## 2 Related Work  
The authors of \"A New Deep Class-Imbalanced Semisupervised Model for Wind Turbine Blade Icing Detection\" suggested a particular deep class-imbalanced, semisupervised prototype for estimating icing conditions on wind turbine blades. A prototype network that is able to stabilize the classes for tagged and unlabeled data is implemented to tackle the class-imbalance problem. By comparing the correlation of models for unlabeled and labeled instances in a latent feature space, the provided prototype network can classify unlabeled data as well as rebalance collected characteristics. The authors suggested enhancing the feature extractor's feature extraction capability by including an additional channel attention module. Last but not least, we carefully evaluated the proposed model using baseline comparison, ablation study, and online detection. The findings show that the suggested model is superior and effective [1].  
The authors of \"A Novel Vision-Based Approach Utilizing Deep Learning for Damage Inspection in Wind Turbine Blades\" proposed a deep learning vision-based method for detecting fractures, wear, and other defects on wind turbine blades. They also display a proof-of-concept that makes use of a robotic tool to take pictures of the blades' surfaces and automatically identify damage. To achieve this, a gallery of images showcasing the various kinds of impairments and breakages assessed in the study was assembled. A convolutional neural network was then trained on the images to detect damage. Following that, a prototype was made. In order to scan the entire surface of the wind turbine blades, a robot with a camera attached and a straightforward route-planning method were developed. A wind turbine model was created in order to examine the entire mechatronics setup [2].  
The authors of \"A New Vision-Based Method Using Deep Learning for Damage Inspection in Wind Turbine Blades\" proposed a distributed, collision-free command strategy for many drones which is computationally light and highly scalable. Unmanned aerial vehicles (UAVs) are guided along predetermined flight paths by the system to monitor structural assets like wind turbines, flare stacks, and tanks. Automated structural inspection employs this strategy to increase coverage by minimizing unscanned areas. To ensure total coverage of the target item, they perform coverage path planning (CPP). Contrarily, humans conduct visual inspections using the UAVs' video streaming [3].  
On the basis of SCADA data, [PERSON] et al. [4] suggested a deep autoencoder-based methodology for recognizing imminent defects on wind turbine blades. Nevertheless, this strategy concentrated on the forecast of blade failures, with no mention of methods for detecting early-stage flaws and continually monitoring their progression.  
A rising trend of employing UAVs to monitor wind turbine blade surface conditions has been found to increase the effectiveness of wind farm operations and maintenance, particularly in offshore wind farms [5; 6; 7; 8; 9; 10].  
For these reasons, we propose developing a deep learning-based technique that can cover the surface of wind turbine generators to forecast and detect different defects on the main components such as the hub, nacelle, blades, and wind turbine tower, making wind turbine generator maintenance more efficient than manual inspection.