A cable core counting method, a cable intelligent detector and a detection method thereof

Abstract

The application discloses a cable inner core counting method, a cable intelligent detector and a detection method thereof. The cable inner core counting method is as follows: taking the width and height of a wire core imaging area as the major axis and the minor axis of an ellipse respectively, and then unfolding the formed ellipse into an equivalent rectangle II, designing a transverse segmentation line of each layer of wire cores, dividing the equivalent rectangle II into subgraphs of each layer by taking the transverse segmentation line as a boundary, reducing each layer to one row in the vertical direction by the gray average method, calculating the gap between the inner cores of the wire cores, taking the maximum number interval value as the width of the inner core, calculating the number of the inner cores of each layer, and finally obtaining the total inner core count by multi-layer aggregation. The application can count the number of wire cores by distinguishing the edges of the inner cores, and can accurately count the number of wire cores even when the outlines of the inner cores are not clear, thereby solving the technical problem that the existing cable intelligent detector cannot accurately count the inner cores of the cable.

Description

TECHNICAL FIELD

[0001] The present application relates to a counting method, in particular to a cable core counting method, a cable intelligent detector using the core counting method, and an intelligent detection method of the cable intelligent detector. BACKGROUND

[0002] Regardless of power cable or overhead cable, there are their own specification requirements, the interpretation and representation method of the model specification of the cable is taken as an example of power cable, the model and variety of power cable mainly have the following aspects 35kV and below power cable model and product representation method.

[0003] (1) The first letter of the Chinese pinyin in capital letters represents the insulation type, conductor material, inner protective layer material and structural characteristics. For example, Z represents paper (zhi); L represents aluminum (lv); Q represents lead (qian); F represents phase (fen); ZR represents flame retardant (zuran); NH represents fireproof (naihuo).

[0004] (2) The outer protective layer is represented by numbers, which has two digits. No number represents no armored layer and no outer coating. The first digit represents the armor, and the second digit represents the outer coating. For example, thick steel wire armored fiber outer coating is represented as 41.

[0005] (3) The cable model is generally arranged in the order of: insulation material; conductor material; inner protective layer; outer protective layer.

[0006] (4) The cable product is represented by model, rated voltage and specification. The method is to add Arabic numerals to explain the rated voltage, core number and nominal cross-sectional area after the model. For example, VV42-10 3x50 represents a copper core, polyvinyl chloride insulation, thick steel wire armored, polyvinyl chloride sheath, rated voltage 10kV, 3 cores, and nominal cross-sectional area 50mm2 power cable. The format can be: category-[1: type, use] / [2: conductor] / [3: insulation] / [4: inner protective layer] / [5: structural characteristics] / [6: outer protective layer or derivative] / [7: make manganese] 6. Code and meaning of each part of power cable model:

[0007] (5) Category: ZR (flame retardant); NH (fireproof); BC (low smoke low halogen); E (low smoke halogen-free); K (control cable class); DJ (electronic computer); N (agricultural direct burial); JK (overhead cable class); B (cloth wire).

[0008] Therefore, regardless of power cable or overhead cable, the specification has a national standard, not defined by the manufacturer at will. For example, Figure 1As shown, the left area is a schematic diagram of the port of the power cable, and the right area is a schematic diagram of the port of the overhead cable. The power cable comprises, from inside to outside, a core 1, a conductor shielding layer 2, an insulation layer 3, and an insulation shielding layer 4. The core 1 is a core area and comprises a plurality of inner cores 5 arranged in a plurality of concentric ring structures with one of the inner cores 5 as the center, one ring being nested in another ring. The overhead cable does not comprise the insulation shielding layer 4, and of course, there are some differences in the respective size specifications.

[0009] The current cable intelligent detector has the function of calculating the thickness of the insulation layer through pictures, although the accuracy needs to be improved, and it is a great progress compared with the manual measurement method. However, the function is too single, lacks the cable automatic recognition function, and lacks the shielding layer thickness calculation function, the core 1 area calculation function, and the inner core 5 number calculation function. SUMMARY

[0010] To solve the technical problem that the existing cable intelligent detector cannot accurately count the inner cores of the cable, the application provides an inner core counting method of a cable, a cable intelligent detector adopting the inner core counting method, and an intelligent detection method of the cable intelligent detector.

