Method for on-line automatic measurement of inter-wire gap of steel wire rope based on machine vision

Abstract

The machine vision-based steel wire rope strand gap online automatic measurement method comprises digital image acquisition and processing, digital image edge positioning and steel wire rope strand gap calculation. The digital image acquisition and processing is to acquire a digital image of the steel wire rope in the direction of the central axis of the steel wire rope, the length of which contains at least one lay pitch of the steel wire rope, and to process the image. The digital image edge positioning is to position the edge of the digital image acquired by the digital image acquisition at a sub-pixel level. The coordinates of the required sub-pixel and the physical size of the unit pixel are extracted. The steel wire rope strand gap calculation is to calculate the value of the strand gap according to the geometric characteristics of the strand gap by using the coordinates of the sub-pixel and the physical size of the unit pixel. The method can realize high-precision online automatic measurement of the steel wire rope strand gap.

Description

Technical Field

[0001] This invention belongs to the field of metal product wire manufacturing technology, and relates to an online automatic measurement method for the gap between steel wire rope strands based on machine vision. Background Technology

[0002] The strand gap in wire rope refers to the distance between adjacent strands on the same twisting circle. It is a crucial characteristic parameter in wire rope manufacturing, significantly impacting its performance during dynamic use. Maintaining a suitable strand gap is essential for ensuring optimal wire rope performance. Current technology primarily relies on manual measurement using feeler gauges, which suffers from low accuracy, failing to accurately reflect the actual strand gap and hindering real-time monitoring. Summary of the Invention

[0003] The purpose of this invention is to provide an online automatic measurement method for wire rope strand gap based on machine vision. This method relies on existing mature software and hardware technologies and algorithms or operators to achieve sub-pixel-level high-precision automatic measurement of wire rope strand gap, so as to meet the high-precision online monitoring requirements of wire rope strand gap during production or use.

[0004] The technical solution adopted in this invention is an online automatic measurement method for wire rope strand gap based on machine vision, including digital image acquisition and processing, digital image edge localization, and wire rope strand gap calculation; the specific operation steps are as follows:

[0005] Step 1: Acquire a digital image of the wire rope along the central axis of the wire rope, with a length including at least one wire rope lay length; and preprocess the image to meet the requirements for image edge localization.

[0006] Step 2: The edges of the acquired digital image are located at the pixel level by introducing an edge detection operator, and then the sub-pixel edge localization algorithm is used to locate the image edges at the sub-pixel level.

[0007] Step 3: Extract sub-pixel coordinates and physical dimensions of a unit pixel from the digital image of the steel wire rope;

[0008] The extreme points at the upper edge of the wire rope image are the highest points, and their coordinates are (x...). N g n y N g n The extreme point at the lower edge is the lowest point, and its coordinates are (x...). N d n y N d n ); The physical size k of a unit pixel in the Y-axis direction y The physical dimension k in the X-axis direction x;

[0009] Where N represents the Nth digital image (1≤N≤4), and n represents the nth extreme point or extreme point (n=1,2,3...); when n of the extreme point and the extreme point are equal, it means that they are the extreme points and extreme points that are closest to each other in the X-axis direction of the wire rope image;

[0010] Step 4: Calculate the strand gap of the wire rope using the sub-pixel coordinates and physical size of the unit pixel described in Step 3.

[0011] The invention is further characterized by:

[0012] Step 1 preprocessing can be any one or more of the following: ROI extraction, geometric correction, image enhancement or restoration, smoothing, and binarization.

[0013] Step 2: The edge detection operator can be any one of the following: Roberts operator, Sobel operator, Laplacian operator, LOG operator, or Canny operator.

[0014] Step 2: Subpixel edge localization algorithms include any one of the moment method, interpolation method, or fitting method.

[0015] The calculation method for the wire rope strand gap in step 4 is as follows:

[0016] The gap between strands in a wire rope can be divided into axial gap, cross-sectional gap, and actual gap.

[0017] The axial clearance δ of the wire rope strands Z It can be calculated according to formula (1):

[0018]

[0019] The cross-sectional gap δ of the steel wire strands J The formula for calculation is:

[0020] δ J =δ Z tanα (2)

[0021] The true gap δ of the steel wire strands S The formula for calculation is:

[0022] δ S =δ J cosα (3)

[0023] Where D is the diameter of the wire rope, d is the strand diameter, S is the lay pitch, and α is the lay angle of the wire rope;

[0024] The twist angle α can be calculated using equation (4):

[0025]

[0026] Step 4: The strand gap of a wire rope refers to the distance between two adjacent strands of the wire rope.

[0027] The beneficial effects of this invention are:

[0028] This invention provides a machine vision-based online automatic measurement method for wire rope strand gaps. Utilizing existing mature software and hardware technologies, algorithms, or operators, it achieves sub-pixel-level high-precision automatic measurement of wire rope strand gaps. This method enables high-precision automatic online monitoring of wire rope strand gaps during production or use, providing significant guidance for improving and optimizing wire rope design and production, and enhancing wire rope performance. Attached Figure Description

[0029] Figure 1 This is a schematic diagram illustrating the strand gap calculation in the online automatic measurement method for wire rope strand gap based on machine vision according to the present invention.

[0030] Figure 2 This is a schematic diagram of the axial clearance of the wire rope strands in this invention;

[0031] Figure 3 This is a schematic diagram of the cross-sectional gap of the steel wire rope strands of the present invention;

[0032] Figure 4 This is a schematic diagram of the actual gap between the wire rope strands of the present invention. Detailed Implementation

[0033] The present invention will now be described in detail with reference to the accompanying drawings and specific embodiments.

