# A Novel Object Detection Method of Pointer Meter Based on Improved YOLOv4-Tiny
## 1 Introduction  
Pointer meters have been widely used in industrial places such as oil wells, oil fields, and substations because of their simple structure, low cost, long life, and strong anti-interference properties [1]. However, pointer meters generally do not have the functions of data transmission and data storage, and cannot realize automatic data collection and real-time transmission. The most traditional method is manual reading, but this method has the disadvantages of low efficiency, large error, and low timeliness [2], and when there is an unfavorable environment for manual reading outdoors, there is a possibility of safety accidents. With the development of the era, mobile robots and computer technology have been continuously developed and applied. At present, in the high-temperature, complex electromagnetic interference, corrosion, and other complex real environments, inspection robots with algorithm models have gradually replaced the manual meter detection, reading, early warning, and other applications, which can significantly reduce labor costs, improve the efficiency of instrumentation information conveyance, and ensure the safety and stability of industrial sites. The detection of pointer meters is very important work and has been widely studied and applied. [PERSON] et al. [3] first used the localized pixel inhomogeneity factor model to improve the saliency of the pointer meter region in the binarized image, and then used Hough circle detection to detect the dial range of the meter, but the recognition results obtained by using Hough circle detections are often misread and miss detection due to light, shooting angle, complex background, etc. ; in addition, different-sized circles need to set different parameters during detection, so the stability is poor. [PERSON] et al. [4] proposed a circle detection algorithm based on the line segments of circular bracket components to detect the meter and obtain circle center coordinates and radius of the meter. [PERSON] et al. [5] used perspective transform detection and matching features to obtain various meter images. [PERSON] et al. [6] used scale-invariant feature transform features to match the image to be detected with the template and used random sample consensus [7] to remove the wrong feature points, and thus the meter was detected. Since these methods require complex calculations and specific attributes of the meter, as well as a large number of template images of various types of meter being used to match features, these methods have the disadvantage of poor real-time performance and are easily limited in the actual complex environment, and the performance of automatic recognition needs to be improved. In order to accurately detect and locate meters in complex real-world scenes, it is necessary to have an object detection algorithm that can perform with high accuracy in a complex background environment. Image recognition and object detection through deep learning can effectively compensate for the shortcomings of traditional methods such as low accuracy, missed detection, and poor real-time performance, and can have better recognition effects on images with noise, occlusion, blur, and other interferences. At present, image object detection methods based on deep learning have shown excellent performance in many fields; these object detection methods can be divided into two categories: two-stage method and one-stage method. The two-stage method includes region convolutional neural network (RCNN) [8], Faster RCNN [9], Mask RCNN [10], etc. ; the steps of these algorithms can be divided into extracting object regions and using convolutional neural networks (CNNs) to classify and recognize regions, so it is called two-stage target detection algorithm. The two-stage method has slow speed and is unable to meet the target detection requirements in real-time environments, so the application of this method is limited.