# Identification and Localization of Wind Turbine Blade Faults Using Deep Learning
## 2 Literature Review
In addition, the inclusion of SENet attention modules was utilized to enhance feature extraction and reduce redundant feature information. Additionally, the C3 module was replaced with the C2F module, resulting in a more abundant gradient flow [18]. With these architecture changes, the performance gained an increase in precision over the base YOLOv5s by 1.9% [18]. Using the performance gains of BiFPN and CBAM, [PERSON] et al. proposed an enhanced YOLOv8 architecture [19]. Like [17; 18], BiFPN replaced PANet, which prioritizes small target features and increases multi-scale fusion. Further improving small-target acquisition, the CBAM attention module was integrated into the backbone. Finally, EIoU was deployed to help with model training and convergence. For the validation of model performance, a dataset was created through UAV inspection of WTBs with the following classes: breakages, cracks, and scratches [19]. The ablation experiment indicated the highest performance increase through BiFPN, which achieved an mAP50 of 83.3% compared to a base YOLOv8 of 81.9% [19]. In an effort to mitigate the complexity of YOLOv and enhance multi-scale small-target WTB feature detection, [PERSON], [PERSON], and [PERSON] introduced WT-YOLOx [20]. This novel architecture incorporates a RepVGG backbone, which effectively diminishes the computational burden of YOLOx, thereby augmenting real-time performance while preserving extensive feature fusion through a cascade feature fusion module. To further optimize model performance and address class imbalance, focal loss was employed. For the purpose of evaluating the architecture, a total of 725 images were captured using UAVs at a wind farm in Mongolia. To augment the dataset's variability, image augmentation techniques were applied, culminating in a comprehensive dataset of 5800 images encompassing the classes of pollution, fix, crack, and break [20]. These architectural enhancements culminated in an impressive 94.29% mAP50, surpassing the performance of both YOLOx and SSD architectures [20]. The modifications of the YOLO architectures for WTB fault diagnosis can be summarized as follows. 1. Backbone Alteration: By adjusting the backbone feature extraction architecture, higher performance can be gained in low-level feature propagation through the network. This can result in higher accuracy in hard-to-detect classes in fault diagnosis. Additionally,modifications can strive for a reduction in overall computational complexity resulting in better real-time and edge performance. 2. Loss Function: In changing the loss function, the model can be driven toward specific goals. Commonly, the focal loss is deployed which achieves better performance on hard-to-detect or partially obscured defects by weighting these over easy objects of interest. 3. Attention Modules: In the pursuit of enhancing the feature maps produced by the network, attention modules allow a unique weighting to prominent features to increase representation through the network. This has been shown to be a valuable addition in fault identification, specifically in the performance on smaller anomalies. Mask R-CNN, a prominent network architecture in WTB visual analysis, offers instance segmentation alongside precise bounding box predictions. In this context, the term mask refers to the capability of the model to generate pixel-wise segmentation masks for each detected object instance. These masks outline the exact shape and extent of the object within the image, providing detailed information about its spatial distribution. Consequently, the masks generated by Mask R-CNN enable researchers to perform further analysis, such as assessing the size and severity of defects on WTBs. For instance, [PERSON], [PERSON], and [PERSON] proposed a pre-processing pipeline aimed at refining mask data obtained from UAV imagery to filter out background noise [21].