A vision-based method for measuring the hole of a moving rope

Abstract

A vision-based method for measuring the diameter of a moving rope, relating to fields of precision measurement technology such as computer vision and mechanics. To address the technical problem in the prior art that the measurement of a rope's diameter in motion cannot achieve real-time, accurate measurement of the rope's diameter under dynamic conditions of force and motion, the present invention provides a technical solution: a vision-based method for measuring the diameter of a moving rope, comprising: capturing images of the rope after deformation under different force conditions; selecting a designated area within the image; processing the designated area to obtain the rope's diameter; and obtaining the rope's compression and transmission ratio under force based on the rope's diameter and real-time tension sensor data. The method also includes the step of plotting a curve of the rope's diameter change under different force conditions. The method is suitable for use in measuring the diameter of a rope in motion.

Description

Technical Field

[0001] The present invention relates to the fields of computer vision, mechanics and other precision measurement technologies, and specifically to the precise measurement of the change in rope diameter when the rope is subjected to tension. Background Art

[0002] In the field of modern precision measurement, measuring the stress state of ropes has always been an important research topic. In particular, measuring the diameter change of ropes under dynamic motion and tensile deformation is crucial for various engineering applications. Existing rope measurement methods are mostly based on traditional contact measurement methods, such as calipers and tension sensors. However, these methods have significant limitations, especially in the following aspects:

[0003] First, there are the limitations of static measurement. Traditional measuring tools, such as calipers and gauges, are mostly used to measure ropes in a static state. These tools require manual operation, are subject to significant human error, and are not suitable for dynamic measurements. For example, when a rope is moving at high speeds or is stretched and deformed, a caliper struggles to accurately measure its instantaneous diameter, resulting in inefficient and unreliable measurements. This is particularly evident in industrial production and safety monitoring, such as in lifting operations, mine safety, and cable bridge monitoring, where real-time understanding of the rope's status is crucial to ensure safety.

[0004] Secondly, contact measurement methods have drawbacks. Because ropes deform under dynamic forces, friction and slippage can occur on their surfaces, making it difficult for contact measurement tools to provide accurate data. Furthermore, contact measurement can cause physical damage to the rope itself, which can pose a serious safety hazard, especially in precision applications such as maintenance and inspection of aerial cables. Therefore, non-contact measurement has become an effective alternative to minimize physical disturbance to the rope.

[0005] In recent years, computer vision technology has been widely used in industrial inspection. It uses cameras to capture images and image processing algorithms to analyze the size and deformation of objects. These methods are non-contact, automated, and highly accurate, and have been applied to a variety of measurement scenarios. For example, in production lines, computer vision technology can automatically detect product dimensions, avoiding errors caused by manual operation. In addition, the development of image processing technologies such as edge detection, morphological operations, and image segmentation has made the dimensional measurement of target objects more accurate and efficient. For static objects, computer vision technology has demonstrated significant advantages.

[0006] However, existing visual measurement methods still face numerous challenges when measuring ropes in motion. For example, ropes in motion can be affected by a variety of factors, such as lighting conditions, rapid deformation of the rope, and complex surface textures. These factors can interfere with image clarity and measurement accuracy. Furthermore, due to sliding friction and tension, the deformation of a moving rope is dynamic and irregular. A single camera may not be able to fully capture all deformation information, resulting in unstable measurement results.

[0007] Therefore, existing technologies generally suffer from the following technical issues: There is a lack of effective methods for measuring rope diameter while in motion, making it impossible to accurately and in real time measure rope diameter under dynamic conditions of force and motion. Furthermore, existing visual measurement systems are poorly adaptable to environments with fluctuating lighting and complex deformations, resulting in low measurement accuracy. Traditional contact measurement methods are even more inadequate for real-time monitoring and high-frequency measurement. Summary of the Invention

[0008] In order to solve the technical problem in the prior art that the rope diameter measurement in the moving state cannot achieve real-time and accurate measurement of the rope diameter under dynamic conditions of force and motion, the technical solution provided by the present invention is as follows:

[0009] A vision-based method for measuring a hole in a moving rope, the method comprising:

[0010] Steps for collecting images of the rope after deformation under different stress conditions;

[0011] A step of selecting a designated area in the image;

[0012] a step of processing the designated area to obtain the diameter of the rope;

[0013] The step of obtaining the compression amount and transmission ratio of the rope under stress according to the diameter of the rope and real-time tension sensor data.

[0014] Furthermore, a preferred embodiment is provided, in which different forces on the rope are achieved through a motor, a weight and a pulley.

