# Pointer Meter Recognition Method Based on Yolov7 and Hough Transform
## 4 Experiment
### Analysis of Experimental Results  
#### 4.4.1 Object Detection Evaluation Index  
mAP50, FPS, params, and Gflops are indicators to measure the performance of object detection models [35]. mAP50 (mean average precision at 50) is an indicator used to measure the performance of the object detection algorithm, indicating the average accuracy of the matching degree between the detected object and the real object, where 50 means the IoU (intersection-over-union ratio) threshold is 0.5 [36]. The higher the mAP50, the higher the detection accuracy of the algorithm [37]. FPS (frames per second) indicates the number of image frames that the object detection model can process per second, that is, the inference speed of the model. The higher the FPS, the faster the detection speed of the algorithm. params (parameters) refers to the number of parameters of the model, which is an important indicator for evaluating the complexity of the model. The fewer params, the smaller the model and lower the requirement of computing resources and storage space. Gflops (giga-floating-point-operations-per-second) indicates the number  
Figure 9: Experimental results of the intermediate steps: (**a**) Original image; (**b**) Object detection result; (**c**) Cropped image of the square table; (**d**) Overlay effect map of the image segmentation; (**e**) Predicted mask result of the image segmentation; (**f**) Thinned image; (**g**) Hough transform; (**h**) OCR result; (i) Reading.  
of floating-point operations performed by the model per second and is an indicator to measure the computational efficiency of the model [38]. The higher the Gflops, the higher the computational efficiency of the model, which can complete the reasoning task faster. Their calculation formulas are in Formulas (5)-(9) [28].  
\[\text{AP}=\frac{1}{\text{m}}\underset{\text{i}}{\overset{\text{m}}{\sum}}\text{ P}_{\text{i}}=\frac{1}{\text{m}}\times\text{P}_{1}+\frac{1}{\text{m}}\times\text{P}_{2}+ \cdots+\frac{1}{\text{m}}\times\text{P}_{\text{m}}=\int\text{P}(\text{R})\text {d}\text{R}, \tag{5}\]  
\[\text{m}\text{AP}=\frac{1}{\text{C}}(\text{AP}_{1}+\text{AP}_{2}+\cdots+\text {AP}_{\text{c}}), \tag{6}\]  
\[\text{FPS}=\frac{1}{\text{T}}, \tag{7}\]  
\[\text{params}=\text{n1}\times\text{m1}+\text{n2}\times\text{m2}+\cdots+\text {nk}\times\text{mk}, \tag{8}\]  
\[\frac{(2\times\text{n1}\times\text{m1}\times\text{h1}\times\text{w1}\times \text{d1}+}{\text{n2}\times\text{m2}\times\text{h2}\times\text{w2}\times \text{d2}+\cdots+}\]  
\[\text{Gflops}=\frac{\text{nk}\times\text{mk}\times\text{hk}\times\text{wk} \times\text{dk})}{10^{9}\times\text{T}}, \tag{9}\]  
In this experiment, we trained the model 300 times and compared the recognition effects of the five models. After analyzing the experimental results, we found that Yolov7 performed the best. The comparison of different models on the meter dataset is shown in Table 3.  
According to the table, the maps of Yolov5 and Yolov7 are above 99%, far exceeding Yolov. The Yolov7 model has the highest Map50 score of 99.8, indicating that it performs best in object detection accuracy. Although it has the highest computational resource requirements, it is still the best choice, especially when accuracy is a key metric. Yolov5-s has the least number of parameters and Gflops, so it is the most lightweight model and requires the least computational resources. Additionally, Yolov5-s has the highest FPS, so it is suitable for tasks that need to process images quickly. The choice of which model to use depends on the specific application scenario and requirements. If you need high-precision object detection and have enough computing resources, you can choose the Yolov7 model. If you need to process images quickly or have limited computing resources, you can choose Yolov5-s.