Automobile Instrument Detection Using Prior Information and Fuzzy Sets

Zhang, Jiawei; Liu, Yu; Yu, Jinyong; Yang, Xianqiang; Yu, Xinghu; Rodríguez-Andina, Juan J.; Gao, Huijun

doi:10.1109/tie.2021.3135523

Cited by 6 publications

(3 citation statements)

References 23 publications

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“…Image processing-based methods for meter image quality enhancement include methods such as image greyscaling to reduce computational effort [30], threshold segmentation to obtain target regions [31], and histogram equalisation to improve image contrast [32]. For the difficulty in obtaining real datasets, researchers have generated virtual meteration data in large quantities by means of deep learning [33,34].…”

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.

View full text Add to dashboard Cite

show abstract

“…Then calculate the intersection point between the linear equations ( 6) and ( 7) of the long axis and the short axis and the elliptic equation (1), that is, the set of key points {S 1 , S 2 , S 3 , S 4 } before perspective transformation. According to the linear equations ( 6) and ( 7) of the long axis and the short axis, calculate the intersection point with the elliptic tangent equation (8), that is, the set of key points {S ′ 1 , S ′ 2 , S ′ 3 , S ′ 4 } after perspective transformation.…”

Section: Perspective Correctionmentioning

confidence: 99%

“…Pointer-type meters have the advantages of a wide range of applications, low environmental impact and high accuracy, so they are used in most plants [1]. However, the reading of the pointer meter is mainly manual and some meters are in complex environments such as high altitude, high pressure and high radiation, so staff cannot take the reading easily, * Author to whom any correspondence should be addressed.…”

Section: Introductionmentioning

confidence: 99%

Correction and pointer reading recognition of circular pointer meter

Zhang

Zhao

et al. 2022

Meas. Sci. Technol.

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For the meter images collected in the actual environment, there is the possibility of tilt and rotation. This paper presents a method to calibrate the circular pointer-type meter based on YOLOv5s network. The convolution neural network (CNN) framework is used to detect the scale value in the meter panel as the key point, the position information and value information of the detected scale value are used to fit the elliptic equation of the position of the scale value with the least square method for perspective correction and rotation correction of the meter, and the corrected meter image is used to obtain the meter pointer reading. This paper proposes the weighted angle method to read the meter reading. The accumulated error of the meter image after multiple transformations is eliminated. Finally, comparing the pointer detection method of this paper with the traditional pointer detection method, the error of this detection method is smaller; comparing the meter reading results before and after correction, the meter reading error after correction is 50\% less than before correction, and comparing the method of this paper with other mainstream methods, it proves the effectiveness of the correction method of this paper.

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