## 3 Transformer Tank Cover Disassembly Algorithm
### Segmentation and Disassembly of Welded Covers  
#### 3.4.1 Semantic Segmentation of Welded-Type Cover Plates  
The visible light image captured by the camera is input into the cover recognition segmentation network, where the cover and each of its components are finely segmented using the improved Short-Term Dense Concatenation (STDC) network. To better adapt to the structural characteristics of the transformer cover plate and enhance the network's perception of spatial location and geometry, this paper introduces a geometric feature assistant module and position coding, leveraging the spatial distribution characteristics of the transformer cover plate components. This approach optimizes the segmentation performance of the STDC network for various components.  
Figure 7: Diagram of the target detection effect.  
Feature Extraction: The input image \(I_{\text{input}}\) is fed into the STDC network for feature extraction, which gathers multi-scale features through multiple convolutional layers and STDC modules. Based on the traditional feature extraction, this paper introduces a geometric feature assist module, specifically designed for segmenting complex components on the surface of the transformer cover, such as screws, bushings, and release valves. The geometric feature assist module first detects geometric structures (e.g., circles, lines, edges) in the image and generates a geometric feature map \(F_{\text{geo}}\). These geometric features are fused with the multi-scale features \(F_{\text{STDC}}\) extracted through the STDC module to produce an enhanced feature map \(F_{\text{enhanced}}\). \[F_{\text{geo}}=\text{Geo\_Feature}(I_{\text{input}})\] (23) \[F_{\text{enhanced}}=F_{\text{STDC}}+\alpha\cdot F_{\text{geo}}\] (24) where \(\alpha\) is the coefficient of the geometric feature map [24].
2. Multi-scale Feature Fusion: To improve the segmentation of various components on the cover, particularly screws and sleeves with spatially fixed locations, the enhanced STDC network employs a multi-scale feature fusion strategy combined with a position coding mechanism to boost its spatial sensing capability. The position encoding module incorporates the spatial position information of each pixel \(\text{Pos}(x,y)\) into the feature map. The feature maps at different scales \(F_{s}\) are combined with the corresponding position encoding for feature fusion, resulting in a higher-resolution fused feature map \(F_{\text{fusion}}\). \[F_{\text{fusion}}=\sum_{s=1}^{S}w_{s}\cdot(F_{s}+\text{Pos}(x,y))\] (25) where \(F_{s}\) is the input feature map of the \(s\)-th scale, \(w_{s}\) is the corresponding weight, \(S\) is the number of scales, and \(\text{Pos}(x,y)\) is the position encoding of the pixel [25].
3. Sampling on the Feature Map: The extracted enhanced multi-scale features are upsampled by a full convolutional network (FCN) to produce the segmentation result image. This up-sampling process employs the inverse convolution operation and integrates both geometric features and position encoding to ensure accurate segmentation results, particularly in edge regions. \[M=\text{Deconv}(F_{\text{fusion}})\] (26) where \(M\) is the segmentation result image [25]. The cover segmentation result is shown in Figure 8.  
Figure 8: Result of the transformer cover segmentation.