Pointer gauge adaptive reading method based on a double match

Xu, Shidun; Hou, Chunping; Wang, Zhipeng; Li, Beichen

doi:10.1088/1361-6501/ab9410

Cited by 7 publications

(6 citation statements)

References 21 publications

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“…The accuracy of pointer meter readings can be significantly enhanced through correction methods [20]. The method adopted in [6,7,25] is used to correct the dial by matching the similarity between the template image and the captured dial image. Nevertheless, when the dial image is impacted by lighting and the surrounding surroundings, the approach performs poorly [8].…”

Section: Dial Correctionmentioning

confidence: 99%

“…Additionally, capturing the dial image at an inclined angle can lead to image distortion and rotation. The method adopted in [6,7] is used to correct the dial by comparing the characteristics of the template dial image with the captured dial image. Nevertheless, this method is only suitable for certain types of meters and is susceptible to the effects of the natural environment [8].…”

Section: Introductionmentioning

confidence: 99%

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

Tan,

Wu,

Ding

et al. 2024

Electronics

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The automatic reading recognition of pointer meters plays a crucial role in data monitoring and analysis in intelligent substations. Existing meter reading methods struggle to address challenging difficulties such as image distortion and varying illumination. To enhance their robustness and accuracy, this study proposes a novel approach that leverages the TransUNet semantic segmentation model and a perspective transformation correction method. Initially, the dial of the pointer meter is localized from the natural background using YOLOv8. Subsequently, after enhancing the image with Gamma correction technology, the scale lines and the pointer within the dial are extracted using the TransUNet model. The distorted or rotated dial can then be corrected through perspective transformation. Finally, the meter readings are accurately obtained by the Weighted Angle Method (WAM). Ablative and comparative experiments on two self-collected datasets clearly verify the effectiveness of the proposed method, with a reading accuracy of 97.81% on Simple-MeterData and 93.39% on Complex-MeterData, respectively.

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Section: Dial Correctionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

Tan,

Wu,

Ding

et al. 2024

Electronics

View full text Add to dashboard Cite

show abstract

“…Meter Image Preprocessing: The movable device can be tilted and rotated when capturing the meter image, and completing the meter image correction can improve the accuracy of the meter reading calculation [22]. The calibration method consists of calculating the similarity between the image to be detected and the template image features to complete the meter calibration [23][24][25]. The feature-based detection method has limitations when the meter image is subject to natural lighting and the natural environment [26].…”

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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“…After the features have been extracted from images, feature matching is carried out according to the gray neighborhood information, the spatial distribution of features, or the symbol description of features in the image, in order to find the feature correspondence between the reference image and the distorted image. Feature-matching methods are mainly divided into two types: spatial relations based [20][21][22] and invariant descriptor based [23][24][25]. In [26], a set of viewpointinvariant region descriptors was used to track the same object in video frames with different perspectives.…”

Section: Related Workmentioning

confidence: 99%

An improved subpixel-level registration method for image-based fault diagnosis of train bodies using SURF features

Liu¹,

Huang²,

Qin³

et al. 2021

Meas. Sci. Technol.

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Using computer-vision-based fault-diagnosis technologies, anomalies in train bodies can be automatically identified as part of daily maintenance. Nevertheless, since the train speed changes constantly and nonlinearly during the process of image acquisition, the obtained images can be severely distorted. As such, it is necessary to first register the collected images to correct the distortion, so as to accurately extract the key parts of the train body for fault detection. This paper proposes a new accurate registration method for train images based on speeded-up robust features, which improves on the traditional segment registration (SR) method by adding procedures for feature selection, piecewise linearization, and interpolated registration. The novelty of the proposed method is that up to subpixel-level registration accuracy can be achieved. Meanwhile, the robustness of the algorithm is ensured by its strong ability to screen out non-matching pairs when selecting features for registration. To demonstrate the performance of the proposed scheme, experimental comparison and verification are conducted, which show that, compared with the traditional SR method, the registration accuracy is remarkably improved in terms of mean square error, normalization cross-correlation, and structural similarity index.

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Pointer gauge adaptive reading method based on a double match

Cited by 7 publications

References 21 publications

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

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

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

An improved subpixel-level registration method for image-based fault diagnosis of train bodies using SURF features

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