[0015] Furthermore, a preferred embodiment is provided, wherein the designated area is the rope portion near the hole, which is achieved by ROI area selection.

[0016] Furthermore, a preferred embodiment is provided, wherein the processing includes denoising and thresholding processing.

[0017] Furthermore, a preferred embodiment is provided, wherein the diameter of the rope is obtained through the profile of the rope.

[0018] Furthermore, a preferred embodiment is provided, which further includes the step of drawing a diameter variation curve of the rope under different stress conditions.

[0019] Based on the same inventive concept, the present invention also provides a visual-based sports rope hole measurement device, the device comprising:

[0020] A module for collecting images of rope deformation under different stress conditions;

[0021] Selecting a module in a designated area of ​​the image;

[0022] Processing the designated area to obtain a module of the rope diameter;

[0023] A module for obtaining the compression amount and transmission ratio of the rope under stress according to the rope diameter and real-time tension sensor data.

[0024] Based on the same inventive concept, the present invention also provides a computer storage medium for storing a computer program. When the computer reads the computer program, the computer executes the method described.

[0025] Based on the same inventive concept, the present invention also provides a computer, comprising a processor and a storage medium. When the processor reads the computer program stored in the storage medium, the computer executes the method described above.

[0026] Based on the same inventive concept, the present invention also provides a computer program product, which is a computer program. When the computer program is executed, the method described above is implemented.

[0027] Compared with the prior art, the technical solution provided by the present invention is beneficial in that:

[0028] By building a multi-camera visual measurement platform, they achieved multi-angle capture of the moving rope. This multi-camera layout effectively reduces interference from lighting changes and rope deformation, ensuring image stability and measurement accuracy. Compared to traditional single-camera systems, the use of multiple cameras allows for acquisition of images from different angles, avoiding measurement information loss due to rope twisting or occlusion, significantly improving overall system robustness and data integrity.

[0029] Using advanced image processing algorithms like OpenCV, including edge detection, morphological operations, and binarization, the system accurately extracts the rope's outline from the image. These algorithms enable the system to effectively isolate the rope's boundaries against complex backgrounds, minimizing the impact of ambient noise on measurement. Compared to traditional manual visual inspection or simple contact measurement, this computer vision-based processing method can capture subtle deformation details, significantly improving accuracy, especially when the rope is deformed under load.

[0030] By introducing tension sensors to capture rope force data and combining them with visual measurement results, the system enables a comprehensive analysis of the relationship between rope force and diameter changes. This combined approach of mechanical and visual measurement effectively addresses the problem that relying solely on visual measurement may not reflect internal force states. Compared to traditional measurement methods, this approach enables a comprehensive analysis of rope force conditions based on real-time monitoring, enabling more accurate prediction of rope wear and fatigue, improving safety and early warning capabilities.

[0031] The combination of dynamic capture and real-time image processing overcomes the limitations of traditional static measurement, which is unable to cope with deformation during motion. The camera captures the rope's deformation in real time under load, and combined with synchronous motor control, ensures both temporal and spatial accuracy. Compared to static measurement, this real-time measurement method maintains high accuracy even under high-frequency dynamic changes, providing reliable data support for engineering applications that require a precise understanding of the rope's dynamic characteristics.

[0032] It is suitable for measuring the diameter of ropes in motion. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 Get a schematic for the rope measurement picture;

[0034] Figure 2 This is a schematic diagram of the rope diameter measurement results;

[0035] Figure 3 Frame the image area.

[0036] Figure 4 Schematic diagram of threshold processing;

[0037] Figure 5 It is a schematic outline diagram;

[0038] Figure 6 Get a schematic diagram for rope distance and diameter;

[0039] Figure 7 Schematic diagram of the image taken during the test rope;

[0040] Figure 8Schematic diagram of the relationship between force transmission ratio, speed and angle;

[0041] Figure 9 Schematic diagram of the relationship between compression ratio, angle and speed. DETAILED DESCRIPTION

[0042] In order to make the advantages and benefits of the technical solution provided by the present invention more clearly reflected, the technical solution provided by the present invention is now further described in detail with reference to the accompanying drawings, specifically:

[0043] Embodiment 1: This embodiment provides a vision-based method for measuring the hole of a moving rope, the method comprising:

[0044] Steps for collecting images of the rope after deformation under different stress conditions;

[0045] A step of selecting a designated area in the image;

[0046] a step of processing the designated area to obtain the diameter of the rope;

[0047] The step of obtaining the compression amount and transmission ratio of the rope under stress according to the diameter of the rope and real-time tension sensor data.

