# An Insulator Fault Diagnosis Method Based on Multi-Mechanism Optimization YOLOv8
## 2 Model and Methods
### CARAFE Lightweight Operator  
The CARAFE (Content-Aware ReAssembly of Features) module [24] addresses the limitations of traditional feature up-sampling methods in retaining details and reconstructing semantic information. Its primary function is to perform up-sampling on the input feature maps while ensuring the integrity of semantic information. Its structure is illustrated in Figure 6.  
Figure 5: CW-DRB structure and DRB principle.  
Figure 6: CARAFE operator schematic.  
The CARAFE module has three main advantages: (1) It has a large receptive field, allowing it to aggregate contextual information. (2) It is lightweight, adding minimal computational overhead, and can be flexibly integrated into network architectures. (3) It has content-aware processing capabilities, enabling CARAFE to dynamically generate adaptive kernels to process features from different samples. As shown in Figure 5, the up-sampling principle of CARAFE consists of two steps: the first step involves predicting specific adaptive kernels based on the content at each target location, and the second step involves feature reconstruction through kernel reassembly to obtain the up-sampled feature map. For a feature input \(H\times W\times C\) of a given size and an up-sampling ratio \(\sigma\in Z\), a feature map of size \(\sigma H\times\sigma W\times C\) is generated by the kernel prediction module. The coordinates of the target point in the output feature map correspond to the original coordinates, and their coordinate relationship is shown in Equation (3) [24].  
\[\left\{\begin{array}{l}l=(i,j),l^{\prime}=(i^{\prime},j^{\prime})\\ i=\frac{i^{\prime}}{\sigma},j=\frac{i^{\prime}}{\sigma}\end{array}\right. \tag{3}\]  
In the equation, \(\sigma\) represents the down-sampling ratio, \(l\) denotes the coordinates before up-sampling, \(l^{\prime}\) denotes the coordinates after up-sampling, \((i,j)\) represents the original coordinates before sampling, and \((i^{\prime},j^{\prime})\) denotes the coordinates after sampling.  
Taking the target point as an example, the kernel prediction module works by first adjusting the number of channels by performing a \(1\times 1\) convolution on the input features. Then, a recombination kernel is generated by content encoding, and a recombination kernel of size \(k_{up}\times k_{up}\) is generated for the point after normalizing the kernel using the softmax function. Finally, feature recombination is performed for all points in the region \(N\big{(}Xl,k_{up}\big{)}\) centered on the point. The output result is shown in Equation (4) [24].  
\[X^{\prime}_{l^{\prime}}=\sum_{n=-r}^{r}\sum_{n=-r}^{r}W_{l^{\prime}(n,m)}\cdot X _{(i+n,j+m)};r=\frac{k_{up}}{2} \tag{4}\]  
where \(W_{l^{\prime}(n,m)}\) denotes the reorganization kernel of the point, \(X^{\prime}_{l^{\prime}}\) is the post-output feature map, and \(X_{(i+n,j+m)}\) is the pixel point of the neighboring region. \(k_{up}\) is the size of the reorganized kernel. Therefore, to address challenges such as narrow target perception range within images, insufficient fault feature details, and suboptimal feature information utilization, incorporating CARAFE to improve YOLOv8 enhances the model's ability to better integrate semantic information from different regions and strengthens the model's capability to extract detailed fault features.