An automatic recognition method of pointer instrument based on improved Hough transform

Xu, Lingfeng; Fang, Tian; Gao, Xiaoyu

doi:10.1117/12.2202805

Cited by 6 publications

(2 citation statements)

References 2 publications

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“…In the research of automatic value identification of pointer-type pressure gauge, most of them use threshold segmentation, edge detection, Hough circle [3] or feature matching [4] to detect pressure gauges, Hough line to detect scale and pointer [5]. Angle between pointer and the minimum scale or distance between adjacent is used to giving the reading of pressure gauge [6,7].…”

Section: Introductionmentioning

confidence: 99%

Research on Automatic Value Identification of Pointer-Type Pressure Gauge Based on Machine Vision

Hu,

Zhu,

Sun

et al. 2024

Artificial Intelligence and Human-Computer Interaction

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In order to reduce the workload of staff, break through the data isolation between the front and back ends and enhance the foundation of intelligent construction, Research is carried out on pointer-type pressure gauge which is installed on the gas distribution cabinet based on machine vision to realize the automatic value identification of pointer-type pressure gauge. Using YOLOv3 deep learning framework, the recognition model of multi-pressure gauges under complex conditions is trained, and the automatic extraction algorithm of key information of pointer-type pressure gauges is designed by using the universal structural characteristics of pointer-type pressure gauge. The algorithm we proposed is used to extract and recognize the pointer-type pressure gauge on image and give a reading result automatically. We evaluate the model and calculate the accuracy in a video of pointer-type pressure gauge with changing reading. The calculation and analysis results show that the accuracy of the pressure gauge recognition model established in this paper is 100 % in 66 test pictures, and there is no error detection and missed detection. The dynamic video analysis on pressure gauge with range of 2.5MPa shows that the maximum error of the readings is 0.052 MPa, and the average error is less than 0.01 MPa. Our algorithm is implemented in Python language, which is convenient for the deployment of domestic equipment and has certain popularization and application value in future.

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Section: Introductionmentioning

confidence: 99%

Research on Automatic Value Identification of Pointer-Type Pressure Gauge Based on Machine Vision

Hu,

Zhu,

Sun

et al. 2024

Artificial Intelligence and Human-Computer Interaction

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show abstract

“…For scale marks detection, the connected-component method [4] and the central projection method [5] are most often adopted after scale region segmentation. For pointer detection, several methods like frame di erence [6], Hough line detection [7,8], straight-line tting [9], template matching [10] and central projection [11] have been proposed. There are mainly two methods for reading recognition when the pointer has been detected.…”

Section: Introductionmentioning

confidence: 99%

A pointer location algorithm for computer visionbased automatic reading recognition of pointer gauges

2019

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In order to implement a computer vision based reading recognition system for pointer gauges, we propose an algorithm to locate the pointer by searching for the minimal bounding rectangle (MBR) of the pointer region. Having determined the center of dial region and the centroids of scale marks, we build up a reference system for reading recognition with the dial region center and the scale mark centroids. To identify the indicating value of a gauge image, we first detect the pointer region based on frame difference, and determine the MBR of the pointer using the longest edge of its convex hull. We then take the centroid of the MBR enclosing the pointer region as the centroid of the pointer. We next take the connection between the pointer centroid to the dial center as the pointer, and calculate the indicating value corresponding to the pointer based on the angle method. Experimental results reveal the effectiveness of the pointer location algorithm in computer vision based reading recognition for pointer gauges.

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Reading detection of needle-type instrument in a noisy environment using computer vision-based algorithms

Hsiao

Chang

Vida

et al. 2022

Multimed Tools Appl

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An automatic recognition method of pointer instrument based on improved Hough transform

Cited by 6 publications

References 2 publications

Research on Automatic Value Identification of Pointer-Type Pressure Gauge Based on Machine Vision

Research on Automatic Value Identification of Pointer-Type Pressure Gauge Based on Machine Vision

A pointer location algorithm for computer visionbased automatic reading recognition of pointer gauges

Reading detection of needle-type instrument in a noisy environment using computer vision-based algorithms

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