Pointer Instrument Research Articles

A general-purpose pointer meter reading detection method based on adaptive feature fusion and data augmentation

Pointer meters continue to serve as the primary monitoring instruments within substations. To solve the issue of poor model accuracy and low reading accuracy when reading multiple types of pointer meters, this paper proposes a universal cascaded convolutional neural network for accurate reading of various types of pointer instruments. The proposed detection method consists of two stages: instrument digital positioning and instrument pointer fitting. In the digital(figure of a number) positioning stage, an instrument data augmentation method for “large background and small target” is proposed to improve the model's sensitivity to digital position and reduce the interference of redundant background information to the model. In addition, an adaptive feature fusion pyramid structure is proposed for more effective feature fusion and enhanced fuzzy small target recognition. Compared with the baseline model, the method introduced in this study realizes a 0.47 % improvement in target detection accuracy. In our proposed dataset, the average recognition accuracy for the digital position location phase is 98.65 %,the average reading errors in square instruments, circular instruments, and instruments with uneven scales are 0.76 %, 0.73 %, and 0.67 % respectively. The proposed universal detection method in this paper achieves ultra-high precision and can be applied to reading pointer instruments of different types and scales. Compared with existing pointer meter reading methods, the proposed detection method showcases superior simplicity and effectiveness. Furthermore, it showcases a reduced measurement error, thereby bolstering its practical utility in industrial contexts.

Pointer instrument positioning and indication recognition algorithm based on YOLOV5s

The substation inspection robot has low accuracy in positioning and indication reading of the pointer instrument. An algorithm design for indicator recognition is proposed. The algorithm mainly includes dial area extraction, scale line extraction and dial center determination, zero scale line determination, and indicator recognition. An experimental system for pointer instrument positioning and indication recognition is constructed to verify the proposed algorithm. The results indicate that the positioning accuracy of the two instruments is 90.5% and 100% respectively. Ten positioned instruments are selected, and the reading recognition error is 0.126. It shows that the proposed pointer instrument positioning and indication recognition algorithm can improve the efficiency of substation instrument recognition.

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

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.

NextLens-The Next Generation of Surgical Navigation: Proof of Concept of an Augmented Reality System for Surgical Navigation.

Objective The aim of this work was the development of an augmented reality system including the functionality of conventional surgical navigation systems. Methods An application software for the Augmented Reality System HoloLens 2 from Microsoft was developed. It detects the position of the patient as well as position of surgical instruments in real time and displays it within the two-dimensional (2D) magnetic resonance imaging or computed tomography (CT) images. The surgical pointer instrument, including a pattern that is recognized by the HoloLens 2 sensors, was created with three-dimensional (3D) printing. The technical concept was demonstrated at a cadaver skull to identify anatomical landmarks. Results With the help of the HoloLens 2 and its sensors, the real-time position of the surgical pointer instrument could be shown. The position of the 3D-printed pointer with colored pattern could be recognized within 2D-CT images when stationary and in motion at a cadaver skull. Feasibility could be demonstrated for the clinical application of transsphenoidal pituitary surgery. Conclusion The HoloLens 2 has a high potential for use as a surgical navigation system. With subsequent studies, a further accuracy evaluation will be performed receiving valid data for comparison with conventional surgical navigation systems. In addition to transsphenoidal pituitary surgery, it could be also applied for other surgical disciplines.

An intelligent vision recognition method based on deep learning for pointer meters

Nowadays, pointer instruments remain the main state monitoring devices in the power industry, because they have strong mechanical stability to resist electromagnetic interferences compared with digital instruments. Although the object detection algorithms based on deep learning have widely been used in the field of instrument detection, the meter recognition process still relies on threshold segmentation to recognize object points and on Hough transform to extract the meter pointer. An intelligent vision recognition method based on YOLOv5 and U2-Net network (YLU2-Net) is proposed to improve the accuracy and efficiency of meter recognition in a complex environment. Firstly, the pointer meter is located in the instrument images by using the YOLOv5 network as a region of interest (RoI). Then, the instrument RoI is processed by means of perspective transformation and image resizing. Thirdly, an improved U2-Net image segmentation method with the deep separable convolution and the focal loss function is devised to distinguish the pointers and scales from the background in the instrument RoI. Further, a dimension reduction reading method with the polar coordinate transformation is developed to calculate the meter reading accurately and efficiently. Finally, the ablation experiment is conducted to test the performance of each algorithm module in our method, and the competition experiment is completed to compare our method with other state-of-the-art ones. The experimental results verify the accuracy and efficiency of the YLU2-Net recognition method proposed.

