# Identification and Localization of Wind Turbine Blade Faults Using Deep Learning
## 4 Simulations and Results
### YOLO Results  
With the rapid evolution and continuous enhancement of YOLO architectures, the task of WTB fault localization has become an area of intense interest. In this study, we compared the performance of the YOLOv5, YOLOv7, YOLOv8, and the latest YOLOv9, which was unveiled in February 2024. We also trained and compared the nano, medium, and extra-large model sizes to identify the optimal performance among the YOLO architectures. This research promises to bring new insights and advancements to the field. To ensure the reliability and fairness of our comparison between architectures, we deployed image augmentation through the provided repositories. The same parameters were meticulously utilized for each YOLO model, as shown in Table 3. The mosaic augmentation, a sophisticated strategy that overlays four images along with their corresponding labels into one image, was employed. This approach simulates various scene compositions and object interactions, thereby enhancing the performance of object detectors. Each architecture and corresponding model size was trained with the parameters shown in Table 4. Following training, the test set was deployed to determine model performance on unseen data. The corresponding results can be seen in Table 5. Here, each of \begin{table}
\begin{tabular}{c c} \hline \hline
**Augmentation Parameter** & **Value** \\ \hline Hue & 0.015 \\ Saturation & 0.7 \\ Brightness & 0.4 \\ Translation & 0.1 \\ Scale & 0.5 \\ Flip Left/Right & 0.5 \\ Mosaic & 1.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: YOLO architectures image augmentation parameters. the YOLO versions along with model sizes are shown with the obtained mAP50 and mAP50-95 metrics. The newest YOLO model in version 9 provided the highest mAP50 and mAP50-95, showing a considerable improvement over previous architecture designs. Between the two available sizes, the smaller size in C provided the best resulting performance on this dataset, obtaining an mAP50 of 0.8490 and mAP50-95 of 0.5390. The trend of the medium size architectures providing considerable performance increase over the nano and extra-large variants continued through each of the next YOLO architecture versions. Here, YOLOv8 Medium achieved the third best performance, just short of both YOLOv9 sizes. YOLOv9C, the model with the best performance, is further evaluated using the confusion matrix in Figure 6, which visualizes the predictions against the ground truths. The confusion matrix reveals that the model infrequently predicts other fault types among the existing false negatives. However, it often misclassifies backgrounds as containing no faulty blades. The fault class hole shows the lowest accuracy, with 77% of labels correctly predicted, while the crack class exhibits the highest accuracy, with 90% of labels correctly predicted. This disparity in class performance is also reflected in the precision-recall curve shown in Figure 7.  
To visually inspect the different YOLO architecture versions, a graph of the validation mAP50 values versus the epoch of training is plotted in Figure 8. Here, the best corresponding runs from each architecture were chosen. It can be seen that the YOLOv9 architecture achieves a higher mAP50 on the validation data at a faster rate than the previous versions. YOLOv5 and YOLOv8 are comparable throughout each epoch with YOLOv5 achieving a marginally better mAP50 at epoch 100. \begin{table}
\begin{tabular}{c c} \hline \hline
**Training Parameter** & **Value** \\ \hline Epoch & 100 \\ Optimizer & AdamW \\ Image Size & 300 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: YOLO architecture training parameters. Figure 6: YOLOv9 C confusion matrix. Figure 7: YOLOv9 C precision–recall curve.