## 3 Method
### Meta-Learner  
As the most important module in the meta-learning-based object detection model, the meta-learner generates class prototypes as meta-features for each target class by extracting sufficient support set features to assist the model in classifying ROI features of query set. However, the overall features of the defective components of the power transmission line are similar to those of the normal components. The overall features of normal components have already been learned in meta-training. However, treating novel classes as completely new classes unrelated to base class in meta-test not only results in learning insufficiently the features of defective parts but also adds unnecessary redundancy to the predictor head.  
We designed a meta-learner with the Defect Feature Reconstruction Module (DFRM) as its core according to the characteristics of defect detection in power transmission lines. By learning the channel differences between normal and defective components, the meta-learner reconstructs the meta-features to improve the attention of the predictor head to the channel relevant to defect class in the query set ROI features.  
As shown in Figure 4, we perform global average pooling on the support set features to obtain a one-dimensional vector \(f_{s}^{i}\), which is used as channel-level weights to reweight the original support set feature and enhance the representation of potential important features. Then, the output features are fed into the global average pooling layer to be compressed into one-dimensional features as meta-features, which will conduct channel-wise dot product with the query set ROI features in order to achieve channel feature selection. The one-dimensional vector \(f_{s}^{i}\) is the support set one-dimensional vector mentioned in the previous section, and the reason for choosing global average pooling to squeeze features is that this operation can retain more class-related information. To prevent confusion between meta-features of different classes, which would lead to ambiguity in classification task of predictor head, we adopted the Meta Loss from Meta R-CNN. The meta-features fed into a fully connected network for classification and adjusted the weights of the meta-learner through the feedback from loss function to enhance the distinguishability between different meta-features.  
Figure 4: Architecture of Meta-Leaner.  
To improve the detection of differences between normal and defective components in the same category, the differences mainly exist in a few channels of image features. In the meta-testing phase, we proposed the Defect Feature Reconstruction Module (DFRM) to calculate the difference values between the corresponding meta-features of normal and defective components on each channel. The DFRM multiplies the vector of difference values with the meta-feature of normal components to enhance the values corresponding to channels with different values that are large. After fusing with the ROI features of query set, the predictor head can pay more attention to the channels related to defects.  
First, the element-wise subtraction between normal and defective meta-features is computed, followed by taking absolute value to obtain the initial difference values. Then, through standardization and _ReLU_ as activation function, the small difference values are suppressed to zero as noise, which could reduce their impact on distinguishing the defect-related features in predictor head. The formula is shown in Equation (3), where \(F_{B}\) represents the meta-feature of base class, \(F_{N}\) refers to the meta-feature of novel class, and \(F_{R}\) represents the reconstructed meta-feature.  
\[F_{R}=ReLU(Stand(|F_{B}-F_{N}|))\times F_{B}+F_{B} \tag{3}\]