[0011] The application adopts the following technical scheme: an inner core counting method of a cable, the inner core counting method comprising the following steps:

[0012] Step S11, converting a to-be-recognized image into a brightness grayscale image, wherein the to-be-recognized image is a front view image obtained by imaging the cable port in the center area of the picture at a target focal length and a target imaging distance with a front view angle;

[0013] Step S15, obtaining a rectangular region subgraph at the n*n pixel region position in the center of the brightness grayscale image, the value of n satisfying that the area of n*n is always within the imaging area of the cable, and taking the lowest grayscale value in the part of pixels with a grayscale value greater than N in the rectangular region subgraph as another threshold value to perform binaryzation processing on the brightness grayscale image;

[0014] Step S16, finding the core area contour of the cable in the brightness grayscale image after the binaryzation processing by using a contour finding function, and taking the corresponding core area frame as the core imaging area;

[0015] Step S38, taking the width and height of the core imaging area as the major axis and the minor axis of an ellipse respectively, and unfolding the formed ellipse into an equivalent rectangle II;

[0016] Step S39, reducing the equivalent rectangle II to 1 column to the left in a grayscale average manner, and taking the minimum grayscale value in the equivalent rectangle II as a to obtain the horizontal segmentation line of each layer of the core in the horizontal direction;

[0017] Step S310, divide the equivalent rectangle into subgraphs of each layer, and reduce each layer to one row in the vertical direction by the average of the gray scale, and count the minimum gray scale in the two;

[0018] Step S311, count the number of pixels with a minimum gray scale less than the target threshold value as the gap between the inner cores of the wire core;

[0019] Step S312, calculate the distance between the minimum gray scale, and count the number of the same distance by histogram, and take the maximum number of distance as the width of the inner core;

[0020] Step S313, calculate the number of inner cores of each layer according to the total width of the equivalent rectangle minus the gap of the inner core, and then divided by the width of the inner core;

[0021] Step S314, multiple layers are summarized to obtain the total number of inner cores.

[0022] As a further improvement of the above scheme, the following steps are further included before step S15:

[0023] Step S12, denoising the brightness gray scale image to remove noise points.

[0024] As a further improvement of the above scheme, the value of N is 128.

[0025] As a further improvement of the above scheme, the value of n is 100.

[0026] As a further improvement of the above scheme, if the cable port is not necessarily in the center area of the picture, the inner core counting method includes the following steps:

[0027] Step S13, according to the picture position and picture diameter of the cable port in the reference image, a corresponding picture area one is circled on the brightness gray scale image, the minimum value of the pixel gray scale outside the picture area one is counted and taken as a threshold value, and the brightness gray scale image is binarized; wherein, the reference image is a front view image obtained by taking an image of the cable port of the largest size specification cable at the target focal length and the target image distance with a front view angle;

[0028] Step S14, using a contour finding function, find the overall contour of the cable port in the binarized image to be recognized, and calculate the corresponding overall area frame, which is the cable imaging area;

[0029] And in step S15, a rectangular region subgraph is obtained at the center of the cable imaging area.

[0030] Further, in step S13, the minimum value of the pixel gray scale outside the picture area and located at one corner of the gray scale image is counted as the threshold value.

[0031] The application also provides a cable intelligent detector adopting the inner core counting method of any cable.

[0032] The application also provides a detection method of the cable intelligent detector, which comprises the inner core counting method of any cable.

[0033] As a further improvement of the above scheme, the detection method further comprises the following steps:

[0034] In step S315, the cable intelligent detector is networked through the 4G module, linked to the background, and remotely upgraded.

[0035] Further, the detection method further comprises the following steps:

[0036] In step S316, the pictures that cannot be recognized by the cable intelligent detector are returned through the 4G network, and the background updates the detector database through training.

