# Identification and Localization of Wind Turbine Blade Faults Using Deep Learning
## 2 Literature Review  
The growing availability of high-performance computational resources coupled with the surge in research interest surrounding deep learning has increased the application of these algorithms to the domain of wind turbine inspection [13]. Of these inspection methods, object detection remains dominant in the literature, providing an accurate solution to the localization and classification of WTB anomalies. The algorithms deployed in this sector consist of either a single-stage model or a two-stage model. The inherent trade-offs in these different architecture design methods include inference computation time and accuracy, with two-stage models traditionally garnering higher accuracy overall. However, in resource-constrained or time-sensitive environments, single-stage architectures can provide close to real-time inference. Here, the literature introduces novel modifications to increase the accuracy, inference speed, or both. In this section, a breakdown of the recent methods and results is discussed for both single-stage detectors and two-stage detectors, followed by alternative methods. To further reduce the computational complexity of the backbone of You Only Look Once (YOLO) version 4, [PERSON], [PERSON], and [PERSON] incorporated a MobileNet1 architecture as the feature extraction [14]. Furthermore, the model was further enhanced with attention modules, including Squeeze and Excitation Networks (SENet), Convolutional Block Attention Module (CBAM), and Efficient Channel Attention for Deep Convolutional Neural Networks (ECANet) [15]. Through a sensitivity study, the backbone-only adjustment reduced overall model complexity but decreased the performance compared to base YOLOv4. With the addition of attention modules, specifically SENet and ECANet, the model precision overtook base YOLOv4 while maintaining a reduced model complexity due to backbone alteration [14]. The proposed model, MobileNetv1-YOLOv4, achieved a mAP50 of 88.61 on their created dataset consisting of spalling, pitting, crack, and contamination faults on WTB [14]. Similarly, [PERSON] and [PERSON] investigated the performance of YOLOv4 by adding Spatial Attention Modules (SAMs) and a unique Mish activation function [16]. With the added generalization of pre-trained COCO weights for distillation and data augmentation schemes to introduce training variance, the proposed model achieved 86.17% mAP50 [16]. To increase the performance of YOLOv5 on minor target defects in WTBs, [PERSON] et al. proposed novel architecture modifications, including an improved feature pyramid network, coordinate attention module Coordinate Attention (CA), and integration of an Efficient Intersection over Union (EIoU) loss function [17]. The alteration to the pyramid network utilizes the Bi-directional Feature Pyramid Network (BiFPN) to enable the network to learn input features of varying importance through weighted feature fusion [17]. Additionally, the CA module that was implemented allows the model to augment the representations of interest. Finally, with the EIoU implemented, the model achieved a better localization effect of the WTB defects. To test their proposed model, a dataset of 599 images collected with Unmanned Aerial Vehicles (UAVs) was utilized after being synthetically augmented to increase the dataset to 2995. The following ablation experiment indicated the most model improvement with adding the CA module, increasing mAP50 from 80.5% in base YOLOv5s to 82.5%. Finally, with all modifications, the model achieved 83.7% [17]. Echoing the performance abilities of YOLOv5, [PERSON], [PERSON], and [PERSON] also investigated a BiFPN backbone to replace the base YOLOv5 Feature Pyramid Network (FPN) and Path Aggregation Network (PANet) to allow better feature fusion on WTBs [18]. Additional modifications in the proposed architecture also included focal loss, making it harder to classify examples to better handle class imbalance.