[0048] Specifically:

[0049] Step 1: Build the rope survey platform

[0050] Establish an experimental platform, including ropes, tension sensors, motors, limit holes, cameras and light sources.

[0051] Detailed Description: The first task in building a rope measurement platform is to assemble the various components of the system. Specifically, one end of the rope is connected to a tension sensor and motor, and the other end is connected to a weight. A pulley is used in between to guide the rope's motion. A camera is mounted near the pulley to capture the rope's movement and deformation. Limiting holes made of transparent materials such as glass define the rope's trajectory for better measurement. Furthermore, a light source is used to provide sufficient brightness for the camera to capture clear images, minimizing interference from shadows and reflections.

[0052] Step 2: Set camera parameters and shoot

[0053] Set the camera parameters and control the camera to shoot in real time.

[0054] Detailed Description: Run the pre-programmed camera program and adjust the camera parameters, including exposure time, aperture, and focal length, according to the experimental conditions. The camera parameters must be adjusted according to the lighting conditions to ensure image quality sufficient for subsequent processing. The control program will set the camera's shooting interval to continuously capture the rope's state at different points in time during its movement. By setting up the automatic shooting function, the rope's movement can be monitored and recorded in real time throughout the process, providing data for the next image processing step.

[0055] Step 3: Motor Controls Rope Movement and Synchronizes Shooting

[0056] The motor is started to control the movement of the rope, while the camera takes pictures and outputs an image sequence.

[0057] Detailed Description: After the motor is started, it drives the weight upward, causing the rope to deform under stress near the stopper hole. The weight then remains in place for a period of time before returning to its initial position. During this process, a camera continuously captures the rope's deformation using a pre-set program. Each captured image records the rope's current state, including changes in diameter and deformation. Furthermore, a tension sensor simultaneously records the rope's force data. The output of this step is a continuous sequence of images and corresponding force data, providing the basis for subsequent data processing.

[0058] Step 4: Image Processing

[0059] The captured images are processed to extract the rope edges and calculate their diameter.

[0060] Detailed Description: Image processing is performed using the OpenCV library. First, the image is denoised to reduce noise introduced by uneven lighting or complex backgrounds. After denoising, image binarization techniques are used to separate the rope from the background, retaining only the rope area. After extracting the target area, edge detection algorithms, such as Canny edge detection, are applied to identify the rope's boundaries and obtain a clear edge outline. Next, contour extraction is performed to identify the rope's two edges, and the shortest distance between the edges is calculated to determine the rope's diameter. The entire image processing process must take real-time performance into consideration, ensuring that each frame can be processed at a speed sufficient to meet real-time monitoring requirements.

[0061] Step 5: Comprehensive analysis of tension and diameter data

[0062] The rope diameter and tension data are processed comprehensively to perform stress analysis.

[0063] Detailed Description: While the rope diameter is being acquired, a tension sensor records the rope's force information. These two sets of data are combined to analyze the trend of rope diameter changes with force, inferring the rope's stress state and deformation behavior. Data fitting and modeling methods can be used to generate a curve showing the relationship between diameter and force, helping to determine the rope's deformation characteristics under different stress conditions. This process can be implemented programmatically, calculating parameters such as the force transmission ratio and compression. The final analysis results can be used to optimize rope usage and improve the accuracy of safety monitoring.

[0064] Step 6: Data storage and further analysis

[0065] The acquired image data, diameter data and force sensor data are stored and analyzed.

[0066] All collected data, including the diameter information and corresponding tension data for each frame, is stored in a database or file for subsequent analysis and verification. To increase universality, ropes of different materials can be tested, including fiber ropes, wire ropes, and rubber ropes. By comparing the deformation of different rope types under the same conditions, the accuracy and stability of the system can be verified. This data can also be used to conduct further force simulation and structural reliability analysis, providing a scientific basis for rope selection and use in engineering applications.

[0067] In the specific implementation, step 1: build a measurement platform

[0068] Build a measuring platform to film and measure the rope in motion.

[0069] Detailed Description: A rope is connected to a tension sensor and motor at one end, and a weight at the other end, which is then rotated by a pulley. A camera is placed near the pulley to capture images of the rope's deformation under load. The platform design ensures that the rope undergoes noticeable deformation when stressed, facilitating subsequent imaging and analysis.

[0070] Step 2: Set the camera parameters and shoot

[0071] Set the camera parameters to ensure clear and accurate shooting.