[0037] Compared with the prior art, the application has the following beneficial effects:

[0038] (1) The inner core counting method of the cable can count the number of cores according to the area by distinguishing the inner core and the edge of the inner core, and can accurately count even when the outline between the inner cores is not clear, thereby solving the technical problem that the existing cable intelligent detector cannot accurately count the inner cores of the cable.

[0039] (2) The intelligent detection method of the cable intelligent detector is updated in real time: the equipment is networked through the 4G module, can be linked to the background, and remotely upgrades the machine, thereby ensuring that the equipment version is the latest.

[0040] (3) The cable types or cable models that cannot be recognized by the cable intelligent detector are timely updated in the database, the pictures that cannot be recognized are returned through the 4G network, the background updates the detector database through training, the database is gradually improved, and the recognition rate of the detector is improved.

[0041] (4) The inner core counting method of the cable is applied to the intelligent detection method of the cable intelligent detector, which can enrich the intelligent detection function of the cable intelligent detector and realize the functional diversification of the cable intelligent detector. BRIEF DESCRIPTION OF DRAWINGS

[0042] Figure 1 It is a schematic diagram of the port of the existing power cable and overhead cable.

[0043] Figure 2 Flow chart of the automatic identification method of the cable type provided in Embodiment 1 of the present application.

[0044] Figure 3 Flow chart of the automatic identification method of the cable type provided in Embodiment 1 of the present application. Figure 2 Flow chart of the identification method of the cable imaging area and the core imaging area adopted in the automatic identification method.

[0045] Figure 4 Flow chart of the automatic identification method of the cable type provided in Embodiment 1 of the present application. Figure 2 Schematic diagram of the picture processing in the automatic identification method.

[0046] Figure 5 Flow chart of the parameter detection method of the cable provided in Embodiment 2 of the present application.

[0047] Figure 6 Flow chart of the core counting method of the cable provided in Embodiment 3 of the present application.

[0048] Figure 7 Flow chart of the core counting method of the cable provided in Embodiment 3 of the present application. Figure 6 Schematic diagram of the equivalent rectangle II formed by the core counting method in Embodiment 3 of the present application.

[0049] Figure 8 Flow chart of the intelligent detection method of the cable intelligent detector provided in Embodiment 4 of the present application. DETAILED DESCRIPTION

[0050] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work fall within the scope of protection of the present application.

[0051] Embodiment 1

[0052] The present embodiment discloses an automatic identification method of the cable type, which is used to automatically identify whether the cable belongs to a power cable or an overhead cable, and further identify the specific model of the cable after identifying the cable type. The automatic identification method of the cable type can be embedded in the CPU of the cable intelligent detector in the form of software, so that the cable intelligent detector can implement the automatic identification method of the cable type of the present application when detecting.

[0053] Please refer to Figure 2 The automatic identification method of the cable type includes the following steps.

[0054] Firstly, in step S10, the cable imaging area and the core imaging area are identified in the image to be identified.

[0055] Please combine Figure 3 The identification method of the cable imaging area and the core imaging area can be various, but the self-developed identification method of the cable imaging area and the core imaging area is adopted in the present application, which includes the following steps S11-S16.

[0056] Step S11, the image to be identified is converted into a brightness gray image.

[0057] The image to be identified is an image taken at the cable port, and is a front view image obtained by taking the image of the cable port at a front view angle. The existing cable intelligent detector can be used to focus on the cable port with a designed focal length. The focal length is designed to achieve a fixed focal length, and the distance between the camera and the cable port is also a predetermined distance. The cable port is located in the center area of the image taking picture as much as possible. The advantage of this is that the front view image of the largest size cable (whether it is a power cable or an overhead cable, the largest diameter cable) can be obtained at a fixed focal length and a fixed image taking distance. At this time, the parameters of the front view image are known, which provides a reference for subsequent automatic recognition of the cable type.

