Small-Scale Robust Digital Recognition of Meters Under Unstable and Complex Conditions

Lv, Qingsong; Rao, Yunbo; Zeng, Shaoning; Huang, Cheng; Cheng, Zhanglin

doi:10.1109/tim.2022.3195244

Cited by 6 publications

(1 citation statement)

References 57 publications

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“…These methods are limited in that they require manual setting of specific parameters for each image, and if the image is affected by natural lighting, external occlusions etc the set parameters will lose their value. Lv et al [38] proposed a Multi-Classifier under Feature Engineering to recognize the values of meter scales, and also designed a multilayer kernel regression positioning to improve the accuracy of meter recognition. Zhang et al [39] used Yolov4 to recognize each pointer dial in a water meter and used multi-feature fusion RFB-Net to detect scales and hands in each dial.…”

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%