Automatic detection method for travel time accuracy of clock based on machine vision

Abstract

The invention discloses a clock travel time accuracy automatic detection method based on machine vision, and relates to the technical field of machine vision. The method comprises the steps of capturing an image and a video stream of a target clock in real time through a high-resolution camera, performing preprocessing, extracting clock dial and pointer characteristics through Hough transform and an image segmentation technology, calculating a pointer angle, matching the pointer angle with a standard dial image, and obtaining a static error index; the method comprises the following steps of: dynamically tracking a pointer motion track through an optical flow method and a Kalman filter, analyzing the influence of environmental factors on a travel time error, obtaining a dynamic error index, and finally, comprehensively analyzing a static error and a dynamic error, calculating the travel time error index of the clock, and comparing the travel time error index with an error threshold value, so as to judge the travel time accuracy of the clock. The method can automatically and accurately detect the timekeeper travel time error, improves the detection efficiency, reduces personal errors, and comprehensively analyzes the influence of environmental factors on the timekeeper travel time precision.

Description

Technical Field

[0001] The invention relates to the technical field of machine vision, in particular to an automatic detection method for the timekeeping accuracy of a clock based on machine vision. Background Art

[0002] As the demand for time accuracy continues to increase in modern society, the accuracy of clocks and watches as timing devices has attracted much attention. Whether it is a watch in daily life or a precision timing device in the industrial field, accurate time display not only affects personal life, but also plays a key role in many industries. In the rapidly developing fields of industrial production, aerospace, medical equipment, etc., the accuracy requirements for timing equipment are becoming increasingly stringent. However, traditional manual detection methods are inefficient and have large errors, and cannot meet the current needs for efficient and accurate detection, which has promoted the research and development of clock and watch accuracy detection methods based on automation technology.

[0003] Traditional methods for detecting the travel time of watches mostly use mechanical or electronic timing devices to record the travel time deviation of watches through manual operation. These methods have significant shortcomings. The manual detection process relies on the operator's experience and judgment, which is prone to subjective errors and cannot ensure the accuracy of the detection. Secondly, traditional methods are usually limited to static analysis of the travel time of watches, and cannot track the errors of watches in dynamic operation, especially ignoring the potential impact of environmental factors (such as temperature, humidity, vibration, etc.) on the accuracy of watch travel time. In addition, traditional detection methods have a low degree of automation and cannot adapt to the needs of large-scale watch detection in mass production environments. The detection efficiency is low and it is difficult to meet the requirements of industrial production. Therefore, there is an urgent need for a solution based on machine vision and automation technology to improve the efficiency and accuracy of watch travel time detection. Summary of the invention

[0004] In view of the deficiencies of the prior art, the present invention provides an automatic detection method for the timekeeping accuracy of a clock based on machine vision, which solves the problems of the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: an automated detection method for the accuracy of clock time based on machine vision, comprising the following steps: S1. capturing target clock images and video streams in real time through a high-resolution camera, and preprocessing the captured target clock images and video streams; S2. performing feature extraction and angle calculation on the preprocessed target clock images through Hough transform and image segmentation technology, and performing feature matching with the standard dial image to obtain a static error index; S3. dynamically tracking the preprocessed video stream through the optical flow method and Kalman filter, analyzing the influence of environmental factors on the clock time error, and obtaining a dynamic error index; S4. performing a comprehensive analysis of the static error index and the dynamic error index to obtain the clock time error index, comparing the clock time error index with the set error threshold, and judging the accuracy of the clock time.

[0006] Furthermore, the specific process of preprocessing the captured target clock image and video stream is as follows: performing color space conversion on the captured target clock image, converting the RGB color space into a grayscale image, and denoising the image; enhancing the clock edge features through edge detection, and extracting the effective image area of ​​the clock; performing frame extraction and geometric correction on the video stream.

[0007] Furthermore, the specific process of feature extraction and angle calculation of the preprocessed target clock image through Hough transform and image segmentation technology is as follows: the preprocessed clock image is separated into the dial and pointer parts through image segmentation technology, and the circular features in the image and the center position and radius of the clock are identified and extracted through Hough transform; the pointer edge is identified through edge detection method, and the pointer is marked to obtain the pointer angle information, including the pointer angle, time mark position and corresponding angle value.

