## 4 Algorithm for Disassembly of Internal Transformer Components
### Feature Extraction and Classification  
In the cutting process of transformers, feature extraction, fusion, and target detection techniques are crucial for achieving high-precision automated cutting. Firstly, for each transformer component image, multi-scale feature extraction is performed on the RGB image \(I_{xy}\) using a CNN to generate feature maps \(F_{s}^{xy}\) at different scales, represented as  
\[F_{s}^{xy}=\text{CNN}_{s}(I_{xy}) \tag{38}\]  
This multi-scale feature extraction method can effectively capture the surface information of the component, providing a foundation for subsequent 3D feature mapping. After completing the 2D feature extraction, the 2D features are projected into the 3D space using the depth map \(D_{xy}\) along with the internal and external parameter matrices \(K\) and \(RT\) sum of the camera. The coordinate points in the 3D space are obtained as follows:  
\[P_{3D}=K^{-1}\cdot(RT)^{-1}\cdot D_{xy} \tag{39}\]  
With this step, the features are converted from a 2D plane to a 3D point cloud, ensuring an accurate correspondence with the physical space.  
To enhance the spatial consistency of the 3D features, a KNN algorithm is employed to identify the neighboring points of each 3D point. The features are then fused by combining the positional encoding and attention mechanisms.  
\[F_{3D}^{\text{fused}}(P_{3D})=\sum_{p\in N(P_{3D})}\text{Attn}(F_{3D}(P_{3D}), F_{3D}(P_{j}),PE(P_{3D},P_{j})) \tag{40}\]  
The method can effectively integrate spatial information and is particularly suitable for fusing features of complex transformer structures, such as the contact surface of windings and silicon steel sheets.  
After feature fusion, the ODIN deep learning model is leveraged to perform target detection on the fused 3D features, identifying the key components of the transformer. For the detection of screws, the model predicts them using the following equation:  
\[Y_{\text{screw}}=\sigma(W\cdot F_{3D}^{\text{fused}}(P_{3D})+b) \tag{41}\]  
This process outputs the target location and category, providing accurate coordinate information for subsequent cutting path planning.  
Although the basic process of feature extraction and target detection is consistent, the characteristics of different transformer parts lead to varying complexities in the detection methods. For simpler parts like screws and copper plates, feature extraction mainly relies on surface information. In contrast, for windings and studs, the structural complexity necessitates higher accuracy to capture intricate details. The contact surface between the silicon steel sheet and the winding requires consideration of its spatial contact characteristics, relying on positional encoding in 3D feature fusion to ensure accurate detection.  
Therefore, in the transformer cutting task, the accurate identification of different components is achieved by effectively combining 2D and 3D information along with utilizing feature extraction and target detection techniques. The structural differences among components necessitate the application of varied feature extraction and detection strategies, ultimately enabling efficient automated cutting.