A YOLOv3-Based Industrial Instrument Classification and Reading Recognition Method

Abstract

Aiming at the demand of industrial instrument reading, this study proposes a method of industrial instrument classification and reading recognition based on YOLOv3. Given that industrial meters can be divided into pointer meters and digital meters according to the dial type, this method conducts a reading study for each of the two types of meters. Firstly, the YOLOv3 model is trained to recognize and detect the meter types and classify the meters according to the values of the obtained classes. The pointer meter uses a Hough circle to detect the dial, extracts the scale and the pointer, calculates the angle between the 0 scale line and the pointer, and obtains the reading of the pointer meter. The digital meter extracts the digits by finding the contours of the dial and the digit area and then uses a support vector machine (SVM) to identify the extracted digits and output the readings of the digital meter. Through the test, the mean average precision (mAP) of the recognition model in this study is 93.73%. The absolute error of pointer meter reading is less than 0.1 in general, and the maximum relative error is 0.35%. The accuracy of the digital meter reading is 99.7%. The proposed method can accurately read the value of the instrument and meet the needs of industrial production.

Research on the Recognition Method of Dial Scales for Arrester Pointer Instruments Based on Deep Learning

“…The trials conducted validate the effectiveness and performance of the YLU2-Net method they propose [12]. Zhang et al, have developed a technique that utilizes YOLOv3 for the categorization and reading detection of industrial instruments, capable of interpreting both pointer-type and digital meters [13]. Liu et al, have introduced a flexible detection and identification system for pointer meters using computer vision, adaptable to a range of settings.…”

“…With the advancement of deep learning, numerous detection algorithms stemming from this field have been proposed, including the YOLO series [9][10][11][12][13] and the R-CNN series [14][15][16][17][18]. Wu et al, have developed the YOLOX-CAlite algorithm for meter detection to address the specific performance challenges encountered in target detection within the context of meter reading.…”

A Robust Pointer Meter Reading Recognition Method Based on TransUNet and Perspective Transformation Correction

“…Huo et al [27] used the distance method to read the meter. Zhang et al [28] applied the angle method to obtain the final readings. Calculating meter readings using only two neighboring scales may lead to some errors.…”

Research on the Pointer Meter Reading Method Based on Adaptive Pooling and Hierarchical Fusion

“…Lin et al [15] utilized a histogram normalization algorithm to enhance meter images before detecting and localizing meters using YOLOv3 [16]. Zhang et al [17] utilized the YOLOv3 model to detect the instrument image's dial region. Subsequently, they applied the contour method to identify the scale lines and employed the Hough transform to detect the pointer.…”