[0008] Furthermore, feature matching is performed with the standard dial image to obtain the static error index. The specific process is as follows: calculate the difference between the pointer angle extracted from the preprocessed clock image and the standard angle of the corresponding pointer in the standard dial image; compare the calculated angle difference with the preset threshold to obtain the static error index.

[0009] Furthermore, the preprocessed video stream is dynamically tracked by the optical flow method and the Kalman filter, and the specific process of analyzing the influence of environmental factors on the clock timing error is as follows: the pointer position of each frame in the video stream is tracked by the optical flow method, the motion vector of the pointer in each frame image is calculated, and the dynamic position information of the pointer is extracted; the pointer position information obtained by the optical flow method is smoothed by the Kalman filter, and the smoothed dynamic pointer trajectory is compared with the theoretical pointer motion trajectory to obtain the real-time dynamic error value of the pointer; the influence of environmental factors on the dynamic error is analyzed, the influence of environmental changes on the clock timing accuracy is evaluated, and the environmental correction factor is obtained.

[0010] Furthermore, the specific process of obtaining the dynamic error index is as follows: based on the real-time dynamic error value of the pointer and combined with the pointer motion vector data extracted by the optical flow method, the cumulative error of the pointer in different time periods is calculated; according to the environmental correction factor, the cumulative error of the pointer is corrected to eliminate the influence of environmental factors on the pointer motion trajectory; the corrected dynamic error value is compared with the theoretical pointer trajectory to obtain the dynamic error index.

[0011] Furthermore, a comprehensive analysis is performed on the static error index and the dynamic error index to obtain the watch's travel error index. The specific process is as follows: the static error index and the dynamic error index are weighted according to predetermined weights to generate a comprehensive error value; a statistical analysis is performed on the comprehensive error value to evaluate its fluctuation range and trend to obtain the watch's travel error index.

[0012] Furthermore, the specific process of judging the accuracy of the clock's running time is as follows: comparing the clock's running time error index with a preset error threshold; if the clock's running time error index is less than or equal to the preset error threshold, it indicates that the clock is running accurately; if the clock's running time error index is greater than the preset error threshold, it indicates that the clock has a running time error.

[0013] The present invention has the following beneficial effects:

[0014] (1) This automatic detection method for the accuracy of clock time based on machine vision uses a high-resolution camera to capture the image and video stream of the target clock in real time, providing high-definition raw data, which helps the accuracy of subsequent processing, improves the quality of the image, makes the feature extraction of the clock more accurate and reliable, and reduces noise and distortion. Through Hough transform and image segmentation technology, the clock dial and hands can be accurately identified, and the circular features in the target image and the angle information of the clock hands can be extracted. By matching the features with the standard dial image, the static error index is obtained, which helps to detect the accuracy of the clock in a stationary state, provides a basis for the subsequent dynamic error analysis, and ensures the basic accuracy of the clock without environmental interference.

[0015] (2) The automatic detection method for the accuracy of clock travel based on machine vision can effectively capture the motion trajectory of the clock pointer in the video stream and extract the dynamic position information of the pointer by using the optical flow method and Kalman filter for dynamic tracking. The motion trajectory is smoothed by the Kalman filter to eliminate the influence of noise on the detection result and obtain a more accurate real-time dynamic error value. The influence of environmental factors on the travel accuracy is analyzed in combination with the environmental correction factor, so that the detection system can more comprehensively and dynamically evaluate the travel accuracy of the clock in the actual use environment. The static error index and the dynamic error index are comprehensively analyzed to comprehensively evaluate the overall travel accuracy of the clock. By comparing the comprehensively obtained travel error index with the set error threshold, it is possible to quickly determine whether the travel of the clock meets the standard. The accuracy and efficiency of the system are guaranteed, and automatic screening can be realized in the process of mass production or maintenance, reducing the possibility of human misjudgment.

[0016] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The present invention is a flow chart of the automatic detection method of the timekeeping accuracy of a clock based on machine vision. DETAILED DESCRIPTION

[0018] The embodiment of the present application solves the problem of difficulty in achieving high-precision and real-time monitoring in the process of clock time error detection by using an automated detection method for clock time accuracy based on machine vision.

