# A Novel Object Detection Method of Pointer Meter Based on Improved YOLOv4-Tiny
## 3 Experiments and Discussions
### Experimental Results
In the actual scene 1 shown in Figure 11a-c, all three models do not produce any missed detection due to small objects, but the recognition rate of PMD-YOLO for each object is higher than that of the other two algorithms. In the actual scene 2 shown in Figure 11d-f, the meter detection results of YOLOv4-Tiny have serious missed detection, and the meter detection results of YOLOv5-s and PMD-YOLO also have missed detection, but such too-small objects lose their reading significance because the too-small meters are not able to detect other features required for meter reading, and PMD-YOLO is higher than YOLOv5-s in terms of accuracy. It can be seen from the experimental comparison results that the detection results of PMD-YOLO are closer to the minimum external rectangle of the meter target, which indicates that PMD-YOLO can detect the position of the meter more accurately. Figure 12 shows the detection results of three models under overexposure, under-exposure, and oblique angle shooting conditions. Under overexposed environments,  
Figure 11: Comparison detection results of three models on pointer meters in different complex background scenes. The object confidence scores in each graph are shown from left to right: (**a**) shows 1.00, 1.00, 0.98, and 0.83; (**b**) shows 0.92, 0.95, 0.82, and 0.81; (**c**) shows 1.00, 0.99, 0.99, and 0.87; (**d**) shows 0.56; (**e**) shows 0.81, 0.78, and 0.55; (**f**) shows 0.99, 0.77, and 0.63. YOLOv5-s's bounding box was unaffected, but there was one false positive. The other two algorithms showed a slight offset of the bounding box. Under underexposed environments, YOLOv4-Tiny missed one target. YOLOv5-s and PMD-YOLO were able to recognize the tilted meter, but their detection accuracies also suffered a minor impact. Under oblique angle shooting conditions, YOLOv4-Tiny missed one target again, and the detection accuracy of all three models was affected to varying degrees. However, PMD-YOLO maintained a relatively high level of recognition accuracy overall. In summary, YOLOv4-Tiny and YOLOv5-s, respectively, missed and falsely detected targets under various adverse conditions, indicating low robustness. PMD-YOLO also experienced slight deviations and decreases in prediction accuracy under various adverse conditions, but it can be seen that PMD-YOLO has good overall detection performance. (4) Comparison with traditional methods  
In order to show the advantages of the method proposed in this paper compared with traditional target detection methods, the comparative experiment of Hough circle transform and PMD-YOLO in different real scenes was carried out, and the experimental results are shown in Figure 13. In order to show that Hough circle transform is more easily  
Figure 12: Comparison detection results of three models on pointer meter in different conditions; (**a–c**) are the results of overexposure shooting; (**d–f**) are the results of underexposed shooting; (**g–i**) are the results of oblique angle shooting. The object confidence scores in each graph are shown from left to right: (**a**) shows 0.99 and 0.87; (**b**) shows 0.75, 0.93, and 0.93; (**c**) shows 0.98 and 0.93; (**d**) shows 0.98; (**e**) shows 1.00 and 0.76; (**f**) shows 0.96 and 0.76; (**g**) shows 0.52 and 0.76; (**h**) shows 0.62, 0.90, and 0.56; (**i**) shows 0.86, 0.76, and 0.89.  
affected by interference information in environments with quasi-circular features, occlusion, and other interference factors, while PMD-YOLO has stronger anti-interference ability, the application scene completely different from Figure 11 was selected during the experiment. It can be seen from Figure 13 that when Hough circle transform is used to detect the meter, it is very easily interfered with by other circular features in the picture, and when the meter part is partially occluded, the position deviation of the detected target will be large.