[0058] Therefore, the image to be identified in step S11 is a front view image obtained by taking the image of the cable port of the cable at a front view angle under a designed focal length and an image taking distance (i.e. under a target focal length and a target image taking distance). In actual operation, the cable intelligent detector can be provided with a positioning frame for positioning the cable and positioning the cable port in the center area of the camera picture of the cable intelligent detector, and the distance between the cable port and the camera is a target distance. Thus, the camera can take the image of the cable port in the center area of the picture at a front view angle under a target focal length and a target image taking distance to obtain a front view image.

[0059] The conversion of the brightness gray image can be converted by using the HSV color space conversion technology, and can also be converted by using image inversion, logarithmic transformation, gamma transformation and the like as long as the brightness gray conversion can be performed.

[0060] Step S12, the luminance gray image is denoised to remove the noise points. Of course, this step can be skipped, but the noise reduction processing can improve the accuracy of subsequent identification. The removal of noise points can use Gaussian blur calculation method, Gaussian blur is also called Gaussian smoothing, which is widely used in image processing software such as Adobe Photoshop, GIMP and Paint.NET. Its role is to make the image blurred and smooth, and it is usually used to reduce image noise and reduce detail levels. The image generated by this blur technique has a visual effect like looking at the image through a frosted glass, which is obviously different from the out-of-focus imaging effect of the lens and the effect in the ordinary lighting shadow. Gaussian filter is a linear smoothing filter, which is suitable for eliminating Gaussian noise and is widely used in image processing noise reduction process. In simple terms, Gaussian filtering is a process of weighted average of the entire image, and the value of each pixel point is obtained by weighted average of itself and other pixel values in the neighborhood. The Gaussian blur calculation method is not described in detail here.

[0061] Step S13, according to the picture position and picture diameter of the cable port in the reference image, the corresponding picture area I is circled on the denoised luminance gray image, the minimum value of the pixel gray scale outside the picture area I is counted as a threshold, and the denoised luminance gray image is binarized.

[0062] This reference image can be an image stored in the cable intelligent detector in advance, which is a front view image obtained by taking an image of the cable port of the largest size specification cable at the target focal length and the target image distance with a front view angle. Because the focal length and the image distance used in the image to be identified and this reference image are the same, the cable port in the center area of the image has the same size ratio and approximately the same center point. Because the cable in the reference image is the largest in size, the corresponding picture area I in the denoised luminance gray image is defined by the picture position and the picture diameter of the cable port in the reference image, and the picture area I obtained must contain the actual imaging area of the cable port of the cable to be identified. Therefore, the area outside the picture area I must be all background, and by using the minimum value of the pixel gray scale outside the picture area I as a threshold, the binarization processing is implemented, and then the image to be identified after binarization processing can distinguish the imaging of the cable port from the background, so as to facilitate the subsequent identification of the overall outline of the cable port.

[0063] In actual operation, the minimum value of the pixel gray scale of one part selected outside the picture area I can be counted as a threshold. For example, Figure 4As shown, the left side is slightly blocked when taking the image, resulting in incomplete imaging of the cable port, but one advantage of the present application is that it does not affect subsequent identification of the cable type and model specification even in such a case. For the case of Figure 4 the upper right part of the image, the minimum pixel gray value outside the cable port imaging area (i.e., outside the image area) can be calculated.

[0064] In step S14, a contour finding function is used to find the overall contour of the cable port in the binary processed image to be identified, and the corresponding overall area frame 6 is calculated, i.e., the cable imaging area.

[0065] The contour finding function is widely used, and a typical application is to define the boundary between the imaging and the background in PS image processing. The contour finding function, such as the OpenCV contour function, corresponds to a series of points that represent a curve in the image in a certain way. In OpenCV, a contour function is represented by a series of two-dimensional vertices, and cv2.findContours() calculates the contour from a two-dimensional image. The image processed by it can be an image with edge pixels obtained from the cv2.Canny() function, or an image obtained from the cv2.threshold() and cv2.adaptiveThreshold() functions, in which case the edge is the boundary between the positive and negative areas. The overall contour finding technique is not described in detail here, and the focus of the present application is not to design a contour finding function, but to call an existing contour finding function to find the overall contour of the cable port outside the binary processed image to be identified. After the overall contour is obtained, the points on the outermost edge in the positive direction can be set according to the overall contour to set the corresponding overall area frame 6, such as the wireframe on the outermost edge in Figure 4 the image in Fig. 6. The cable imaging area of the cable port is within the overall area frame 6.

