# Identification and Localization of Wind Turbine Blade Faults Using Deep Learning
## 2 Literature Review
This was accomplished through the proposed MinReact Network (MRnet) where the Graham scan algorithm extracts the convex hull points of a fault and allows the orientation of the image to minimize the area of the bounding box. This was followed by DenseNet-121 forming a new predictive head [21]. This method achieved an mAP50 of 98.7% on the utilized dataset, showing promising accuracy in WTB fault detection. However, the authors indicated a reduction in inference speed due to redundant calculations [21]. For identifying gaps in object detection performance metrics, [PERSON], [PERSON], and [PERSON] proposed three new evaluation measures for fault detection, namely Prediction Box Accuracy (PBA), Recognition Rate (RR), and False Label Rate (FLR) [22]. Additionally, an image augmentation pipeline was introduced to further enhance the predictive capabilities of the Mask R-CNN architecture. The simulations used a dataset created through routine WTB maintenance and consisting of the following classes: cracks, erosion, voids, and others. With the proposed image enhancement and augmentation pipeline consisting of flipping, rotation, and histogram equalization, the performance resulted in the mAP50 of 84.21% [22]. Seeking computational performance gains, researchers [PERSON] and [PERSON] utilized depthwise separable convolution in the ResNet50 backbone of the Cascade Mask R-CNN model [23]. This reduction in backbone complexity resulted in a 13 million parameter decrease overall. Inference speed when compared to Mask R-CNN was reduced dramatically from 368 s to 136 s [23]. Leveraging a dataset consisting of lightning damage, rust, erosion, crack, and broken parts along with data augmentation, the performance of the proposed model was compared. Here, the decoupled convolution layers in the proposed architecture provides comparable accuracy with a considerable inference speed increase at mAP 84.36% overall [23]. After reviewing all these related works, it is clear that while significant advancements have been made in applying deep learning to WTB fault detection, there remain critical gaps that our study addresses. Current research predominantly focuses on individual architectural modifications without comprehensive comparative analyses between single-stage and two-stage detectors. Our study is crucial as it provides a detailed comparison of YOLO and Mask R-CNN architectures, highlighting their performance in terms of accuracy and computational efficiency under consistent conditions. Additionally, the creation of a new synthetic dataset simulating real-world conditions fills a vital resource gap, enabling more practical and scalable solutions. By addressing these gaps, our study enhances the robustness and efficiency of WTB fault detection systems, contributing to improved maintenance and reliability in wind turbine operations. The modifications of the Mask R-CNN architectures for WTB fault diagnosis can be summarized as follows. Image Enhancement and Data Augmentation: With the inherent difficulty of image collection for WTB inspection, image enhancement techniques can provide de-blurring or feature enrichment. Additionally, the use of data augmentation has been heavily explored for further performance gains. 2. Backbone alteration: Caused by the two-stage architecture design of Mask R-CNN, the inference speed is considerably slower than that of YOLO. Novel backbone changes reducing complexity can bring this architecture closer to real-time detection with minimal accuracy impacts. Additionally, architecture improvements can further increase the accuracy of this model for defect identification.