# Pointer Meter Reading Recognition Based on YOLOv11-OBB Rotated Object Detection
## 4 Experimental Results
### Model Comparison Experiment
#### 4.2.2 Model Comparison Result  
This study presents a comparative evaluation of the performance characteristics of several YOLO model variants, including YOLOv8n, YOLOv9m, YOLOv10n, YOLOv11n, YOLOv11s, and YOLOv11-OBB. The analysis focuses on key metrics such as precision, recall, mean average precision (mAP@50, mAP@75, and mAP@50:90), model weight size, parameter count, and floating-point operations per second (FLOPs). Detailed results are reported in Table 1. Additionally, indicators of inference speed and frames per second (FPS) are provided in Table 2.  
The YOLOv11-OBB model presented in this study demonstrates exceptional detection performance across various evaluation metrics. In terms of Precision, all models exhibit comparable results, with the YOLOv11-OBB model ranking first with an accuracy of 98.1%, indicating a low false detection rate. Regarding Recall, all models perform well. The YOLOv11-OBB model significantly outperforms other models in the mAP@50 metric, achieving a score of 99.1%, showcasing a clear advantage in overall detection performance. Furthermore, in the mAP@75 metric, the YOLOv11-OBB model stands out with a perfor \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**No.** & **Model** & **Class** & **Precision** & **Recall** & **mAP@50** & **mAP@75** & **mAP@50:90** & **Weight Size** & **Parameter** & **FLOPs** \\  & & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(MB)** & **(MB)** & **(MB)** & **FLOPs** \\ \hline \multirow{3}{*}{1} & \multirow{3}{*}{YOLOv8n} & all & 97.7 & 99.6 & 98.8 & 96.2 & 83.7 & & & \\  & & base & 95.5 & 99.1 & 98.1 & 93.1 & 0.762 & 6.3 & 11.4 & 8.1 \\  & & pointer & 99.9 & 1 & 99.5 & 99.5 & 0.918 & & & \\ \hline \multirow{3}{*}{2} & \multirow{3}{*}{YOLOv9m} & all & 98 & 99.7 & 98.3 & 95.6 & 85.3 & & & \\  & & base & 95.9 & 99.4 & 97.0 & 92 & 78 & 40.8 & 20.0 & 76.5 \\  & & pointer & 99.9 & 1 & 99.5 & 99.2 & 92.7 & & & \\ \hline \multirow{3}{*}{3} & \multirow{3}{*}{YOLOv10n} & all & 97.5 & 99.7 & 98.5 & 95.7 & 83.2 & & & \\  & & base & 95.3 & 98.9 & 97.5 & 92.1 & 75.7 & 5.8 & 10.3 & 8.2 \\  & & pointer & 99.9 & 1 & 99.5 & 99.4 & 92.2 & & & \\ \hline \multirow{3}{*}{4} & \multirow{3}{*}{YOLOv11n} & all & 97.7 & 99.7 & 98.8 & 96 & 83.4 & & & \\  & & base & 96.2 & 99.3 & 98.2 & 92.5 & 76 & 5.5 & 10.3 & 6.3 \\ \cline{1-1}  & & pointer & 1 & 1 & 99.4 & 99.4 & 92.5 & & & \\ \hline \multirow{3}{*}{5} & \multirow{3}{*}{YOLOv11s} & all & 97.8 & 99.6 & 98.7 & 96.5 & 85.0 & & & \\ \cline{1-1}  & & base & 95.8 & 99.2 & 97.9 & 93.6 & 77.4 & 19.2 & 9.41 & 21.3 \\ \cline{1-1}  & & pointer & 99.9 & 1 & 99.5 & 99.4 & 92.6 & & & \\ \hline \multirow{3}{*}{6} & \multirow{3}{*}{YOLOv11-OBB} & all & 98.1 & 99.7 & 99.1 & 98.7 & 88.7 & & & \\ \cline{1-1}  & & base & 96.2 & 99.4 & 98.7 & 98.1 & 88.1 & 6.1 & 10.6 & 6.6 \\ \cline{1-1} \cline{2-11}  & & pointer & 1 & 1 & 99.5 & 99.4 & 89.3 & & & \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of performance indicators of different models. mance of 98.7%. In the mAP@50:95 metric, the YOLOv11-OBB model also demonstrates robust detection accuracy under different Intersection over Union (IoU) thresholds, scoring 88.7%. The YOLOv11-OBB model also performs excellently in the point and base categories across the aforementioned metrics. The YOLOv9m model exhibits the largest weight file size (40.8 MB) and parameter count (75.8 MB) among the models examined. Conversely, the YOLOv11n model presents a smaller weight size (5.5 MB) and parameter count (10.3 MB). The model proposed in this study achieves a weight size and parameter count of 10.6 MB, striking a favorable balance between performance and lightweight design. Floating-point operations (FLOPs) are key in evaluating a model's computational complexity. The YOLOv9m model has the highest FLOPs at 76.5G, indicating the most computational demand among the models assessed. In contrast, the YOLOv11n-OBB model records only 6.6G FLOPs, significantly lower than YOLOv9m and surpassing YOLOv8n (8.1 G), YOLOv10n (8.2 G), and YOLOv11s (21.3 G).