[0019] The overall idea of ​​the problem in the embodiment of this application is as follows:

[0020] The target clock image and video stream are captured in real time by a high-resolution camera, and the captured target clock image and video stream are pre-processed.

[0021] The preprocessed target clock image is subjected to feature extraction and angle calculation through Hough transform and image segmentation technology, and feature matching is performed with the standard dial image to obtain the static error index.

[0022] The preprocessed video stream is dynamically tracked by optical flow method and Kalman filter, the influence of environmental factors on the clock timing error is analyzed, and the dynamic error index is obtained.

[0023] A comprehensive analysis is performed on the static error index and the dynamic error index to obtain the clock travel error index, which is then compared with the set error threshold to determine the accuracy of the clock travel.

[0024] See also Figure 1The embodiment of the present invention provides a technical solution: an automated detection method for the accuracy of clock time based on machine vision, comprising the following steps: S1. capturing the target clock image and video stream in real time through a high-resolution camera, and preprocessing the captured target clock image and video stream; S2. extracting features and calculating angles of the preprocessed target clock image through Hough transform and image segmentation technology, and performing feature matching with the standard dial image to obtain a static error index; S3. dynamically tracking the preprocessed video stream through the optical flow method and Kalman filter, analyzing the influence of environmental factors on the clock time error, and obtaining a dynamic error index; S4. comprehensively analyzing the static error index and the dynamic error index, obtaining the clock time error index, comparing the clock time error index with the set error threshold, and judging the accuracy of the clock time.

[0025] In this implementation scheme, S1. Image and video stream capture and preprocessing: High-resolution camera: used to capture images and video streams of the target clock. High resolution can ensure that the captured image has sufficient details for subsequent processing. S2. Feature extraction and static error calculation: Hough transform: a technology used to identify geometric shapes (such as circles) in images, which can accurately extract the circular features of the dial and pointers of the clock. Image segmentation: separate the different parts of the clock image (such as the dial and pointer) so that each part can be analyzed independently. Static error index: by comparing the current pointer position of the clock with the pointer position of the standard dial, the angle difference between the two is calculated to determine the time error of the clock under static conditions. S3. Dynamic tracking and error analysis: Optical flow method: a technology used to detect motion in an image, which tracks the dynamic motion of the clock pointer by analyzing the changes between video frames to obtain the motion trajectory of the pointer. Kalman filter: used to smooth the motion data obtained by the optical flow method, reduce the influence of noise, and thus obtain a more accurate pointer motion trajectory. Dynamic error index: Calculate the error of the clock under dynamic conditions based on the difference between the actual motion trajectory of the pointer in the video stream and the theoretical motion trajectory. S4. Comprehensive analysis and accuracy judgment: Clock error index: Combine the static error index and the dynamic error index to comprehensively evaluate the overall accuracy of the clock. Error threshold: It is a pre-set standard used to determine whether the error of the clock is within an acceptable range. If the error index exceeds the threshold, it indicates that the clock has an inaccurate timekeeping problem.

[0026] Specifically, the specific process of preprocessing the captured target clock image and video stream is as follows: perform color space conversion on the captured target clock image, convert the RGB color space into a grayscale image, and denoise the image; enhance the clock edge features through edge detection, and extract the effective image area of ​​the clock; perform frame extraction and geometric correction on the video stream.