Target Detection of Pointer Instrument based on Deep Learning

This paper proposed an instrument target detection algorithm based on yolov3 network for the drawbacks caused by manual inspection of pointer instruments in complex industrial environments. Firstly, the algorithm improved the model convergence speed by introducing the k-means++ algorithm to cluster out 9 sets of initial anchor boxes suitable for the pointer meter data set. Moreover, by combining the channel attention mechanism with spatial attention mechanism in the yolov3 backbone network, the extraction of shallow features was further improved by adding two residual blocks to the second residual block, then a new model yolov3-CBAM (Convolutional Block Attention Module) was formed. In addition, the mean average accuracy (map) of the training and testing of the three types of instruments on the data set reaches 90.8% by the results, which is about 2.1% higher than the original yolov3. This algorithm has obvious advantages in the patrol inspection and identification of industrial instruments.

Automobile Instrument Detection Using Prior Information and Fuzzy Sets

Since there are many kinds of automobile instruments and the industrial environment is usually complex, automatic instrument detection during automobile production test becomes a challenging task. This article presents an automobile pointer instrument detection method based on prior information and fuzzy sets. The proposed method consists of two frameworks built around a pointer meter prior information model (PMPIM). The first one targets PMPIM construction to obtain the required prior information. With this purpose, a pointer-free template is obtained from a template generation algorithm and pointer positions are mapped into an energy function for optimization, using an energy function-based pointer positioning algorithm. The energy function is defined based on the distance between the crisp and fuzzy sets. The second framework targets PMPIM utilization to detect pointer meters during production test. A fuzzy-based image enhancement method is proposed to enhance test images and the template simultaneously. A prior information and energy function-based pointer positioning algorithm is also proposed to locate pointers in test images. Finally, the indicator value (the value the pointer points to) is calculated according to the positions of the pointer and scale marks. Experimental results show that the proposed method achieves better generalization and robustness than existing state-of-the-art methods.

Instrument Pointer Recognition Scheme Based on Improved CSL Algorithm.

The traditional pointer instrument recognition scheme is implemented in three steps, which is cumbersome and inefficient. So it is difficult to apply to the industrial production of real-time monitoring. Based on the improvement of the CSL coding method and the setting of the pre-cache mechanism, an intelligent reading recognition technology of the YOLOv5 pointer instrument is proposed in this paper, which realizes the rapid positioning and reading recognition of the pointer instrument. The problem of angle interaction in rotating target detection is eliminated, the complexity of image preprocessing is avoided, and the problems of poor adaptability of Hough detection are solved in this strategy. The experimental results show that compared with the traditional algorithm, the algorithm in this paper can effectively identify the angle of the pointer instrument, has high detection efficiency and strong adaptability, and has broad application prospects.

New identification method of linear pointer instrument

New identification method of linear pointer instrument

Instrument reading recognition by deep learning of capsules network model for digitalization in Industrial Internet of Things

AbstractUsing the intelligent image recognition method to recognize the reading of the unintelligent manual reading meter is an important approach to expand the sensing means and reduce the cost of transformation of the Industrial Internet of Things. Traditional methods based on morphology and back propagation neural networks are difficult to solve the problems of shadow, deflection, and dimness in the industrial environment. In this article, the deep learning‐based capsule networks for pointer instruments and digital display instruments were proposed to implement unintelligent manual reading meter recognition using. Due to the complex structure and a large number of parameters, the basic capsule network is difficult to run on devices with low computing resources. To overcome this issue, the improved capsule network model was designed to reduce the parameters of the model. The experimental results show that the improved capsule network improves the recognition accuracy by 13% and reduces the parameters by 23%.

Using the ROSA Robot for Lesion Resection: A Novel Adapter With Added Applications.

The ROSA robot (Medtech) has been shown to be a useful instrument in the surgeon's armamentarium for accurate placement of stereotactic electroencephlography depth electrodes. However, it has not yet been used as a navigation tool for lesion resection. Here, we demonstrate a novel adapter that allows the surgeon to use the ROSA robot with the NICO BrainPath for the resection of deep lesions. To demonstrate the utility of an adapter that allows the ROSA robot to be used in conjunction with the NICO BrainPath tube for lesion resection. A stainless steel adapter was made based on the specifications of the ROSA pointer instrument. Two 3D printed models were used to undergo a "mock" surgery using the adapter to assess for ease of use and applicability. The adapter allowed for adequate accessibility and visualization of the tumors in both mock cases. In addition, the stability of the ROSA robot and the design of the adapter allowed the surgeon to rest their hands on the instrument without jeopardizing its position. The ROSA adapter allowed for accurate navigation and exposure of these lesions, combining the accuracy and stability of the ROSA robot, with the retraction of the BrainPath tube.

