# A Novel Object Detection Method of Pointer Meter Based on Improved YOLOv4-Tiny
## 3 Experiments and Discussions
### Experimental Results
(2) Comparison experiments with other models  
In order to verify the superiority of PMD-YOLO, we selected several target detection algorithms and compared their performances with that of PMD-YOLO under the same dataset. These algorithms include the classical two-stage object detection algorithm (Faster RCNN), one-stage object detection algorithm (YOLOv4) and its related improved algorithms, and YOLOv5-s with small volume. The results of the comparison experiments are shown in Table 3.  
It can be seen from Table 3 that YOLOv4-MobileV3 has the best overall performance among network models whose backbone network is replaced by the MobileNet series networks; therefore, YOLOv4-MobileV3 represents the YOLOv4 model whose backbone is MobileNet series networks. From the data in Table 3, it can be seen that although the recall rate and \(F1\) of PMD-YOLO are slightly lower than other individual algorithms, PMD-YOLO has obvious advantages in detection accuracy, number of computed parameters, and weight size. The precision of PMD-YOLO is 99.17%, which is 5.64%, 0.31%, 0.53%, and 3.11% higher than that of YOLOv4, YOLOv4-MobileV3, YOLOv4-Tiny, and YOLOv5-s, respectively; the _mAP_ of PMD-YOLO is 97.82%, which is 4.4%, 2.2%, 5.18%, and 1.1% higher than that of  
Figure 10: Curves of precision–recall and recall. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Model** & **Backbone** & \begin{tabular}{c} **Precision** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **Recall** \\ **(\%)** \\ \end{tabular} & \begin{tabular}{c} **\(F1\)** \\ **(\%)** \\ \end{tabular} & \
\begin{tabular}{c} **\(mAP\) (\%)** \\ \end{tabular} &
\begin{tabular}{c} **Weight Size** \\ **(MB)** \\ \end{tabular} & **Parameter** \\ \hline Faster RCNN & Resnet50 & 84.86 & 98.34 & 0.91 & 98.31 & 102 & \(136\times 10^{6}\) \\ YOLOv4 & CSPDarknet53 & 93.53 & 73.85 & 0.85 & 93.42 & 244 & \(63.9\times 10^{6}\) \\ YOLOv4-MobileV1 & MobileNetV1 & 98.12 & 79.42 & 0.88 & 94.67 & 51.1 & \(12.3\times 10^{6}\) \\ YOLOv4-MobileV2 & MobileNetV2 & 96.64 & 79.75 & 0.88 & 94.27 & 46.5 & \(10.4\times 10^{6}\) \\ YOLOv4-MobileV3 & MobileNetV3 & 98.86 & 80.42 & 0.89 & 95.62 & 53.7 & \(11.3\times 10^{6}\) \\ YOLOv4-Tiny & CSPDarknet53-tiny & 98.64 & 72.19 & 0.83 & 92.64 & 22.4 & \(5.9\times 10^{6}\) \\ YOLOv5-s & CSPDarknet & 96.06 & 80.74 & 0.90 & 96.72 & 27.1 & \(7.1\times 10^{6}\) \\ PMD-YOLO & I-GhostNet & 99.17 & 78.81 & 0.88 & 97.82 & 9.38 & \(3.1\times 10^{6}\) \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Performance comparison of PMD-YOLO and other detection algorithms. YOLOv4, YOLOv4-MobileV3, YOLOv4-Tiny, and YOLOv5-s, respectively. Meanwhile, the weight volume and calculation parameters of PMD-YOLO are also minimized; the weight volume of PMD-YOLO is reduced to 9.38 MB, which is 3.8%, 17.5%, 41.9%, and 34.6% of that of YOLOv4, YOLOv4-MobileV3, YOLOv4-Tiny, and YOLOv5-s, respectively; the computational parameters of PMD-YOLO are reduced to 3.1 \(\times\) 10\({}^{6}\), which is 4.9%, 27.4%, 52.5%, and 43.7% of that of YOLOv4, YOLOv3, YOLOv4-Tiny, and YOLOv5-s, respectively. In addition, the two-stage object detection algorithm Faster RCNN is 0.49% higher than PMD-YOLO in terms of _mAP_, but the weight volume and the amount of computational parameters are too large to be applicable on mobile devices. In a comprehensive view, PMD-YOLO effectively improved the feature extraction ability of the algorithm by replacing the backbone network, introducing CBAM, improving the RFB module, and optimizing the feature fusion method, while reducing the computational parameters of YOLOv4-Tiny, and the object detection effect was significantly improved. In this paper, YOLOv4-Tiny, YOLOv5-s, and PMD-YOLO, which are also lightweight networks, were chosen as the experimental groups to compare the detection effect of pointer meters in real scenes, and Figure 11 shows the detection effect of the above three models for pointer meter in complex background scenes.