Recognition Method of Digital Meter Readings in Substation Based on Connected Domain Analysis Algorithm

Zhang, Ziyuan; Hua, Zexi; Tang, Yongchuan; Zhang, Yunjia; Lu, Weijun; Dai, Congfei

doi:10.3390/act10080170

Cited by 10 publications

(5 citation statements)

References 39 publications

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“…It is a science that enables translation of various types of documents or images into analyzable, editable, and searchable data [ 22 ]. Traditional OCR is based on image processing (binarization, connected domain analysis, projection analysis, etc., [ 23 , 24 , 25 ]) and statistical machine learning (Adaboot, SVM [ 26 , 27 ]) to extract the text content on the picture. However, traditional OCR performs poorly at character recognition in complex scenarios.…”

Section: Materials and Methodsmentioning

confidence: 99%

A Remote Calibration Device Using Edge Intelligence

Wang

Hao

et al. 2022

Sensors

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Power system facility calibration is a compulsory task that requires in-site operations. In this work, we propose a remote calibration device that incorporates edge intelligence so that the required calibration can be accomplished with little human intervention. Our device entails a wireless serial port module, a Bluetooth module, a video acquisition module, a text recognition module, and a message transmission module. First, the wireless serial port is used to communicate with edge node, the Bluetooth is used to search for nearby Bluetooth devices to obtain their state information and the video is used to monitor the calibration process in the calibration lab. Second, to improve the intelligence, we propose a smart meter reading method in our device that is based on artificial intelligence to obtain information about calibration meters. We use a mini camera to capture images of calibration meters, then we adopt the Efficient and Accurate Scene Text Detector (EAST) to complete text detection, finally we built the Convolutional Recurrent Neural Network (CRNN) to complete the recognition of the meter data. Finally, the message transmission module is used to transmit the recognized data to the database through Extensible Messaging and Presence Protocol (XMPP). Our device solves the problem that some calibration meters cannot return information, thereby improving the remote calibration intelligence.

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Section: Materials and Methodsmentioning

confidence: 99%

A Remote Calibration Device Using Edge Intelligence

Wang

Hao

et al. 2022

Sensors

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“…Bishwokarma et al [16] used a deep neural network YOLOv3 to detect and recognize meter counters and digits. Zhang et al [1] proposed a digital meter method based on the connected domain analysis algorithm, using Faster R-CNN to locate the dial area, and then using YOLOv4 to detect the digital area. Combined with the connected area algorithm, it judged whether there was a decimal point after the number.…”

Section: Related Workmentioning

confidence: 99%

“…The readings of the meters in the substation can reflect the operation of many pieces of equipment. However, due to the complex external environment of the meter, the large number, and the dependence of the original equipment of the substation on the meter, traditional substation inspection cannot effectively guarantee the real-time validity of the meter reading [1].…”

Section: Introductionmentioning

confidence: 99%

Pointer Meter Recognition Method Based on Yolov7 and Hough Transform

et al. 2023

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The current manual reading of substation pointer meters wastes human resources, and existing algorithms have limitations in accuracy and robustness for detecting various pointer meters. This paper proposes a method for recognizing pointer meters based on Yolov7 and Hough transform to improve their automatic readability. The proposed method consists of three main contributions: (1) Using Yolov7 object detection technology, which is the latest Yolo technology, to enhance instrument recognition accuracy. (2) Providing a formula for calculating the angle of a square pointer meter after Hough transformation. (3) Applying OCR recognition to the instrument dial to obtain the model and scale value. This information helps differentiate between meter models and determine the measuring range. Test results demonstrate that the proposed algorithm achieves high accuracy and robustness in detecting different types and ranges of instruments. The map of the Yolov7 model on the instrument dataset is as high as 99.8%. Additionally, the accuracy of pointer readings obtained using this method exceeds 95%, indicating promising applications for a wide range of scenarios.

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“…Medically, it is believed that the distance between the medial border of the scapula and the spine is about 6-7cm. In the thresholded binary image obtained in the previous step, the connected domain algorithm [19] was adopted to divide the obtained connected domain into the box of the patient's head (headArea) and mark the area (Figure 3). The back midline was calculated by adding up the left and right coordinates of the head and dividing the sum by two.…”

Section: Back Contour Segmentationmentioning

confidence: 99%

Scoliosis Detection Based on Feature Extraction from Region-of-Interest

Yang¹,

Xi²,

Zhang³

et al. 2022

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In recent years, the incidence of scoliosis is rising among adolescents. Considering the radiation hazards of X-ray detection, this paper intends to develop an effective non-radiation detection method for scoliosis. The research method consists of the following steps: (1) Collect clear an image of the back of the patient with a high-resolution digital camera, and optimize the image through preprocessing; (2) Segment the region of interest (ROI) of the back and spine to reduce the complexity of subsequent calculations; (3) Extract the back contour and mark the feature points; (4) Extract features according to the grayscale change of the spine ROI, and fit the spine midline according to the feature points; (5) Evaluate the degree of scoliosis according to the symmetry of the posture features and the Cobb angle of the spine midline. Finally, experimental results were analyzed, which indicate that the proposed scoliosis detection method can preliminarily evaluate the posture features. The scoliosis detection error fell in the reasonable range (0-4 degrees), when the subjects had a Cobb angle between 0 and 30 degrees. Hence, our algorithm is accurate and effective, and provides a low-cost, efficient solution for scoliosis detection.

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Recognition Method of Digital Meter Readings in Substation Based on Connected Domain Analysis Algorithm

Cited by 10 publications

References 39 publications

A Remote Calibration Device Using Edge Intelligence

A Remote Calibration Device Using Edge Intelligence

Pointer Meter Recognition Method Based on Yolov7 and Hough Transform

Scoliosis Detection Based on Feature Extraction from Region-of-Interest

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