Real-time reading system for pointer meter based on YolactEdge

Yang, Chengjun; Zhu, Ruijie; Yu, Xinde; Yang, Ce; Xiao, Lijun; Fowler, Scott

doi:10.1080/09540091.2023.2241669

Cited by 3 publications

(1 citation statement)

References 32 publications

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“…Findings indicate that their method for detecting pointers shows enhanced robustness and superior resistance to interference when compared to traditional image segmentation and skeletonization techniques [7]. Yang et al, have proposed a real-time system for reading single-point analog meters, employing the YolactEdge instance segmentation framework, which has been executed and operationalized on the Jetson Xavier NX edge computing device [8].…”

Section: Introductionmentioning

confidence: 99%

Research on the Recognition Method of Dial Scales for Arrester Pointer Instruments Based on Deep Learning

Wang,

Hu,

Yin

et al. 2024

Applied Sciences

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To address the recognition challenges faced by arrester pointer instruments’ dial scales in various scenarios, this paper introduces a deep learning-based recognition method for pointer instrument scales. An attention module is integrated into the YOLOv5 network architecture, enhancing the accuracy and robustness of the model. After correcting the dial, dial recognition is conducted with OpenCV to achieve precise identification of the instrument scales. The proposed method was tested using images of arrester pointer instruments against diverse backgrounds. The experimental results demonstrate that the method processes instrument data images in an average time of 0.662 s and achieves a successful recognition rate of 96% with an average error of 0.923%. This method provides a rapid and efficient approach for recognizing instrument scales and offers a novel solution for identifying similar types of instruments.

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Section: Introductionmentioning

confidence: 99%

Research on the Recognition Method of Dial Scales for Arrester Pointer Instruments Based on Deep Learning

Wang,

Hu,

Yin

et al. 2024

Applied Sciences

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A comprehensive research on measurement and evaluation of intelligent meter reading systems

Zhang,

Bai,

Zhang

et al. 2024

Eng. Res. Express

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Based on the machine vision and artificial intelligence technologies, the reading and acquisition of meter values have become the mainstream technical solutions for real-time monitoring of production data in the current industrial field. At the same time, the number of intelligent instruments and meters is increasing, and the quality varies. There is a lack of scientifically measurement methods and quality trust for intelligent meter reading algorithms. To promote their reliable and widespread application in various fields, it is necessary to conduct in-depth research on the assessment system of artificial intelligence in intelligent meter reading systems. This paper comprehensively studies various aspects involved in the emerging measurement field of artificial intelligence evaluation at the current stage, providing scientific measurement and evaluation references for the quality trust of artificial intelligence in the field of meters.

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A deep learning method for pointer meter reading recognition in inspection robots at refrigeration stations

Wang,

Yu,

Feng

et al. 2024

Eng. Res. Express

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To improve the intelligent operation and maintenance of large public building refrigeration stations, various automatic reading methods for pointer meters used in inspection robots have been developed. However, most methods exhibit low detection accuracy for pointer meters in refrigeration stations and poor segmentation of pointers and scale lines, ultimately leading to inaccurate readings. To address the challenges posed by the complex environments of refrigeration stations, this study proposes a deep learning-based image recognition method for precise pointer meter readings. Firstly, the pointer meters in the environment are detected and located by the YOLOV8-CBAM-Wise-IoU model. Secondly, an improved Fast Semantic Segmentation Network (Fast-SCNN) is used for image segmentation of the pointers and scale lines of the pointer meters, and a perspective transformation method is employed to correct images of tilted meters. Then, the image is transformed into a rectangular image using polar coordinate transformation. Finally, the accurate reading is calculated using the distance method, based on the relative distances between the pointers and scale lines. This research conducted experimental tests using an inspection robot in the actual environment of a large public building's refrigeration station. The results demonstrate that the proposed method for pointer meter reading recognition achieves a fiducial error within 0.41%, which can realize the inspection task of the pointer meter in refrigeration stations.

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Real-time reading system for pointer meter based on YolactEdge

Cited by 3 publications

References 32 publications

Research on the Recognition Method of Dial Scales for Arrester Pointer Instruments Based on Deep Learning

Research on the Recognition Method of Dial Scales for Arrester Pointer Instruments Based on Deep Learning

A comprehensive research on measurement and evaluation of intelligent meter reading systems

A deep learning method for pointer meter reading recognition in inspection robots at refrigeration stations

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