## 3 UAV Specification and Wind Farm Specification
### Proposed Technique for Wind Turbine Blade Defect Detection  
1. Proposed Model for Feature Extraction  
The Res-CNN3 is made up of three convolutional neural networks that are identical and concatenated, including residual blocks with skip connections. The neural network's skip connections obtain activation from the top layer and direct it to a lower layer. As a result, it is possible to train deeper networks in a more straightforward manner [15]. 2. Defect Classification Deep Neural Network  
With the temporal channel complexity simplification design, we can process the wind turbine blade image through the same number of channels more than once, which increases handling efficiency for complex features and precision in identifying the RGB delta across the wind turbine blade image, while requiring the least amount of computational resources on the training and testing platforms. The applied technique is referred to as Temporal Channel Complexity Simplification (or TCCS). (3) Defects Regions Detection of Wind Turbine Blades  
This function of detecting defect regions in wind turbine blade images uses a bipartite approach to determine whether or not the wind turbine blade image contains RGB delta indicative of wind turbine blade defects. This commences detailed and definitive defect region object detection. Thus, wind turbine blade defects detection must come first. The wind turbine blade images' anomaly detection process is refined thanks to the thoughtful creation and alteration of the suggested technique for defect classification and feature extraction. The first layers of the suggested feature extraction model are designed to extract the features of the image with the damaged wind turbine blade. Because the drone flight across the individual wind turbine generators installed in the wind farm represents arrays of wind turbine blades rather than individual wind turbine blades, individual wind turbine blade objects do not typically occupy the entire image, but rather, multiple wind turbine blade objects connected across different WTGs in the wind farm do. This feature of the dataset inspired the design of the DCNN for detecting defects regions in wind turbine blades. Instead of relying on multi-scale computation due to some multi-scale features and stacking due to objects occupying the entire image, we rely on averaging and max-pooling. Logistic regression log(\(\cdot\)) is used as the loss function. Thus, the following explanation can be given for the objective function:  
\[L^{d}\Big{(}\left\{x_{i}^{d},y_{i}^{d}\right\};W^{d}\Big{)}=y_{i}^{d}log\Big{(} p\left(y_{i}^{d}\left|x_{i}^{d};W^{d}\right.\right) \tag{1}\]  
Figure 2: Review of images of wind turbine blades with different defects. p(\(\cdot\)) means the probability of the corresponding model. 4. Defect region object detection of wind turbine blade Once the wind turbine blade defects region detection has occurred, the more detailed and categorical defects region object detection follows. To accomplish this, the Selective Search (SS) algorithm [16; 17] is used to place several Rols in the laser image of the wind turbine blade. The laser and RGB region object, along with the laser and RGB image and the Rols, will then be detected by the defects region object detection model. Finally, the proposed technique's final output determines and predicts the classification and location of each RoI. In general, the scale of the complete laser and RGB image of the wind turbine blade is much larger than the scale of the defective wind turbine blade regions in the laser image. The RoIs in the input wind turbine blade laser image are predicted by the Selective Search (SS) algorithm. The defects of the wind turbine blade are located within the area of these RoIs via RGB delta. Selective Search is thus very useful for defects region object detection. Each RoI is represented by a Bounding Box based on the SS's region proposal coordinates.