An Intelligent Instrument Reader: Using Computer Vision and Machine Learning to Automate Meter Reading

Sowah, Robert R.; Ofoli, Abdul R.; Mensah-Ananoo, Eugene; Mills, Godfrey A.; Koumadi, Koudjo M.

doi:10.1109/mias.2021.3063082

Cited by 9 publications

(2 citation statements)

References 11 publications

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“…The offset calculation calculates the direction and length difference of each joint point after standardization and infers and calculates the angle difference between the joint point of the target image and the standard action in three-dimensional space. According to the calculation of the deviation angle between the learner's action and each joint point of the standard action, the quantitative index of action similarity is designed to calculate the similarity between the basketball action and each joint point of the standard action [ 7 ]. Select the joint with the lowest score, correct the coordinate of the corresponding joint position information in the standard action image again, and restore the standard action corresponding to the joint position with nonstandard action to the target image through coordinate change, so as to obtain the corresponding action correction suggestions [ 8 ].…”

Section: Intelligent Correction Methods Of Shooting Actionmentioning

confidence: 99%

Intelligent Correction Method of Shooting Action Based on Computer Vision

Wang

Feng³

2022

Computational Intelligence and Neuroscience

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Aiming at the problem that the students’ long-term use of nonstandard shooting action leads to poor basketball teaching effect, an intelligent correction method of shooting action based on computer vision is proposed. Combined with the principle of computer vision, the image acquisition model of basketball shooting action is constructed. The edge contour and adaptive feature segmentation of basketball images are detected, and abnormal shooting movements are recognized. The intelligent correction model of shooting action is constructed, and the intelligent correction of shooting action is realized. Finally, through experiments, it is proved that the visual analysis and intelligent correction effect of basketball shooting action are obviously better, and it can correct shooting action in real time and accurately.

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Section: Intelligent Correction Methods Of Shooting Actionmentioning

confidence: 99%

Intelligent Correction Method of Shooting Action Based on Computer Vision

Wang

Feng³

2022

Computational Intelligence and Neuroscience

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“…In 2021, Li et al [7] proposed an Otsu algorithm and an improved Hough algorithm, which can well avoid the influence of uneven illumination on pointer meter readings. Sowah et al [8], using computer vision and machine learning technology, developed a new algorithm used for automatic meter reading, using a series of image contour filter cascading number classifier to extract the advanced features; this method does not rely on any information before the instrument is being read and can directly get the meter reading. 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.…”

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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