## 3 Methods
### Efficient Mobile Inverted Bottleneck Convolution Module  
To enhance the detection performance for isolation switches, specific filters can be employed to emphasize their structural features. Additionally, to reduce the parameter count and computational load of the backbone network, efficient and lightweight convolutions can be used to construct these filters. Consequently, this paper improves the bottleneck module in C2f by incorporating the MBConv module from EfficientNet [33], resulting in the design of an efficient and lightweight network module, EMBC, as illustrated in Figure 2.  
First, to enable the model to learn different abstract features from the input data, we applied pointwise convolution to increase the network's depth. This helps the model acquire more fine-grained features and makes it easier to train. Meanwhile, to ensure that the network remains lightweight, while capturing richer and more complex features, we utilized depthwise convolutions for feature extraction. As shown in Equations (1) and (2), \(\delta\) represents the swish activation function.  
\[\chi_{2,\zeta_{2}} =\delta\big{(}BN\big{(}PWConv(X_{1,\zeta_{1}})\big{)}\big{)} \tag{1}\] \[\chi_{3,\zeta_{3}} =\delta\big{(}BN\big{(}DWConv(X_{2,\zeta_{2}})\big{)}\big{)} \tag{2}\]  
Secondly, to enhance the spatial feature representation in different directions during isolation switch detection, we introduced the lightweight and efficient ELA attention mechanism. The ELA mechanism extracts horizontal and vertical feature vectors through strip pooling in the spatial dimension, as shown in Equations (3) and (4). Initially, each channel undergoes average pooling in both directions, maintaining an elongated kernel shape to capture long-range dependencies, while avoiding interference from irrelevant regions in label prediction. Subsequently, 1D convolutions are applied to strengthen the positional information in both the horizontal and vertical directions. Group normalization (GN) is  
Figure 2: The structure of the EMBC (efficient mobile inverted bottleneck convolution) module.  
then employed to process the enhanced positional information, representing positional attention in these directions, as demonstrated in Equations (5) and (6). Here, \(F_{h}\) represents the \(1\times 1\) convolution, and \(\sigma\) denotes the sigmoid activation function. This approach generates informative object position features in each direction. ELA independently handles these directional feature vectors for attention prediction, and then integrates them via multiplication to produce the final output Y, as shown in Equation (7). This ensures precise location information for the regions of interest, while maintaining the model's lightweight nature. Such processing is crucial for detecting the open and closed states of telescopic isolation switches.  
\[z_{-}^{h}c(h)=\frac{1}{H}\sum 0\leq i<H\kappa_{c}(h,i) \tag{3}\]  
\[z_{-}^{w}c(w)=\frac{1}{W}\sum 0\leq j<W\kappa_{c}(j,w) \tag{4}\]  
\[y^{h}=\sigma\big{(}G_{n}\big{(}F_{h}(z_{h})\big{)}\big{)} \tag{5}\]  
\[y^{w}=\sigma(G_{n}(F_{w}(z_{w}))) \tag{6}\]  
\[Y=\kappa_{c}\times y^{h}\times y^{w}, \tag{7}\]  
Lastly, a residual module is used to integrate the input features with those processed by the ELA attention mechanism, in order to enhance the feature extraction capability of the EMBC module. The residual connection allows the fusion of input features with the ELA enhanced features, effectively leveraging multi-scale feature information. With this approach, the network convergence is accelerated, so that the model is capable of effectively learning features associated with isolation switches in a short period of time. Moreover, residual connections mitigate the gradient vanishing problem, enabling higher-level features to be extracted from networks. Consequently, by incorporating both ELA and residual modules, the EMBC module is better equipped to extract features from both horizontal and vertical types of isolation switches, leading to a more accurate detection and localization.