[0027] In this embodiment, color space conversion: RGB color space is converted to grayscale image: RGB is a combination of red, green and blue colors, and each pixel contains three color channels in the RGB image. Grayscale image only contains brightness information (i.e. black and white image). By converting RGB image into grayscale image, the complexity of image processing is simplified, important structural information is retained, and it is conducive to subsequent feature extraction and analysis. Denoising: During the image capture process, noise may exist in the image due to factors such as lighting conditions or sensor noise. Denoising (such as using Gaussian filtering, mean filtering and other techniques) aims to remove random noise in the image, maintain the clarity of the image, and improve the accuracy of subsequent analysis. Edge detection and feature enhancement: Edge detection: It is a technique for identifying the edges of objects in an image. By detecting the rapid changes in image brightness, key features such as clock outlines and hands can be extracted. In this process, commonly used edge detection algorithms include Sobel operator and Canny edge detection. The enhancement of edge features helps to accurately identify the position of the dial and hands in the subsequent process. Extracting effective image area: The image of the clock usually contains some irrelevant background information. Extracting the effective image area of ​​the clock can reduce the amount of calculation and improve the accuracy of analysis. This step focuses the image processing on the part containing the dial and hands by identifying the outline of the clock, thereby eliminating unnecessary background interference. Frame extraction and geometric correction: Frame extraction: Single frame images are extracted from the video stream at fixed time intervals for analysis. This process ensures that there is a corresponding image available for processing at each time point in the video stream. Geometric correction: Due to the shooting angle, position or lens distortion of the camera, the captured clock image may be distorted or distorted. Through geometric correction techniques (such as perspective transformation and correction algorithms), the true shape and size of the image can be restored to ensure the accuracy of subsequent feature extraction and error calculation.

[0028] Specifically, the specific process of feature extraction and angle calculation of the preprocessed target clock image through Hough transform and image segmentation technology is as follows: the preprocessed clock image is separated into the dial and pointer parts through image segmentation technology, and the circular features in the image and the center position and radius of the clock are identified and extracted through Hough transform; the pointer edge is identified through edge detection method, and the pointer is marked to obtain the pointer angle information, including the pointer angle, time mark position and the corresponding angle value.

[0029] In this embodiment, image segmentation is to divide the image into several regions with the same attributes. For the processing of clock images, image segmentation is used to separate the different parts of the clock, especially to separate the dial part from the pointer part. This process helps to analyze the features of each part separately. Threshold-based segmentation can distinguish the outline of the dial and the outline of the pointer from the preprocessed image, ensuring that subsequent feature extraction is more accurate. Hough Transform is a technology for detecting geometric shapes in images. In clock image processing, Hough Transform is often used to identify circular features, that is, the dial of the clock. Through Hough circle transform, the circular edge corresponding to the clock dial can be found in the image, and then the center position and radius of the clock can be extracted. This provides a basis for determining the geometric structure of the clock and the position of the pointer relative to the dial. Edge detection is a technology for identifying the edge of an object by detecting areas with large brightness changes in the image. In clock images, the edge of the pointer is usually manifested as a sharp change in brightness. By using edge detection methods (such as Canny edge detection algorithm), the edge outline of the pointer can be accurately identified, thereby accurately locating the position of the pointer. The purpose of this step is to provide a reference for the subsequent angle calculation of the pointer. After identifying the edge of the pointer, the next step is to mark the pointer, that is, to determine the starting and ending points of the pointer. In a clock based on a circular dial, the position of the pointer can be expressed by its relative angle to the center of the clock. The pointer angle calculation is based on a geometric relationship, taking the line connecting the end point of the pointer and the center of the dial as a vector, and calculating the angle of the vector relative to the fixed time scale (usually the 12 o'clock position) to obtain the current angle information of the pointer. The angle information includes the pointer angle (the specific position of the minute hand, hour hand, and second hand), the time scale position (such as 3 o'clock, 6 o'clock, etc.), and the corresponding angle value (the angle from the center of the clock to the scale, usually expressed in degrees). Calculate the error in combination with the dial characteristics: After obtaining the angle information of the pointer, these angle values ​​can be compared with the ideal time on the standard dial of the clock to calculate the static error index of the pointer. For example, the hour hand should point to the 90-degree position at 3 o'clock. The angle calculation can be used to determine whether there is an error in the actual pointer position.

[0030] Specifically, the specific process of performing feature matching with the standard dial image and obtaining the static error index is as follows: calculating the difference between the pointer angle extracted from the preprocessed clock image and the standard angle of the corresponding pointer in the standard dial image; comparing the calculated angle difference with the preset threshold to obtain the static error index.

