## 4 Algorithm for Disassembly of Internal Transformer Components  
### Semantic Segmentation of Multimodal Transformer Components  
In the automated disassembly of transformers, the semantic segmentation of internal components is crucial for ensuring accurate disassembly. The internal structure of a transformer is complex, containing various components such as screws, copper plates, winding studs, and silicon steel sheets, each differing significantly in physical location and geometry. Therefore, achieving efficient multimodal component recognition and segmentation is a key objective for the automated system. To address this challenge, this paper proposes a semantic segmentation scheme for multimodal components based on the ODIN model. The ODIN model utilizes a design centered on cross-view feature fusion, enabling the effective processing of multi-view data through alternating 2D in-view fusion and 3D cross-view feature fusion [38]. In the transformer disassembly scenario, the system captures internal images of the transformer using a multi-view RGB-D camera, combining these data with depth information to extract and fuse 2D and 3D features at different scales. This model maintains high accuracy in complex component recognition tasks, particularly when multi-view information is incomplete or data are missing, as ODIN's feature fusion technique significantly enhances component recognition accuracy. The ODIN model is employed for 2D and 3D semantic segmentation, incorporating cross-view feature fusion techniques. Transformer data are captured using RGB-D cameras and IMUs. The model leverages multi-scale feature fusion, which enhances the detection of targets of different sizes and improves generalization performance across multiple scenes. During transformer disassembly, the ODIN model enables accurate 2D and 3D instance segmentation and the identification of components such as screw support columns that need to be cut, ensuring high-precision target localization and segmentation to support subsequent disassembly operations. The ODIN model structure is shown in Figure 9, and its functionality is achieved through the following key components:  
1. Alternative 2D and 3D Information Fusion: Two-dimensional feature fusion occurs within each image view, which is subsequently projected into 3D space for cross-view feature fusion. Finally, the fused 3D features are reprojected back to the 2D plane. The 2D feature fusion is conducted using pixel coordinates, while 3D feature fusion utilizes spatial coordinates for positional encoding to ensure information consistency. 2. Multi-scale Feature Fusion: ODIN conducts feature fusion at different scales to enhance the detection of targets of various sizes. This is accomplished by utilizing multi-scale feature maps during both 2D and 3D feature fusion, ensuring that targets of different sizes are effectively detected and segmented. In the 2D feature extraction process, multi-scale feature maps are extracted using Swin Transformer [39]. These feature maps are fused at multiple scales:  
\[F_{\text{fused}}=\sum_{s=1}^{S}w_{s}\cdot F_{s} \tag{32}\]  
where \(F_{s}\) represents the feature map of the \(s\)-th scale, \(w_{s}\) denotes the corresponding weight, and \(S\) is the number of scales. In the 3D feature fusion process, the KNN algorithm is used to select neighboring points in 3D space, with feature fusion achieved through relative position encoding. Figure 9: The process of the ODIN modeling transformer. \[\text{Attention}(Q,K,V)=\text{Softmax}(\frac{QK^{T}}{\sqrt{d_{k}}}+\text{PE}_{3D})V \tag{33}\]  
where \(Q\), \(K\), \(V\) are the Query, key, and value vector, respectively. \(\text{PE}_{3D}\) is the position encoding of the 3D space. 3. Target Detection and Feature Classification: Target detection using the ODIN model predicts the category and location of the component by the following equation. \[Y=\sigma(W\cdot F_{\text{fusion}}+b)\] (34) where \(W\) and \(b\) are the weight and bias of the model, respectively, and \(F_{\text{fusion}}\) is the fused feature map.