# A Novel Object Detection Method of Pointer Meter Based on Improved YOLOv4-Tiny
## 3 Experiments and Discussions
### Experimental Results  
(1) Ablation experiments  
In order to verify the impact of each module in the PDM-YOLO network on the overall detection performance compared to the YOLOv4-Tiny baseline network, ablation experiments were conducted on PDM-YOLO by using \"Model X\" instead of the network name with specific network module. For example, YOLOv4-Tiny is temporarily named as Model 1. The experimental results are shown in Table 2. In the table, \"\(\check{\check{\
u}}\)\" indicates the corresponding method used; GhostNet(CBAM) indicates that the original SE attention mechanism in GhostNet is replaced by the channel attention mechanism in CBAM. From the results in Table 2, it can be seen that the GhostNet lightweight feature extraction network replaces the backbone network in YOLOv4-Tiny; it reduced the model size by 51.3%, and improved the recall rate and mAP by 4.63% and 1.31%, respectively. When the channel attention mechanism in CBAM is used to replace the SE channel attention mechanism, the features are extracted through global average pooling and global maximum pooling; although the recall rate is slightly reduced, the _mAP_ is increased by 0.37%, and the model size is reduced by 60.93% compared with the YOLOv4-Tiny network. When the improved RFB module is added to the model, the importance of the central feature of the receptive field of the model is increased; not only are the recall rate and _mAP_ increased by 1.65% and 1.19%, respectively, but the size of the model is also further reduced. The size of the model is 61.25% of the YOLOv4-Tiny network model, with the highest lightness. When the improved feature fusion is incorporated into the model, the fusion of feature maps with different scales is enhanced, the utilization rate of underlying features is improved, and the recall rate and _mAP_ are increased by 0.72% and 0.65%, respectively. When the CBAM attention mechanism is introduced into the model, the recall rate and _mAP_ can reach 78.81% and 97.82%, respectively, by increasing the attention to important features and restraining the attention to unnecessary features, so that the PMD-YOLO network can accurately detect the target of the pointer meter. The experimental results show that the improvement of YOLOv4-Tiny in this paper is effective; these improvements not only improve the detection accuracy of the model, but also reduce the size of the model, thus making the model more practical. Figure 10 shows the precision-recall (P-R) curve and recall curve generated for the PDM-YOLO with input size of 608 \(\times\) 608 on the constructed dataset of the pointer meter. In the P-R curve, the area below the curve is the value of _AP_, and it can be seen from the figure that the average accuracy of the pointer meter is 97.82%. The recall curve shows the \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline \multirow{2}{*}{**No.**} & **YOLOv4-Tiny** & **GhostNet** & **GhostNet** & **Improved** & **Improved** & **CBAM** & **Recall** & _mAP_ & **Weight** \\  & **(Base)** & **(SE)** & **(CBAM)** & **RFB** & **Fusion** & **Fusion** & **Rate (\%)** & **@0.5 (\%)** & **Size/(MB)** \\ \hline
1 & ✓ & & & & & & & 72.19 & 92.64 & 22.4 \\
2 & ✓ & ✓ & & & & & & 76.82 & 93.95 & 10.9 \\
3 & ✓ & & ✓ & & & & & 76.16 & 94.32 & 8.75 \\
4 & ✓ & & ✓ & ✓ & & & & 77.81 & 95.51 & 8.68 \\
5 & ✓ & & ✓ & ✓ & ✓ & & 78.53 & 96.16 & 9.18 \\
6 & ✓ & & ✓ & ✓ & ✓ & ✓ & 78.81 & 97.82 & 9.38 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Ablation experiments of PMD-YOLO. proportion of the number of correctly predicted target samples to the total positive samples under the condition that the confidence threshold is 0.5; it can be seen from the figure that recall is equal to 78.81%. However, under the same conditions, the _mAP_ of YOLOv4-Tiny is 92.64%, and the recall is 72.19%. These results confirm the effectiveness and superiority of the method proposed in this paper.