[0031] In this implementation, the pointer angle difference calculation formula is: Δθ i =θ measured,i -θ standard,i ; Δθ i : The angle difference of the i-th pointer (hour hand, minute hand, second hand). θmeasured,i : The actual measured angle of the i-th pointer. θ standard,i : The standard angle that the i-th pointer in the standard dial should have. In order to determine whether the error is within the acceptable range, the calculated angle difference needs to be compared with the preset threshold. If the error exceeds the threshold, the error is recorded as a significant error. θ threshold : The preset angle error threshold represents the maximum allowable error range. The static error index formula is as follows: Among them, E static : Cumulative static error index. T: Total observation time. Δθ i (t): The instantaneous angle error of the i-th pointer at time t. W time (t): Time weighting factor, used to take into account the impact of errors at different times (different weights are set according to the running time of the clock or the time period of the detection).

[0032] Specifically, the preprocessed video stream is dynamically tracked by the optical flow method and the Kalman filter, and the specific process of analyzing the influence of environmental factors on the clock timing error is as follows: the pointer position of each frame in the video stream is tracked by the optical flow method, the motion vector of the pointer in each frame image is calculated, and the dynamic position information of the pointer is extracted; the pointer position information obtained by the optical flow method is smoothed by the Kalman filter, and the smoothed dynamic pointer trajectory is compared with the theoretical pointer motion trajectory to obtain the real-time dynamic error value of the pointer; the influence of environmental factors on the dynamic error is analyzed, the influence of environmental changes on the clock timing accuracy is evaluated, and the environmental correction factor is obtained.

[0033] In this embodiment, the optical flow method tracks the motion of the pointer: the optical flow method is an image-based motion detection technology used to estimate the motion of objects in an image. The specific process is as follows: pointer position tracking for each frame of the image: the optical flow method tracks the position changes of the pointer in the image by analyzing each frame of the video stream. Each frame can be regarded as a static image, and the optical flow method calculates the direction and speed of the pointer by detecting the change in image brightness. Motion vector calculation: based on the change in pointer position between frames, the optical flow method calculates the motion vector of the pointer. This vector represents the direction and distance of movement of the pointer in the image, reflecting the dynamic position information of the pointer. Kalman filter smoothing: the Kalman filter is a recursive algorithm used to smooth and suppress noise on signals. It updates the predicted value by comparing the prediction of the system state with the measurement, thereby optimizing the measured pointer position information. The specific steps are as follows: smooth pointer trajectory: the Kalman filter processes the pointer motion vector obtained by the optical flow method to reduce the error caused by noise or image jitter, and obtain a smooth pointer motion trajectory. Theoretical trajectory comparison: The smoothed pointer trajectory is compared with the theoretical standard pointer motion trajectory, and the difference is the real-time dynamic error value. This error reflects the deviation between the pointer in the actual motion process and the theoretical motion trajectory. Obtaining environmental correction factors: Analyzing the impact of environmental factors: Environmental factors, such as temperature, humidity, and light changes, may affect the motion trajectory of the watch pointer. For example, temperature changes may cause metal parts to expand or contract, thereby affecting the movement accuracy of the pointer. Evaluating environmental correction factors: Based on the analysis of environmental factors, evaluate the impact of these changes on the pointer movement and calculate the environmental correction factor. The environmental correction factor is used to compensate for the impact of the environment on the pointer movement to ensure that the calculation of the clock's travel error is more accurate.

[0034] Specifically, the specific process of obtaining the dynamic error index is as follows: based on the real-time dynamic error value of the pointer and combined with the pointer motion vector data extracted by the optical flow method, the cumulative error of the pointer in different time periods is calculated; according to the environmental correction factor, the cumulative error of the pointer is corrected to eliminate the influence of environmental factors on the pointer motion trajectory; the corrected dynamic error value is compared with the theoretical pointer trajectory to obtain the dynamic error index.

[0035] In this implementation, the calculation formula of the dynamic error index is as follows: Parameter explanation, D dynamic ( t ) : The dynamic error index at time t, reflecting the timekeeping accuracy of the clock at that time. N: The number of frames in the time period used for calculation, that is, the total number of frames of pointer motion vector data extracted by the optical flow method Δd i(t): The real-time dynamic error value of the pointer in the i-th frame at time t. It can be calculated according to the following formula: Δd i (t) = d measured,i (t)-d theoretical,i Where: d measured,i (t): The actual measured pointer displacement in the i-th frame. theoretical,i : Theoretically, the pointer should have a displacement. K env (t): Environmental correction factor, which changes with time t and is used to adjust the dynamic error of the pointer to take into account the impact of the environment.

