S is proposed to retrain the classification components of the system. As shown in [78], EfficientNetV2 offers an improvement in learning efficiency and accuracy for microscopy particle analysis. Its integration into HawkEye would benefit both the graphite classification and the analysis of the matrix structure. Another feasible improvement involves quality classification and grading, where the coefficient of variation of particle spatial distribution is already computed but has not yet been integrated into the final classification logic. Including this parameter would contribute to a more comprehensive quality evaluation by accounting for the uniformity of particle dispersion, which is known to affect mechanical performance and failure modes. This enhancement aligns with the objectives of this study and should be prioritized in future research. To ensure trustworthiness in systems like HawkEye, it will be crucial to implement provisions for a manual override capability. While automated processes and confidence scores offer significant efficiency and accuracy, there will always be situations where human intervention is necessary. This capability allows operators to review, adjust, or even completely disregard automated decisions based on their expertise, contextual understanding, or external information not available to the system.