Computer Vision Based Automatic Recognition of Pointer Instruments: Data Set Optimization and Reading

Wang, Lu; Wang, Peng; Wu, Linhai; Xu, Lijia; Huang, Peng; Kang, Zhiliang

doi:10.3390/e23030272

Cited by 27 publications

(17 citation statements)

References 18 publications

(21 reference statements)

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“…Tang et al [9] conducted automatic detection of pointer instrument through image recognition method, and the accuracy of reading recognition met the performance requirements, with efficient and intelligent operation characteristics. Wang et al [10] combined target detection and computer vision, used fast-RCNN to detect the instrument panel, and then read the pointer indicator representation through Hough transform. Lei [2] designed a system capable of reading both pointer and digital meters.…”

Section: Related Workmentioning

confidence: 99%

A YOLOv3-Based Industrial Instrument Classification and Reading Recognition Method

Zhang¹,

Chen²,

Lei³

2022

Mobile Information Systems

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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.

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Section: Related Workmentioning

confidence: 99%

A YOLOv3-Based Industrial Instrument Classification and Reading Recognition Method

Zhang¹,

Chen²,

Lei³

2022

Mobile Information Systems

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“…After obtaining the dial area through the Faster R-CNN positioning network [27], the readings detection is performed on the image of the dial area. Divide the dial area readings detection algorithm into two parts.…”

Section: Dial Area Readings Detectionmentioning

confidence: 99%

Recognition Method of Digital Meter Readings in Substation Based on Connected Domain Analysis Algorithm

et al. 2021

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Aiming at the problem that the number and decimal point of digital instruments in substations are prone to misdetection and missed detection, a method of digital meter readings in a substation based on connected domain analysis algorithm is proposed. This method uses Faster R-CNN (Faster Region Convolutional Neural Network) as a positioning network to localize the dial area, and after acquiring the partial image, it enhances the useful information of the digital area. YOLOv4 (You Only Look Once) convolutional neural network is used as the detector to detect the digital area. The purpose is to distinguish the numbers and obtain the digital area that may contain a decimal point or no decimal point at the tail. Combined with the connected domain analysis algorithm, the difference between the number of connected domain categories and the area ratio of the digital area is analyzed, and the judgment of the decimal point is realized. The method reduces the problem of mutual interference among categories when detecting YOLOv4. The experimental results show that the method improves the detection accuracy of the algorithm.

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“…Noises can drastically reduce the performance of the system. Some complicated methods, like neural network algorithms, are needed to maintain the accuracy of the machine reading [8][9][10].…”

Section: Introductionmentioning

confidence: 99%

Coordinates transformation method for pointer gauge reading by machine vision

Jinuntuya

Sudatham²,

Jinuntuya

2023

J. Phys.: Conf. Ser.

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In this report we present a machine vision algorithm for pointer gauge reading based on coordinates transformation. Since a pointer gauge is a polar representation of a linear scale, it is possible to transform its polar reading to the original linear counterpart. Once the picture of a pointer gauge is captured digitally, we can assign the polar positions to each pixel of the image. Those polar coordinates can be plotted on a rectangular frame to form a transformed image. The scale lines and the pointer are transformed to point vertically (or horizontally). Locations of the scale lines and pointer can be obtained from their pixel histograms maxima. The main advantage of our algorithm is its simplicity. We can read the pointer orientation and scale lines position of a pointer gauge directly from its geometrical structure. The complication of feature extraction process from the conventional algorithm, e.g., Hough transformation, can be avoided. We have tested the algorithm with the ideal and real image of pointer gauges. In the latter case, even though there are some noises on the transformed image, the locations of pointer and scale lines can still be easily found. Due to its simplicity, our algorithm is fast. We expect that our method can be useful in practice as an alternative machine vision algorithm when the real time pointer gauge reading system, e.g., the automatic dial gauge tester, is needed.

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Computer Vision Based Automatic Recognition of Pointer Instruments: Data Set Optimization and Reading

Cited by 27 publications

References 18 publications

A YOLOv3-Based Industrial Instrument Classification and Reading Recognition Method

A YOLOv3-Based Industrial Instrument Classification and Reading Recognition Method

Recognition Method of Digital Meter Readings in Substation Based on Connected Domain Analysis Algorithm

Coordinates transformation method for pointer gauge reading by machine vision

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