[0036] Specifically, the static error index and the dynamic error index are comprehensively analyzed to obtain the clock time error index. The specific process is as follows: the static error index and the dynamic error index are weighted according to predetermined weights to generate a comprehensive error value; the comprehensive error value is statistically analyzed to evaluate its fluctuation range and trend to obtain the clock time error index.

[0037] In this embodiment, the static error index (E static ) and the dynamic error index (E dynamic ) is weighted to generate the clock travel error index (E total ). The weighted calculation formula is as follows: E total =w s ·E static +w d ·E dynamic ; Among them: E total : The clock travel error index reflects the comprehensive error performance of the clock travel. s : The weight of the static error index, indicating the importance of this index in the comprehensive error value. static : Static error index. w d : The weight of the dynamic error index, indicating the importance of this index in the comprehensive error value. dynamic : Dynamic error index. Weight w s and w d The setting should be adjusted according to the requirements of the specific application scenario and the importance assessment of static and dynamic errors and usually meets w s +w d =1. Weighted calculation allows the impact of static and dynamic errors to be considered comprehensively, thus reflecting the accuracy of the watch more comprehensively. The static error index provides a moment-specific error assessment, while the dynamic error index considers the real-time nature of the pointer movement and the impact of environmental changes. Statistical analysis helps to understand the stability and accuracy of the watch over a long period of time by evaluating the fluctuation range and trend. This analysis can reveal potential sources of deviation and directions for improvement.

[0038] Specifically, the specific process of judging the accuracy of a clock is as follows: comparing the clock's running error index with a preset error threshold; if the clock's running error index is less than or equal to the preset error threshold, it indicates that the clock is running accurately; if the clock's running error index is greater than the preset error threshold, it indicates that the clock has a running error.

[0039] In this embodiment, the clock travel error index is an index obtained after comprehensive evaluation of static and dynamic errors, reflecting the overall running accuracy of the clock under specific conditions. The lower the index, the more accurate the clock travels. Set an error threshold, the preset error threshold is determined according to industry standards, product requirements or laboratory test data, and represents the maximum error range allowed by the clock under normal operating conditions. The threshold should be fully verified to ensure its rationality and applicability in practical applications. The clock travel error index is less than or equal to the preset error threshold, which means that the clock travel error is within an acceptable range and meets the predetermined performance standards. At this time, it can be judged that the clock travels accurately, indicating that its internal mechanism and function are in good condition and suitable for continued use or sale. The clock travel error index is greater than the preset error threshold, which means that the clock travel error exceeds the acceptable range and there is an obvious travel error. At this time, the clock needs to be further inspected and adjusted to eliminate potential faults or error causes and ensure that its performance meets the standard requirements. Through clear judgment criteria, quality control can be effectively carried out to ensure the accuracy of factory products and reduce customer complaints and after-sales service costs.

[0040] In summary, this application has at least the following effects:

[0041] The automatic detection method of clock travel accuracy based on machine vision uses a high-resolution camera to capture the image and video stream of the target clock in real time and perform preprocessing. The clock dial and pointer features are extracted through Hough transform and image segmentation technology, the pointer angle is calculated, and it is matched with the standard dial image to obtain the static error index. The pointer motion trajectory is dynamically tracked through the optical flow method and Kalman filter, and the influence of environmental factors on the travel error is analyzed to obtain the dynamic error index. Finally, the clock travel error index is calculated by comprehensive analysis of static error and dynamic error, and compared with the error threshold to determine the travel accuracy of the clock. This method can automatically and accurately detect the travel error of the clock, improve detection efficiency, reduce human errors, and comprehensively analyze the influence of environmental factors on the travel accuracy of the clock.

