Research on automatic reading recognition of index instruments based on computer vision

Wang, Qi; Yuansheng, Fang; Wang, Wenlong; Wu, Mei; Wang, Rui; Fang, Yanjun

doi:10.1109/iccsnt.2013.6967053

Cited by 13 publications

(5 citation statements)

References 4 publications

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“…The Hough line transform and its variations have since been a popular method for reading the pointer's angle yielding feasible results (Ye et al, 2013) (Jiannan Chi, 2015) (Jiale et al, 2011). Others have used least squares as an alternative yielding more accurate results in general (Yang et al, 2014) (Wang et al, 2013).…”

Section: Related Workmentioning

confidence: 99%

“…As presented in the first row of Table 1, the data that have been used for testing the methods generally consists of images of a single dial, with the exception of (Jiannan Chi, 2015) and (Yang et al, 2014). Furthermore, the results are in many cases based on only a few images taken from the same angle and light condition (Ye et al, 2013) (Yi et al, 2017) (Sun et al, 2014) (Jiale et al, 2011) (Wang et al, 2013). This means that, even though the results may be good, there is a high possibility that the methods have been overfitted to their respective setups.…”

Section: Comparison With State-of-the-artmentioning

confidence: 99%

See 1 more Smart Citation

Reading Circular Analogue Gauges using Digital Image Processing

Lauridsen

Grassmé

Pedersen

et al. 2019

Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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This paper presents an image processing based pipeline for automated recognition and translation of pointer movement in analogue circular gauges. The proposed method processes an input video frame-wise in a module based manner. Noise is minimized in each image using a bilateral filter before a Gaussian mean adaptive threshold is applied to segment objects. Subsequently, the objects are described by a set of proposed features and classified using probability distributions estimated using Expectation Maximization. The pointer is classified by the Mahalanobis distance and the angle of the pointer is determined using PCA. The output is a low pass filtered digital time series based on the temporal estimations of the pointer angle. Seven test videos have been processed by the algorithm showing promising results. Both source code and video data are publicly available.

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Section: Related Workmentioning

confidence: 99%

Section: Comparison With State-of-the-artmentioning

confidence: 99%

Reading Circular Analogue Gauges using Digital Image Processing

Lauridsen

Grassmé

Pedersen

et al. 2019

Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Application

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show abstract

“…After image preprocessing, image segmentation should be implemented to obtain a binarization image, wherein the threshold-segmentation method is always applied [10][11][12][13]. In the following step, image thinning needs to be conducted in the binarization image to achieve a one-pixel-wide pointer image.…”

Section: Related Work Of the Pointer Gauge Indicating Valuementioning

confidence: 99%

Machine Vision Based Automatic Detection Method of Indicating Values of a Pointer Gauge

Chi

Liu

et al. 2015

Mathematical Problems in Engineering

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This study proposes an automatic reading approach for a pointer gauge based on computer vision. Moreover, the study aims to highlight the defects of the current automatic-recognition method of the pointer gauge and introduces a method that uses a coarse-to-fine scheme and has superior performance in the accuracy and stability of its reading identification. First, it uses the region growing method to locate the dial region and its center. Second, it uses an improved central projection method to determine the circular scale region under the polar coordinate system and detect the scale marks. Then, the border detection is implemented in the dial image, and the Hough transform method is used to obtain the pointer direction by means of pointer contour fitting. Finally, the reading of the gauge is obtained by comparing the location of the pointer with the scale marks. The experimental results demonstrate the effectiveness of the proposed approach. This approach is applicable for reading gauges whose scale marks are either evenly or unevenly distributed.

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“…Alegria and Serra [5] used Linear Hough Transform to detect the pointer of an analogic meter and contour analysis to extrapolate the meter scale. Fang et al [6] adopted a faster method to identify the pointer angle by using a least squares method. Chi et al [7] implemented a more robust algorithm to locate the gauge display using the region growing method.…”

Section: Introductionmentioning

confidence: 99%

Autonomous Reading of Gauges in Unstructured Environments

Milana

Ramírez-Agudelo

Schmiedt

2022

Sensors

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This paper introduces GAUREAD, an end-to-end computer vision system that is able to autonomously read analogic gauges with circular shapes and linear scales in unstructured environments. Existing gauge reading software still relies on some manual entry, like the gauge location and the gauge scale, or they are able to work just with a frontal view. On the contrary, GAUREAD comprises all the necessary steps to make the measurement unconstrained from previous information, including gauge detection from scene, perspective rectification and scale reconstruction. Our algorithm achieves a speed of 800 milliseconds per reading on the NVIDIA Jetson Nano 4 GB. Experimental tests show that GAUREAD can provide a measurement with an error within 3% for perspective angles below 20° and within 9% up to 50°. The system is foreseen to be implemented on mobile robotics to automatise not only safety routines, but also critical security operations.

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Research on automatic reading recognition of index instruments based on computer vision

Cited by 13 publications

References 4 publications

Reading Circular Analogue Gauges using Digital Image Processing

Reading Circular Analogue Gauges using Digital Image Processing

Machine Vision Based Automatic Detection Method of Indicating Values of a Pointer Gauge

Autonomous Reading of Gauges in Unstructured Environments

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