# Identification and Localization of Wind Turbine Blade Faults Using Deep Learning
## 5 Discussion  
Further investigation of the model performance was conducted following the optimal performance and comparison of each architecture. The top model and size were selected with corresponding test set inference results for visualization, allowing trend and pattern analysis in model prediction. Figure 10 shows a sample of the predictions from the YOLOv9C model. Figure 10a illustrates the misclassifications of the model. Reflections from the shiny blade produced features that looked like cracks, leading to an increased number of false detections. Additionally, crack-like features outside the blade area also contributed to false detections due to the background and turbine chassis features. Figure 10b illustrates the confident predictions from the YOLOv9C model. The model was capable of localizing each small hole feature and crack along the width of the turbine blade. Furthermore, the erosion damage, which is angled to offer more background information, attained significant confidence values. Analyzing the inference results for each YOLO model and size including v5, v8, and v9, shows a common trend in hole classification that explains the lower mAP values in Table 5. Figure 11 contains a sample of these inference results along with their corresponding ground truth label. Here, the model commonly classifies the mounting hardware as hole damage. While the mounting points contained distinctly hole-like features, they were not labeled as such during dataset creation. This led the models to generalize the hole damage features and still detect and classify in these cases, resulting in lower overall performance. Additionally, the crack classification results observed in YOLOv9 continued in each YOLO architecture, with turbine chassis detections and light reflections causing false detections. As was discussed earlier, Table 7 describes the performance of Mask R-CNN with three backbones of VGG19, ResNet50, and ResNet18 on the CAI-SWTB dataset. According to the inference results of this table, Mask-RCNN with ResNet18 shows the most promising results with a higher combined mAP50 box score and a lower computational cost. Figure 12 shows a sample of inference results from the Mask R-CNN model with the ResNet18-FPN backbone network. According to this Figure, it is evident here that the instance segmentation and localization abilities suffer when the turbine chassis is present within the data. The shadow created by the hub, chassis, and blade, shown in Figure 12a, leads to crack and erosion false detections explaining the lower erosion class metrics for each of the modified backbone architectures. However, when the blade is the main component of the composition, the localization abilities are considerably high in confidence. This is further illustrated in Figure 12b, where the confidence for erosion, crack, and hole detection achieves well above 90% and provides instance segmentation, allowing for pixel-level classification. Figure 13a shows additional samples of these common misclassifications from Mask R-CNN with a ResNet18 backbone that were also observed using ResNet50 but with different confidence intervals. The corresponding ground truth labels of healthy blade images are illustrated in Figure 13b. This study investigated two popular architectures for the localization of faults in WTBs: YOLO and Mask R-CNN. While traditionally two-stage detectors, like Mask R-CNN, are considered more accurate, our study shows that the single-stage design of YOLO achieves a comparable performance. This raises the question of which architecture should be chosen for fault localization in WTBs. To answer this, multiple aspects must be considered. YOLO achieves promising precision while maintaining low computational costs and the ability to achieve close-to-real-time inference. However, Mask R-CNN provides comparable precision with the addition of instance segmentation.