## 3 Methods
### Efficient-RepGDFPN Feature Fusion Network  
In the detection of isolation switch states, variations in the sizes of isolation switches pose a challenge, particularly for distant switches, where significant detail loss occurs due to their smaller dimensions. To enhance the accuracy of multi-scale isolation switch detection, while maintaining a lightweight model, we improved the efficient-RepGFPN feature fusion network by incorporating the GSConv and DySample modules [44], resulting in a more lightweight version, efficient-RepGDFPN. Our improvements were based on two key considerations: First, the original efficient-RepGFPN utilizes standard convolutions for feature extraction, which does not meet the requirements for a lightweight model design. Second, the original network performs feature fusion by concatenating feature maps, a method that directly combines different input feature maps, without any distinction. However, in object detection networks, input feature maps have varying resolutions and contribute differently to the final fused output. Therefore, simply concatenating them may not be the optimal approach. To address this, we proposed a more efficient feature fusion network Efficient-RepGDFPN, designed to serve as the bottleneck stage of the detection model. This network meets the requirements for a lightweight object detection network in the context of isolation switch status recognition, by providing an enhanced feature fusion mechanism. The overall structure of this network is illustrated in Figure 3. Compared to the original efficient-RepGPPN feature fusion network, the key improvements of efficient-RepGDFPN are as follows:Firstly, we introduce the lightweight convolution module GSConv, which integrates standard convolution, depthwise convolution, and channel shuffle operations. This module achieves a feature extraction performance comparable to standard convolution at a significantly lower computational cost, effectively reducing the model's burden, while maintaining a high performance. As illustrated in Figure 4, when input feature maps are fed into the GSConv convolution module, the process is as follows: First, a standard convolution performs channel transformation, outputting feature maps with \(c_{2}/2\) channels. Next, depthwise convolution is applied, also producing feature maps with \(c_{2}/2\) channels. Finally, these two feature maps, which have the same number of channels, are concatenated and passed through a channel shuffle operation to achieve information fusion between the feature channels. Secondly, we introduce a weighted feature fusion method, fast normalized fusion, that enables the network to more efficiently learn the importance of different input features by performing distinct fusion based on their significance. As shown in Equation (8), fast normalized fusion scales each normalization weight to the range [0,1], while offering faster training speeds. \[O=\sum_{l}\frac{w_{l}}{\epsilon+\sum_{j}w_{j}}\cdot l_{i} \tag{8}\]  
Thirdly, in the upsampling section, a highly efficient and ultra-light dynamic upsampler, DySample, is utilized. Unlike conventional kernel-based dynamic upsampling methods, DySample is designed from a point-sampling perspective. It consumes fewer  
Figure 4: The GSConv convolutional structure. Figure 3: Structure of the efficient-RepGDFPN feature fusion network. computational resources and demonstrates superior performance in terms of latency, training memory, training time, GFLOPs, and parameter count compared to other upsampling methods. As illustrated in Figure 5, given a feature map \(\mathcal{X}\) with dimensions \(C\times H_{1}\times W_{1}\) and a sampling set \(\mathcal{S}\) with dimensions \(2\times H_{2}\times W_{2}\) (where the first dimension of 2 represents the x and y coordinates), the \"grid sample\" function uses the positions in \(\mathcal{S}\) to perform bilinear interpolation on the assumed \(\mathcal{X}\), generating an upsampled feature map \(\mathcal{X}^{\prime}\) with dimensions \(C\times H_{2}\times W_{2}\).