# A Novel Object Detection Method of Pointer Meter Based on Improved YOLOv4-Tiny
## 4 Conclusions  
In this paper, a one-stage lightweight detection model that can be used for pointer meters was proposed for the purpose of optimizing the model weights while improving the detection performance of traditional object detection models in complex real-world environments. In terms of model weight quantification, a lightweight network named GhostNet and an improved RFB module were introduced to significantly reduce the number of network model parameters; in terms of detection performance improvement, a new channel attention mechanism was introduced in GhostNet, the feature fusion stage was improved to enhance the feature utilization, and two CBAMs were added in the feature fusion stage to increase the feature extraction capability for small- and medium-sized objects. In order to verify the effectiveness and superiority of the model proposed in this paper, an experimental study of the proposed method was carried out with the self-made pointer meter dataset as the research object, and a comparative experimental study was  
Figure 13: Comparison detection results of Hough circle transform and PMD-YOLO. The object confidence scores in (**c**,**d**) are shown from left to right: (**c**) shows 0.99, 0.99, and 0.99; (**d**) shows 0.91 and 0.72.  
carried out with other similar methods. The experimental results show that the overall performance of the proposed model has obvious advantages; the _mAP_ of the model can reach 97.82%, and the weight of the model is only 9.38 M. This shows that the model proposed in this paper not only improves the detection accuracy of the pointer meter, but also reduces the size of the model. The model can be easily deployed on mobile devices to meet the needs of practical applications.  
In experiments, it was also observed that PMD-YOLO's performance would decline when it detected too-small objects or objects taken in images with poor light and angle conditions. To overcome these limitations, these problems will be further studied in the future work to reduce the negative impact of these conditions on the model. Furthermore, since the model introduces depthwise separable convolution, there is a partial performance degradation in terms of detection speed, but it still has high real-time detection capability. In future work, the network structure can be further optimized to improve the detection speed of the model while reducing the model size and improving the detection accuracy.  
W.X: Methodology, formal analysis, software, validation, and writing--original draft. W.W.: Funding acquisition, project administration, formal analysis, and validation. J.R.: Methodology, formal analysis, investigation, and writing--review and editing. C.C.: Funding acquisition, methodology, project administration, and writing--review and editing. Y.X.: Formal analysis, investigation, and writing--original draft. All authors have read and agreed to the published version of the manuscript.  
This work was supported by the Nature Science Foundation of Hebei Province grant no. E2020402060, Special Project for Transformation of Major Technological Achievements in Hebei Province grant no. 22293601Z, and Key Laboratory of Intelligent Industrial Equipment Technology of Hebei Province (Hebei University of Engineering) under grant no. 202206.  
Data sharing not applicable.  
The authors declare no conflict of interests.