[0034] This invention provides an online automatic measurement method for wire rope strand gap based on machine vision. Figure 1 As shown, please follow these steps:

[0035] (1) Digital Image Acquisition and Processing

[0036] Digital image acquisition and processing involves using a digital camera to acquire digital images of a wire rope along its central axis, with a length including at least one wire rope lay length. The images are then processed (including but not limited to ROI extraction, geometric correction, image enhancement or restoration, smoothing, binarization, etc.) to meet the requirements for image edge localization.

[0037] (2) Digital image edge localization

[0038] Edge detection operators (such as Roberts operator, Sobel operator, Laplacian operator, LOG operator, Canny operator, etc.) are introduced to perform pixel-level positioning of the edges of the acquired digital images. Then, sub-pixel edge positioning algorithms (such as moment method, interpolation method, fitting method, etc.) are used to perform sub-pixel-level positioning of the image edges.

[0039] (3) Data Acquisition

[0040] Based on the measurement and calculation requirements, the sub-pixel coordinates and physical dimensions of unit pixels in the digital image are extracted. For example... Figure 1 As shown, in this invention, the extreme point at the upper edge of the wire rope image is called the extreme point, and its coordinates are (x... N g n y N g n The extreme point at the lower edge is the lowest point, and its coordinates are (x...). N d n y N d n To extract sub-pixel coordinates, including the coordinates of the extreme points (x...). N g n y N g n ) or the coordinates of the lowest point (x N d n y N d n The physical size k of a unit pixel in the Y-axis direction. y The physical dimension k in the X-axis direction x This invention uses the coordinates of the highest point as an example to illustrate the method for measuring and calculating the gap between steel wire rope strands.

[0041] N represents the Nth digital image (N≥1), and n represents the nth high or low point (n=1,2,3...).

[0042] (4) Calculation of wire rope strand gap

[0043] The calculation of the wire rope strand gap is performed using the data extracted from the data acquisition, and the value of the strand gap is calculated based on the geometric characteristics of the strand gap.

[0044] The strand gap of a wire rope refers to the distance between two adjacent strands, and can be divided into axial gap (such as...). Figure 2 As shown), cross-sectional gap (such as) Figure 3 (as shown) and the actual gap (as shown) Figure 4 (As shown).

[0045] Given the wire rope diameter D, strand diameter d, and lay length S (D, d, and S can all be obtained using existing technology and measurement methods), the lay angle α of the wire rope can be calculated using equation (1):

[0046]

[0047] The axial clearance δ of the wire rope strands Z It can be calculated according to formula (2):

[0048]

[0049] The cross-sectional gap δ of the steel wire strands J The formula for calculation is:

[0050] δ J =δ Z tanα (3)

[0051] The true gap δ of the steel wire strands S The formula for calculation is:

[0052] δ S =δ J cosα (4)

[0053] This invention provides a machine vision-based online measurement method for wire rope strand gaps. It utilizes sub-pixel edge localization technology based on machine vision, combined with the geometric features of the wire rope strand gaps, to perform high-precision automatic measurement and calculation of the strand gaps. This method provides a way to achieve high-precision online automatic measurement of wire rope strand gaps by relying on existing mature software and hardware technologies and algorithms or operators. Using this method, higher-precision automatic online monitoring of wire rope strand gaps during production or use can be achieved, which has significant guiding significance for improving and optimizing wire rope design and production, and enhancing the performance of wire ropes.

[0054] The above description is only a preferred embodiment of the present invention. Without departing from the main principles of the present invention, several modifications and improvements can be made, all of which fall within the protection scope of the present invention.

Claims

1. A machine vision-based online automatic measurement method for wire rope strand gap, comprising digital image acquisition and processing, digital image edge localization, and wire rope strand gap calculation; characterized in that, The specific operating steps are as follows: Step 1: Acquire a digital image of the wire rope along the central axis of the wire rope, with a length including at least one wire rope lay length; and preprocess the image to meet the requirements for image edge localization. Step 2: The edges of the acquired digital image are located at the pixel level by introducing an edge detection operator, and then the sub-pixel edge localization algorithm is used to locate the image edges at the sub-pixel level. Step 3: Extract sub-pixel coordinates and physical dimensions of a unit pixel from the digital image of the steel wire rope; The extreme point at the upper edge of the wire rope image is the highest point, and its coordinates are ( ). x N g n， y N g n The extreme point at the lower edge is the lowest point, and its coordinates are (). x N d n， y N d n ); unit pixel in Y Physical dimensions in the axial direction k y ,exist X Physical dimensions in the axial direction k x ; in, N Indicates the first N Zhang Digital Images ( 1≤N≤4 ), n Indicates the first n A very high point or a very low point ( n=1，2，3... ); when the highest point and the lowest point n When they are equal, it means that they are in the wire rope image. X The closest extreme high and low points along the axial direction; Step 4: Calculate the strand gap of the wire rope using the sub-pixel coordinates and physical size of the unit pixel described in Step 3.

2. The online automatic measurement method for wire rope strand gap based on machine vision according to claim 1, characterized in that: The preprocessing described in step 1 can be any one or more of the following: ROI extraction, geometric correction, image enhancement or restoration, smoothing, and binarization.

3. The online automatic measurement method for wire rope strand gap based on machine vision according to claim 1, characterized in that: The edge detection operator in step 2 can be any one of the Roberts operator, Sobel operator, Laplacian operator, LOG operator, and Canny operator.

4. The online automatic measurement method for wire rope strand gap based on machine vision according to claim 1, characterized in that: Step 4: The strand gap of a wire rope refers to the distance between two adjacent strands of the wire rope.

5. The online automatic measurement method for wire rope strand gap based on machine vision according to claim 1, characterized in that: The subpixel edge localization algorithm described in step 2 includes any one of the moment method, interpolation method, and fitting method.

Images