[0072] Detailed Description: Run the pre-programmed camera control program and set camera parameters such as the capture time and exposure time. Adjust the camera's aperture and focal length for optimal imaging. Control the camera to capture real-time images of the rope, capturing deformation images under different stress conditions.

[0073] Step 3: Rope Movement and Real-time Image Acquisition

[0074] The rope is forced to move, and corresponding images and sensor data are collected simultaneously.

[0075] Detailed Description: The motor starts, driving the rope to pull the weight, causing the rope to deform under force. A camera continuously captures the moving rope, capturing each frame. During this process, a tension sensor records the rope's force in real time. The motor drags the weight upward, hovers for a period of time, and then descends, simulating the rope's movement and deformation under different conditions.

[0076] Step 4: Image processing and diameter calculation

[0077] The captured images are processed to calculate the diameter of the rope.

[0078] Detailed Description: The camera image is processed using OpenCV. First, a region of interest (ROI) is selected to ensure that only the rope portion near the hole is processed. Next, the image is denoised and thresholded to extract the rope outline. A contour extraction algorithm is used to obtain rope edge data, and the distance between the two edges is calculated to determine the rope diameter. Adaptive thresholding and morphological operations are used to improve the accuracy and stability of the calculation.

[0079] Step 5: Mechanical data analysis and storage

[0080] Analyze the measured data, perform further mechanical calculations and store them.

[0081] Detailed Description: Combine acquired rope diameter data with real-time tension sensor data to perform mechanical analysis. Calculate the rope's compression and transmission ratio under load, and plot a curve of rope diameter changes. Store all measured data in a file for subsequent detailed analysis and verification.

[0082] Implementation method 2: This implementation method further limits the vision-based method for measuring the hole of a moving rope provided in implementation method 1, and realizes different forces on the rope through a motor, a weight and a pulley.

[0083] Implementation method 3: This implementation method further limits the vision-based method for measuring the hole-passing motion rope provided in implementation method 1. The designated area is the rope portion near the hole, which is achieved through ROI area selection.

[0084] Implementation 4: This implementation further limits the vision-based moving rope hole measurement method provided in Implementation 1, and the processing includes denoising and thresholding.

[0085] Embodiment 5: This embodiment further limits the vision-based method for measuring the hole of a moving rope provided in embodiment 1, and obtains the diameter of the rope through the outline of the rope.

[0086] Implementation method 6: This implementation method further limits the vision-based method for measuring the hole of a moving rope provided in implementation method 1, and further includes the step of drawing a diameter change curve of the rope under different stress conditions.

[0087] Embodiment 7: This embodiment provides a visual-based motion rope hole measurement device, the device comprising:

[0088] A module for collecting images of rope deformation under different stress conditions;

[0089] Selecting a module in a designated area of ​​the image;

[0090] Processing the designated area to obtain a module of the rope diameter;

[0091] A module for obtaining the compression amount and transmission ratio of the rope under stress according to the rope diameter and real-time tension sensor data.

[0092] Implementation 8: This implementation provides a computer storage medium for storing a computer program. When the computer reads the computer program, the computer executes the method provided in Implementation 1.

[0093] Implementation 9: This implementation provides a computer, including a processor and a storage medium. When the processor reads the computer program stored in the storage medium, the computer executes the method provided in Implementation 1.

[0094] Embodiment 10: This embodiment provides a computer program product, which is a computer program. When the computer program is executed, the method provided in embodiment 1 is implemented.

[0095] Implementation Method 11: Combination Figure 1-9 This embodiment further illustrates the above technical solution through specific examples, specifically:

[0096] Existing technology for measuring ropes and related forces primarily focuses on static ropes. However, there is no effective method for measuring rope diameter during motion and deformation. However, some problems require real-time force analysis and measurement of the rope, which cannot be achieved with traditional measurement methods. Therefore, to address these issues and enable real-time monitoring and force analysis of moving ropes, a rope measurement solution based on external sensors is needed. This solution, coupled with indirect analysis of the rope's force and other related characteristics based on its own mechanical properties, enables real-time rope detection.

[0097] As the rope moves through the hole, the contact surface pressure causes the rope's dimensions in the compressive direction to be smaller than the diameter of the rope segment outside the hole. Therefore, when the rope and the hole experience sliding friction, the material at the contact point is squeezed and pushed to the sides by the hole, a phenomenon similar to the plowing effect in machining. To verify this conclusion, this experiment simulated the movement of the rope through the hole and captured the deformation near the hole through visual photography.

