## 3 Method Implementation
### Meter Classification and Target Detection  
A convolutional neural network (CNN) is a type of feed-forward neural network characterized by a hierarchical structure that primarily consists of convolutional layers, pooling layers, and fully connected layers. CNNs have widespread applications in fields such as image classification [29], target detection [30], and face recognition [31]. The basic structure of a convolutional neural network is illustrated in Figure 8.  
In this study, the ResNet18 model--a deep residual network within the CNN framework--is utilized to classify digital and pointer meters. The ResNet model employs the architectural strategy of utilizing multiple small 3 \(\times\) 3 convolutional kernels derived from the VGG network. This approach renders the ResNet model computationally more efficient and enhances feature extraction by utilizing smaller convolutional kernels, in contrast to the larger 5 \(\times\) 5 convolutional kernels present in other networks. Residual networks leverage the concept of residuals and are structured to directly map inputs, connecting the inputs and outputs of the neural network through skip connections. Multiple skip connections are considered residual components, resulting in the formation of a deep residual neural network through the stacking of residual blocks. The operation of the residual block can be expressed as follows:  
\[F(x)=H(x)-x\] \begin{table}
\begin{tabular}{c c c c c} \hline
**Dataset Name** & **Image Category** & **Image Number** & **Data Source** & **Applications** \\ \hline UFPR- & Pointer meter & & Energy & Intelligent meter \\ ADMR(v1/v2) & images with & 5000 & Company of & reading, object \\  & labels & & Paran’a (Copel) & detection and \\  & & & & recognition \\  & & & & Intelligent meter \\ UFPR-AMR & Multi-type meter & & Energy & reading, object \\  & images with & 2000 & Company of & detection and \\  & labels & & Paran’a (Copel) & recognition, \\  & & & & OCR \\ \hline \end{tabular}
\end{table} 
Table 1: Evaluation datasets for MAMRS systems. \begin{table}
\begin{tabular}{l l} \hline
**Step** & **Description** \\ \hline
1. Data Standardization & Ensure consistent formats, naming, and annotation styles. \\
2. Data Cleaning & Remove duplicates, filter low-quality data, and normalize labels. \\
3. Distribution Analysis & Balance data distribution and address domain differences using augmentation or style transfer. \\
4. Dataset Merging & Combine datasets, split into training, validation, and test sets. \\
5. Label Harmonization & Create a unified label mapping to align labels across datasets. \\
6. Validation & Verify data integrity and visualize samples for consistency. \\
7. Documentation & Record merging steps and use version control tools like Git/DVC. \\
8. Testing & Run experiments to evaluate and refine the unified dataset. \\ \hline \end{tabular}
\end{table} 
Table 2: Dataset Combination Steps. where \(x\) is the input of residual block as well as the direct connection part; \(H(x)\) is the output of residual block; \(F(x)\) is the residual and learnable parts of the network. The internal structure of the residual block is shown in Figure 9. Conv represents the convolutional layer, BN represents the batch normalization, ReLU represents the linear correction unit of the activation function, and FC represents the fully connected layer. The ResNet18 structure chart is shown in Figure 10, and the detailed structure of ResNet18 in this study is as follows:  
Figure 8: CNN structure. Figure 9: The internal structure of the residual block. **Input layer:** The model receives RGB images as inputs with a size of \(\mathbf{224\times 224}\). The input has 3 channels corresponding to the red, green, and blue color values. The image size is critical for ensuring that the subsequent layers in the model are compatible with the architecture, particularly since the model applies several downsampling operations that reduce the spatial dimensions of the input.