# A Novel Object Detection Method of Pointer Meter Based on Improved YOLOv4-Tiny
## 1 Introduction
The one-stage method is used to view the object detection problem as a regression problem, and directly generates the class probability and location coordinate of the object without the region suggestion stage, so the final detection results can be obtained by a single detection. One-stage object detection methods are represented by single-shot detector (SSD) [11], Retina-Net [12], EfficientNet [13], and YOLO series algorithms [14; 15; 16; 17], which have shown their advantages on both VOC datasets [18] and COCO datasets [19]. The one-stage object detection algorithm is not as accurate as the two-stage object detection algorithm, but the detection speed is better and it can accomplish the real-time detection of the object, so in the application of pointer meter detection, the one-stage detection method is generally used. [PERSON] et al. [20] used the histogram normalized transformation algorithm to enhance the brightness and contrast of the image, and then used YOLOv3 to detect and obtain the position of the meter. [PERSON] et al. [21] used YOLOv5 to detect the meter and cropped the detected meter, and then used mathematical methods to read the pointers, scales, and meter centers of the cropped meter by secondary detection. [PERSON] et al. [22] proposed an improved algorithm based on YOLOv3 to change the feature pyramid network (FPN) structure from the original unidirectional fusion to bidirectional fusion, and introduced Smooth-EloU Loss to improve the algorithm's small object detection accuracy. [PERSON] et al. [23] proposed an improved algorithm named YOLO-SASE (self-adaption squeeze-and-excitation) based on the YOLO framework and super-resolution generative adversarial network, which improved the detection performance of small infrared objects in complex backgrounds by combining the SASE module, SPP module, and multilevel receptive field. [PERSON] et al. [24] proposed a lightweight network based on the improved YOLOv4; they used PP-LCNet and depthwise separable convolution to reduce model parameters, added an attention mechanism in the output layer of the backbone network to optimize the feature fusion structure, and used the SloU loss function to improve the accuracy. Considering the applications in practical scenarios, the weight generated by the one-stage detection algorithms is too large, so they are not convenient to deploy in mobile devices with limited resources. At present, equipping CNN models on mobile and embedded devices remains a considerable challenge due to limitations in storage space and computational speed, so lightweight networks have become a research topic in computer vision in recent years. The purpose of research on lightweight models is to minimize the number of parameters and complexity of the models while maintaining network detection accuracy, so that network models can be deployed on devices with limited storage space and computing power. Currently, there are more and more lightweight networks for this challenge on mobile and embedded devices, such as MobileNetv1-v3 [25; 26; 27] and EfficientNet [13] proposed by Google, ShuffleNet [28; 29] and SqueezeNet [30] proposed by KuangShi Technology, and GhostNet [31] proposed by Huawei. These lightweight networks can reduce the parameters of convolutional neural networks by designing different convolution structures. In 2020, [PERSON] proposed a lightweight network based on YOLOv4 named YOLOv4-Tiny; its parameter is only 1/10 of YOLOv4. It can not only train the model with limited device resources, but also deploy the model in resource-constrained mobile devices to perform practical tasks related to object detection; however, the detection accuracy needs to be improved due to the reduced number of parameters of the model. In order to make the model easy to deploy on mobile or embedded devices as well as to have higher accuracy, this paper designed a lightweight object detection network named pointer meter detection-YOLO (PMD-YOLO).