Automatic pointer meters recognition system based on line scan vision

Wang, Qing

doi:10.1088/1361-6501/ac8b9c

Cited by 8 publications

(6 citation statements)

References 10 publications

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“…Calculate Meter Readings: The ultimate goal is an accurate calculation of the meter reading. Current methods for obtaining meter readings include the distance method [22], the angle method [44], the template method [45] and the depth regression method [46] to determine meter readings. Wang [47] used different states of the pointer in a linear scanner to calculate the parameters of the pointer translation function and predict the pointer reading based on the response obtained at the center of mass of the pointer's point of light and a given input amplitude.…”

Section: Related Workmentioning

confidence: 99%

Application of Swin-Unet for pointer detection and automatic calculation of readings in pointer-type meters

Zhang,

Ji,

Yang

et al. 2023

Meas. Sci. Technol.

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Pointer-type meters are widely used in military, industrial, and aerospace applications. In this paper, we propose a method to automatically calculate the readings of pointer-type meters that exhibits strong robustness to various situations, such as complex backgrounds, tilted meters, blurred images, and meter images with uneven illumination. First, the mask maps of scale and pointers are obtained using the Swin-Unet semantic segmentation network. For the mask map of scale, the Swin Transformer image classification network is used to identify the values of the scale and the coordinates of the centroids of the scale, and for the mask map of the pointer, the least skeleton circle method is proposed to fit the linear equation of the pointer. Second, the influence of the pointer and several adjacent scale on the meter reading is considered, and the weighted angle method (WAM) is proposed to calculate the meter reading. In order to verify the robustness of the algorithm in this paper, the pointer detection method is compared with the traditional pointer detection method, and it is found that the pointer detection method in this paper works better, and the pointer detection algorithm in this paper is applied to different semantic segmentation results, and it is verified that the pointer detection algorithm in this paper can be well adapted to different semantic segmentation results. This paper also compares the proposed algorithm with the existing meter reading calculation methods. The experiments show that using WAM on uncorrected meter images reduces the error by 30% compared with the traditional angle method, and using WAM on corrected meter images reduces the error by about 50%, which finally verifies the effectiveness of the algorithm in this paper.

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Section: Related Workmentioning

confidence: 99%

Application of Swin-Unet for pointer detection and automatic calculation of readings in pointer-type meters

Zhang,

Ji,

Yang

et al. 2023

Meas. Sci. Technol.

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“…This system employs the light-spot centroid algorithm to accurately determine the central point of the meter's pointer. Trials have demonstrated that this method for extracting data from pointer meters exhibits remarkable resilience against various disturbances [3]. Ma et al, have introduced a highly precise and sturdy algorithm for the automatic reading of pointer meters.…”

Section: Introductionmentioning

confidence: 99%

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

Wang,

Hu,

Yin

et al. 2024

Applied Sciences

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To address the recognition challenges faced by arrester pointer instruments’ dial scales in various scenarios, this paper introduces a deep learning-based recognition method for pointer instrument scales. An attention module is integrated into the YOLOv5 network architecture, enhancing the accuracy and robustness of the model. After correcting the dial, dial recognition is conducted with OpenCV to achieve precise identification of the instrument scales. The proposed method was tested using images of arrester pointer instruments against diverse backgrounds. The experimental results demonstrate that the method processes instrument data images in an average time of 0.662 s and achieves a successful recognition rate of 96% with an average error of 0.923%. This method provides a rapid and efficient approach for recognizing instrument scales and offers a novel solution for identifying similar types of instruments.

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“…Salomon et al [11] introduced a new data set in unconstrained scenarios, which improves the meter recognition rate combining YOLOv4 with a novel regression method (AngReg). Literature [2] developed an automatic pointer meter recognition system based on line scan vision. The captured image is used to record the dynamic movement of the pointer, and the centroid position of the pointer is determined by the optical spot centroid algorithm.…”

Section: Introductionmentioning

confidence: 99%

“…In previous work on automatic pointer meter reading such as [2,[6][7][8][9][10][11] only addressed meter detection, pointer extraction and automatic reading content, and did not consider the problem of pointer meter reading in low illumination situations. And in the work of image enhancement such as [12][13][14][15][16][17][18][19] are targeted for natural images with low illumination, not combined with pointer meter reading, and most of the image enhancement networks are complex and time consuming affecting the reading efficiency.…”

Section: Introductionmentioning

confidence: 99%

An optimized design of the pointer meter image enhancement and automatic reading system in low illumination environment

Ge¹,

Yu²,

Hu³

et al. 2023

Meas. Sci. Technol.

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As meter data becomes more and more important in the power industry, detection robots are led into substations for automatic collection of the pointer meters. However, the meter images captured in low illumination environments are unclear, resulting in poor recognition of the meter reading. A low-illumination image enhancement method based on virtual exposure is proposed in this paper, improving the dark and bright areas of low-illumination images, respectively. Then the image fusion was performed based on the Laplace pyramid to obtain clear meter images. In addition, the dial area was extracted using the Hough circle transform, and the pointer’s rotation center was fitted using the least squares approach. Finally, the straight line of the pointer was extracted, and the data reading was based on the line segment detector algorithm. Case studies show the above method has good robustness in low illumination environment, with high rate, and accuracy during the image enhancement and automatic reading of the pointer meter.

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Automatic pointer meters recognition system based on line scan vision

Cited by 8 publications

References 10 publications

Application of Swin-Unet for pointer detection and automatic calculation of readings in pointer-type meters

Application of Swin-Unet for pointer detection and automatic calculation of readings in pointer-type meters

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

An optimized design of the pointer meter image enhancement and automatic reading system in low illumination environment

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