(Retracted) Automatic reading recognition of pointer barometer based on machine vision

To develop a calibration system for a pointer barometer based on machine vision, an automatic reading recognition scheme of the barometer based on color images is proposed. To locate the dial while extracting the pointer, an optimized three-point-based circle determination method is proposed to calibrate the dial center and segment the dial. At the same time, an improved angle method is proposed to realize the automatic reading recognition of pointer instruments. First, the pointer area is detected and extracted by the three frame difference method, and the pointer centroids are extracted by least circumscribed rectangle fitting, and the optimal three points are selected to determine the center of the circle and locate the dial. Then, the two-color image matrix elements corresponding to different pointers are compared point by point to obtain the maximum value, and the dial without the pointers is also obtained. Through cropping, color channel component extraction, and binarization processing on the dial without pointers, a noninterference dial scale image is refitted, and each scale is fitted with a minimum circumscribed rectangle to calibrate the centroid of the scale line. Finally, using the centroids of the calibrated scale lines, the centroid of the fitted pointer, and the circle center obtained by three-point-based circle determination, the improved angle method proposed is used to realize the automatic reading recognition of the pointer instrument. Experimental results show that this scheme can effectively improve the accuracy of machine vision-based automatic reading recognition for pointer instruments.

The Pointer-type Instrument Recognition Method based on Yolov3

<p>In order to solve the problems of fuzzy image, different measuring range and recognition accuracy of pointer instrument in the process of power inspection, propose a pointer-type instrument recognition method based on Yolov3. Firstly, super-resolution technology is introduced to reconstruct the instrument image. Then, a combined Yolov3 and OCR- based dial feature extraction method is used to detect the dial in the instrument image, effectively extract the digital features in the region, and recognize the instrument by combining the digital features pointer-type angle. Next, to solve the instrument image’s fuzzy problem, a super-resolution image reconstruction technology is designed to reconstruct the fuzzy instrument image and get a clear instrument image. Finally, a method based on a Yolov3 and OCR combination is proposed to detect the instrument image and recognize the digital features of the dial to target the insufficient use of dial features. Comparison of the effect of traditional machine learning algorithm and Canny edge detection in the pointer-type instrument image recognition shows that the accuracy and practicability of this method have been significantly improved in the recognition of pointer-type instruments in complex environment images.</p> <p> </p>

Pointer-type instrument positioning method of intelligent inspection system for substation

Robot intelligent inspection is widely used in the positioning of various pointer instruments in power, petroleum, chemical, and other industries. Aiming at the technical problems of poor adaptability, poor real-time performance, and low positioning accuracy of the pointer instrument positioning method in the existing substation intelligent inspection robot system, we propose a simple and effective pointer instrument positioning detection algorithm. The algorithm first extracts locally adaptive regression kernels (LARK) features of the input image, and the dimension of the LARK feature is reduced using the principal components analysis algorithm. Then, the template image is slid in the input image, the cosine similarity is used as an evaluation index, and the Fourier transform is used to accelerate the convolution operation in the cosine similarity calculation. Finally, the accelerated-KAZE algorithm is used to extract the feature points of the pointer-type instrument area image and the template image, and the statistical method of grid motion was used to eliminate the wrong matching points. The remaining matching points were processed by random sample consensus algorithm, and the homography matrix was obtained. The image registration was completed by the homography matrix, and the pointer-type instrument region positioning was realized. The experimental results show that the proposed method has good adaptability, strong real-time performance, and high accuracy of pointer-type instrument positioning.

