## 3 Results  
Under the same experimental conditions, the test set was input into three different meter positioning models--CBAM-YOLOv5, CA-YOLOv5, SE-YOLOv5--as well as the original YOLOv5 model for training and testing. The test results for each model are shown in Table 2. \begin{table}
\begin{tabular}{c c} \hline \hline
**Name** & **Parameters** \\ \hline Operating System & Windows 10 \\ CPU & AMD Ryzen7 5800H \\ GPU & NVIDIA GeForce RTX3070 8G \\ RAM & 16 GB (8 GB \(\times\) 2) \\ Python & 3.7.0 \\ Pytorch & 1.13.1 \\ CUDA & 11.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Configuration Parameters for Both Software and Hardware. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Model.** & **Precision(_P_)** & **Recall (_R_)** & **mean Average Precision (_mAP_)** & **Test Time** \\  & _/\%_ & _/\%_ & _/\%_ & **/ms** \\ \hline CBAM-YOLOv5 & 89.5 & 84.7 & 90.5 & 66.1 \\ CA-YOLOv5 & 91.9 & 88.1 & 92.6 & 66.3 \\ SE-YOLOv5 & 90.3 & 86.6 & 91.4 & 65.8 \\ YOLOv5 & 88.8 & 76.4 & 88.6 & 65.3 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Test Results of Different Models. The data presented in Table 2 reveal that the integration of the attention mechanism module, while adding to the complexity of the base model, has resulted in a longer average detection time for the CBAM-YOLOv5, CA-YOLOv5, and SE-YOLOv5 models relative to the standard YOLOv5 model. However, in terms of mean average precision (\(mAP\)), the CBAM-YOLOv5, CA-YOLOv5, and SE-YOLOv5 models have seen respective increases of 1.9%, 4%, and 2.8% compared to the original YOLOv5 model. This confirms that the inclusion of the attention mechanism module can enhance the capability of extracting key features, thereby improving recognition accuracy. The P-R curves of the four target detection models are shown in Figure 9.  
From Figure 9, it is evident that the _P-R_ curve for the CA-YOLOv5 model covers the largest area, and its \(AP\) value, calculated using Equation (8), is 0.915. Based on the aforementioned four evaluation metrics, it can be seen that the CA-YOLOv5 model has stronger positioning capability in the task of instrument recognition compared to CBAM-YOLOv5, SE-YOLOv5, and YOLOv5. The positioning result of the CA-YOLOv5 target positioning model is shown in Figure 10.  
Figure 10: The Positioning Result of the CA-YOLOv5 Target Positioning Model. Figure 9: The _P-R_ Curves of the Four Target Detection Models. Combining the optimal object detection model CA-YOLOv5 with the scale recognition model, a complete meter scale recognition system can be obtained. Within this system, when an image is inputted, the object detection model CA-YOLOv5 is responsible for locating the meter and marking the location result with a red box. Subsequently, the marked section is inputted into the scale recognition model, which is in charge of extracting and correcting the dial, as well as calculating the deflection angle of the pointer, to ultimately obtain the scale reading results. After model training is complete, it is necessary to perform scale recognition on the meter data in the test set, and to tally the success rate of meter scale recognition under different error thresholds \(e\). The performance of different models is evaluated based on the average error \(w\) of the results and the time consumed. The expression for error threshold \(e\) and average error \(w\) are  
\[e=\frac{|V-V^{\prime}|}{h} \tag{10}\]  
\[w=\frac{1}{200}\sum_{i=1}^{200}e_{i} \tag{11}\]  
where \(V\) is the calculated result of the instrument scale; \(V\) is the actual recognized result of the instrument scale; \(h\) is the unit scale of the area where the instrument needle is located. The recognition results of the test set are shown in Table 3.  
In addition, binary descriptor-based SURF [25, 26, 27] and U-net technique [28, 29] are being widely deployed for recognition systems.