[0042] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0043] The present invention is described with reference to flowcharts and / or block diagrams of systems, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0044] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0045] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0046] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0047] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An automated detection method for the accuracy of clock travel based on machine vision, characterized in that: The following steps are involved: S1. Capturing the target clock image and video stream in real time through a high-resolution camera, and preprocessing the captured target clock image and video stream; S2. Extract features and calculate angles of the preprocessed target clock image by using Hough transform and image segmentation technology, and perform feature matching with the standard dial image to obtain the static error index; S3. Dynamically track the preprocessed video stream by optical flow method and Kalman filter, analyze the influence of environmental factors on the clock's travel error, and obtain the dynamic error index; S4. Perform a comprehensive analysis on the static error index and the dynamic error index to obtain the clock travel error index, compare the clock travel error index with the set error threshold, and determine the accuracy of the clock travel.

2. The method for automatically detecting the timekeeping accuracy of a clock based on machine vision according to claim 1, characterized in that: The specific process of preprocessing the captured target clock image and video stream is as follows: Perform color space conversion on the captured target clock image, convert the RGB color space into a grayscale image, and perform denoising on the image; Enhance the edge features of the clock through edge detection and extract the effective image area of ​​the clock; Perform frame extraction and geometric correction on video streams.

3. The method for automatically detecting the timekeeping accuracy of a clock based on machine vision according to claim 2, characterized in that: The specific process of feature extraction and angle calculation of the pre-processed target clock image by using Hough transform and image segmentation technology is as follows: The preprocessed clock image is separated into the dial and pointer parts by image segmentation technology, and the circular features in the image and the center position and radius of the clock are identified and extracted by Hough transform; The pointer edge is identified by edge detection method, and the pointer is marked to obtain the pointer angle information, including the pointer angle, time mark position and corresponding angle value.

4. The method for automatically detecting the timekeeping accuracy of a clock based on machine vision according to claim 3, characterized in that: The specific process of matching features with the standard dial image and obtaining the static error index is as follows: Calculate the difference between the pointer angle extracted from the preprocessed clock image and the standard angle of the corresponding pointer in the standard dial image; The calculated angle difference is compared with a preset threshold to obtain a static error index.

5. The method for automatically detecting the timekeeping accuracy of a clock based on machine vision according to claim 4, characterized in that: The specific process of dynamically tracking the pre-processed video stream by using the optical flow method and Kalman filter and analyzing the influence of environmental factors on the clock's travel error is as follows: The pointer position of each frame in the video stream is tracked by the optical flow method, the motion vector of the pointer in each frame image is calculated, and the dynamic position information of the pointer is extracted; The pointer position information obtained by optical flow tracking is smoothed by Kalman filter, and the smoothed dynamic pointer trajectory is compared with the theoretical pointer motion trajectory to obtain the real-time dynamic error value of the pointer; Analyze the impact of environmental factors on dynamic errors, evaluate the impact of environmental changes on the accuracy of clocks and watches, and obtain environmental correction factors.

6. The method for automatically detecting the timekeeping accuracy of a clock based on machine vision according to claim 5, characterized in that: The specific process of obtaining the dynamic error index is as follows: Based on the real-time dynamic error value of the pointer and the pointer motion vector data extracted by the optical flow method, the accumulated error of the pointer in different time periods is calculated; According to the environmental correction factor, the accumulated error of the pointer is corrected to eliminate the influence of environmental factors on the movement trajectory of the pointer; The corrected dynamic error value is compared with the theoretical pointer trajectory to obtain the dynamic error index.

7. The method for automatically detecting the timekeeping accuracy of a clock based on machine vision according to claim 6, characterized in that: The specific process of comprehensively analyzing the static error index and the dynamic error index to obtain the clock travel error index is as follows: Perform weighted calculation on the static error index and the dynamic error index according to a predetermined weight to generate a comprehensive error value; Conduct statistical analysis on the comprehensive error value, evaluate its fluctuation range and trend, and obtain the watch timing error index.

8. The method for automatically detecting the timekeeping accuracy of a clock based on machine vision according to claim 7, characterized in that: The specific process of judging the accuracy of a clock is as follows: Comparing the clock's running time error index with a preset error threshold; If the clock's running time error index is less than or equal to the preset error threshold, it means that the clock is running accurately; If the time error index of the watch is greater than the preset error threshold, it means that the watch has a time error.

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