Automatic Reading Algorithm of Substation Dial Gauges Based on Coordinate Positioning

With the development of science and technology, inspection robots have attracted more and more attention, and research on the automatic reading of pointer instruments through inspection robots has become particularly valuable. Aiming at the problems of uneven illumination, complex dial background and damping fluid interference of the collected instrument images, this paper proposes a dial gauge reading algorithm based on coordinate positioning. First, the multi-scale retinex with color restoration (MSRCR) is applied to improve the uneven illumination of the image. Second, a circle detection algorithm based on the arc-support line segment is proposed to detect the disc to obtain the coordinate of the center and radius of the circle. Then, a pointerless template is used to obtain the pointer, and the concentric circle algorithm is applied to locate the refined pointer. Finally, the automatic reading is calculated using the relative position of the pointer and the zero scale. The experimental results prove that the proposed algorithm can accurately locate the center of the circle and the pointer and obtain readings automatically.

Computer Vision Based Automatic Recognition of Pointer Instruments: Data Set Optimization and Reading.

With the promotion of intelligent substations, more and more robots have been used in industrial sites. However, most of the meter reading methods are interfered with by the complex background environment, which makes it difficult to extract the meter area and pointer centerline, which is difficult to meet the actual needs of the substation. To solve the current problems of pointer meter reading for industrial use, this paper studies the automatic reading method of pointer instruments by putting forward the Faster Region-based Convolutional Network (Faster-RCNN) based object detection integrating with traditional computer vision. Firstly, the Faster-RCNN is used to detect the target instrument panel region. At the same time, the Poisson fusion method is proposed to expand the data set. The K-fold verification algorithm is used to optimize the quality of the data set, which solves the lack of quantity and low quality of the data set, and the accuracy of target detection is improved. Then, through some image processing methods, the image is preprocessed. Finally, the position of the centerline of the pointer is detected by the Hough transform, and the reading can be obtained. The evaluation of the algorithm performance shows that the method proposed in this paper is suitable for automatic reading of pointer meters in the substation environment, and provides a feasible idea for the target detection and reading of pointer meters.

A Pointer Type Instrument Intelligent Reading System Design Based on Convolutional Neural Networks

The pointer instrument has the advantages of being simple, reliable, stable, easy to maintain, having strong anti-interference properties, and so on, which has long occupied the main position of electrical and electric instruments. Though the pointer instrument structure is simple, it is not convenient for real-time reading of measurements. In this paper, a RK3399 microcomputer was used for real-time intelligent reading of a pointer instrument using a camera. Firstly, a histogram normalization transform algorithm was used to optimize the brightness and enhance the contrast of images; then, the feature recognition algorithm You Only Look Once 3rd (YOLOv3) was used to detect and capture the panel area in images; and Convolutional Neural Networks were used to read and predict the characteristic images. Finally, predicted results were uploaded to a server. The system realized automatic identification, numerical reading, an intelligent online reading of pointer data, which has high feasibility and practical value. The experimental results show that the recognition rate of this system was 98.71% and the reading accuracy was 97.42%. What is more, the system can accurately locate the pointer-type instrument area and read corresponding values with simple operating conditions. This achievement meets the demand of real-time readings for analog instruments.

Pointer instrument image recognition system for veterinary drug production based on RFID and deep learning

Based on RFID technology, the tag data can be read and identified dynamically, real-time, contactless, long-distance and batch, and the image information of pointer instrument can be collected. Using Faster-RCNN to detect and locate the dial area, and combining with Le Net-5 method to identify the dial number, the system has the advantages of fast reading, high accuracy, strong adaptability and intelligent management. It provides a solution for the automatic recognition and reading of pointer instrument in the complex environment of veterinary medicine production workshop.

Research on the Image Enhancement Algorithm of Pointer Instrument under Inadequate Light

For image processing, the recognition of pointer instrument’s reading by computer vision highly depends on brightness. An image enhancement algorithm based on homomorphic filtering and histogram equalization is proposed in order to reduce the impact of low-light conditions on images of pointer instrument. It combines the methods of spatial with frequency domain, which enhances the image contrast and highlights the image details as well. Compared with the traditional method, the experiments show that the proposed method can eliminate the effect of inadequate light and also perform well in enhancing the image quality.

OpenCV-Based Automatic Detection System for Automobile Meter

For the universality of computer vision in the automatic detection of pointer instrument, an OpenCV-based automatic detection system for automobile meter was proposed and realized. The original video image was pre-processed through the filtering and SURF algorithm was used for the registration of template and test instruments to obtain the transformation parameters. Pointer and dial were treated respectively. Through the gray-scale images subtraction, Canny edge extraction and Hough line detection, the straight line where the pointer was obtained rapidly. Then the pointer readings were obtained through functional relation between the point angle and actual value. Experimental results show that this system can be used to conduct the real-time and automatic detection of different types of meters.