[0098] The steps of the vision-based sports rope measurement method are as follows:

[0099] Step 1: Build a rope measurement platform. As shown in the figure, connect the tension sensor and motor at one end of the rope, and the weight at the other end. Use a pulley in the middle to turn the rope. Set up a camera near the pulley to capture images of the rope deformation.

[0100] Step 2: Run the programmed camera program, modify the required camera parameters, adjust the camera's aperture and focal length, change the exposure time and shooting time, and start the camera to take pictures;

[0101] Step 3: Turn on the motor, causing it to drag the rope in motion while the camera continuously captures the rope. The motor drags the weight upward, hovers for a period of time, and then lowers the weight. Each frame captured by the camera undergoes image processing and outputs the calculated rope diameter. Simultaneously, the tension sensor outputs the tension data at that time.

[0102] The specific method for calculating the diameter is as follows:

[0103] In this experiment, it is believed that the edge contour of the part where the rope fits the hole remains unchanged. By processing the image with OpenCV, first select the ROI area of ​​the image and select the area near the hole for processing. Figure 3 As shown;

[0104] Then, the image is denoised and thresholded to keep only the rope part, e.g. Figure 4 As shown;

[0105] According to the binary image obtained by threshold processing, the image is contour extracted to obtain two contours on both sides of the rope. The obtained contour map is as follows Figure 5 As shown;

[0106] The two extracted contours can be clearly seen in the contour image. By finding the two closest points in the two contours, the pixel positions of the two points corresponding to the distance can be obtained. This method is obtained by traversing all the pixel points on the contour. Since the rope in contact with the pulley is approximately unchanged during the entire motion process, the normal vector corresponding to the point can be obtained through the upper arc local contour, and it is assumed that the normal vector does not change during the entire motion process. Through this normal vector, the distance between the two points of the rope in other pictures is calculated to obtain the diameter of the relatively stable rope, such as Figure 6 shown.

[0107] Step 4: Store the acquired rope diameter information, tension sensor information, etc. in a file for mechanical analysis.

[0108] Data processing:

[0109] In order to increase the universal applicability, different types of ropes were photographed and measured, and test pictures of different materials were taken. Figure 7 shown.

[0110] Among them, a large amount of data testing was carried out on the fiber rope. The weight of the weight was 5kg and the original diameter of the rope was 2.5mm. The tension and weight of the weight were calculated to obtain the force transmission ratio during movement. At the same time, the rope compression was obtained from the original length and the change in length. Based on the obtained data, the rope diameter change diagram was drawn as shown below: Figure 8 、 9 shown.

[0111] The above further describes the technical solution provided by the present invention in detail through several specific embodiments in order to highlight the advantages and benefits of the technical solution provided by the present invention. However, the several specific embodiments described above are not intended to limit the present invention. Any reasonable modification and improvement of the present invention, combination of embodiments and equivalent replacement based on the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for measuring the hole of a moving rope based on vision, characterized in that: Methods include: Steps for collecting images of the rope after deformation under different stress conditions; A step of selecting a designated area in the image; a step of processing the designated area to obtain the diameter of the rope; The step of obtaining the compression amount and transmission ratio of the rope under stress according to the diameter of the rope and real-time tension sensor data; The designated area is the rope portion near the hole, which is achieved by ROI area selection; Obtaining the diameter of the rope through the rope profile; The method also includes the step of drawing a diameter variation curve diagram of the rope under different stress conditions.

2. The method for measuring the hole of a sports rope based on vision according to claim 1, characterized in that: Different forces on the rope are achieved through motors, weights and pulleys.

3. The method for measuring the hole of a sports rope based on vision according to claim 1, characterized in that: The processing includes denoising and thresholding.

4. A vision-based motion rope hole measurement device, characterized in that: The device includes: A module for collecting images of rope deformation under different stress conditions; Selecting a module in a designated area of ​​the image; Processing the designated area to obtain a module of the rope diameter; A module for obtaining the compression amount and transmission ratio of the rope under stress according to the rope diameter and real-time tension sensor data; The designated area is the rope portion near the hole, which is achieved by ROI area selection; Obtaining the diameter of the rope through the rope profile; The invention also includes a module for drawing a diameter change curve diagram of the rope under different stress conditions.

5. A computer storage medium for storing a computer program, characterized in that When a computer reads the computer program, the computer executes the method according to claim 1 .

6. A computer comprising a processor and a storage medium, characterized in that When the processor reads the computer program stored in the storage medium, the computer executes the method according to claim 1 .

7. A computer program product, being a computer program, characterized in that When the computer program is executed, the method according to claim 1 is implemented.

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