## 2 Transmission Line Ice Cover Monitoring System
### Multi-Dimensional Dynamic Convolution  
Conventional convolutions use a single static kernel that is independent of the input samples. In contrast, dynamic convolution employs multiple convolution kernels with linear weighting, where the weighting values depend on the input. This input dependency gives dynamic convolution greater flexibility than traditional static convolutions. By learning linear combinations of multiple convolution kernels and input-related attention weights, dynamic convolution can significantly enhance the accuracy of CNNs, providing higher parameter efficiency and feature extraction capability. As shown in Figure 4, the multi-dimensional dynamic convolution introduces a multi-dimensional attention mechanism using a parallel strategy to learn more flexible attention across the four dimensions of the convolution kernels. For each kernel, attention weights are assigned to the convolution parameters in the spatial domain, the convolution filters of the input channels, the convolution filters of different output channels, and the overall n convolution kernels. Gradually applying different attention weights along the dimensions of position, channel, filter, and kernel will allow the convolution operations to exhibit variability across all dimensions with respect to the input, enhancing the performance in capturing rich contextual information. Standard convolutional layers have a static kernel that applies to all input samples. In contrast, the dynamic convolution layer employs dynamic weighting using a linear combination of n convolution kernels combined with an attention mechanism, making  
Figure 4: Full-dimensional dynamic convolutional feature fusion module. the convolution operation dependent on the input. The traditional dynamic convolution operation can be defined as follows:  
\[x_{out}=x\sum_{i=1}^{n}a_{ni}W_{i} \tag{6}\]  
In the above equation, \(X\) represents the input features of \((h,w,c_{in})\), and \(x_{out}\) represents the output features of \((h,w,c_{out})\). \(h\) represents the height of the input feature map, \(w\) represents the width of the input feature map, \(c_{in}\) represents the number of channels of the input picture, and \(c_{out}\) represents the number of channels of the output picture. \(W_{i}\) indicates the \(i\)-th output filter kernel, and \(a_{wi}\) represents the attention coefficients. However, traditional dynamic convolution only involves convolution kernels and attention weight for the filter kernels. This method overlooks other adjustable parameters that affect convolution and multiplies the convolution parameters by n, resulting in inefficiency. The implementation of multi-dimensional dynamic convolution is described as follows:  
\[x_{od\_out}=x\sum_{i=1}^{n}\left(a_{wi}\odot a_{fi}\odot a_{ci}\odot a_{si} \odot W_{i}\right) \tag{7}\]  
In this formula, \(a_{wi}\), \(a_{fi}\), \(a_{ci}\), and \(a_{si}\) are the attention coefficients for the spatial, channel, filter, and kernel dimensions, respectively. The four types of attention weights ensure that the convolution operation considers every spatial position for different inputs, all input channels, all filters, and all kernels, thereby providing performance guarantees for capturing rich contextual cues, and where \(\odot\) stands for the Hadamard product, which is an element-by-element multiplication operation applicable to vector or matrix operations. 2. Full-Dimensional Feature Fusion ODC2F  
In the baseline model, while the C2F module plays a key role in cross-stage feature fusion, it may also blur or overlook some important feature information during this process, negatively impacting the model's performance. Moreover, it requires the learning of weights and biases for feature fusion, introducing a substantial number of additional parameters, which may lead to risks of overfitting.