[0066] In step S15, a rectangular region sub-image is obtained at the center of the cable imaging area, and the minimum gray value of the pixels with a gray value greater than N in the rectangular region sub-image is calculated as another threshold value, and the image in the cable imaging area is binary processed using the threshold value.

[0067] The value of n satisfies that the area of n x n is always within the area of the cable imaging area, so the value of n cannot be too large, otherwise the rectangular region sub-image cannot be filled with the wire core, and in this embodiment, n = 100 is sufficient. N is generally taken as 128 after evaluation based on experience. The concept of this step is similar to step S13 and will not be repeated here.

[0068] Step S16, using a contour finding function, find the contour of the core area of the cable in the binary processed cable imaging area, and then find the corresponding core area outer frame 7, i.e. the core imaging area, as shown in the second line frame. Figure 3 The concept of this step is similar to step S14, which will not be repeated here.

[0069] Step S17, take a narrow strip area with a width of W pixels and a height of h pixels as the center of the core imaging area, get a transverse narrow strip area subgraph, and reduce the transverse narrow strip area subgraph to a transverse narrow strip area subgraph with a height of 1 pixel by averaging the gray scale. The width of the cable imaging area is W pixels, and the value of h is less than half the height of the core imaging area of the smallest specification cable under the same imaging conditions.

[0070] In this embodiment, h is 100, which is an empirical value. Here, the average gray scale means that the pixel values are averaged.

[0071] Step S18, for the reduced transverse narrow strip area subgraph, calculate the gray scale difference of every two pixels from the left W-2 pixels, and get the corresponding gradient graph.

[0072] Step S19, traverse all the gradient values of the gradient graph from left to right, and respectively find the points with the maximum positive and negative gradient values. Define the point with the maximum positive gradient value as positive, and the point with the maximum negative gradient value as negative. If the left half of the cable imaging area to the middle part of the core imaging area or the right half of the cable imaging area to the middle part of the core imaging area meets the positive-negative-positive-negative change rule, and the corresponding points are numbered 1-4 in order, if the distance between points 2-1 and 3-2 is greater than the distance between points 2-1 and 4-3, then it is determined that the left half of the cable imaging area meets the power cable characteristic or the right half of the cable imaging area meets the power cable characteristic. In this embodiment, the standard of much greater than is at least 3 times the distance.

[0073] Step S110, refer to steps S17 to S19, perform longitudinal narrow strip area subgraph acquisition, corresponding gradient graph acquisition, and corresponding gradient value analysis, to evaluate whether the upper and lower halves of the cable imaging area meet the power cable characteristic.

[0074] Step S111, combine the four power cable characteristic discrimination results of the upper, lower, left and right halves, if more than or equal to two power cable characteristic discrimination results are obtained, it is determined that the cable in the image to be identified meets the power cable characteristic and belongs to power cable, otherwise it is determined to belong to overhead cable.

[0075] Once the cable type is identified, the cross-sectional area of the cable is compared with the cross-sectional areas of various cables in the cable library, and the cable model corresponding to the cross-sectional area is quickly identified. In this embodiment, the cable model identification method is as follows: on the premise that the cable type is identified, the cross-sectional area of the cable is compared with the cross-sectional areas of various cables of the corresponding cable type in the cable library, and the corresponding cable model in the cable library is taken as the cable model of the cable according to the matched cross-sectional area.

[0076] The present application can automatically identify the cable type without manual input, reducing input errors. Compared with the existing cable type automatic identification method, the cable type automatic identification method of the present application can normally identify the image even if it is slightly blocked or has defects. Moreover, the present application automatically identifies the cable type by comprehensively considering the identification results of the four power cable features of the upper, lower, left, and right halves, so there is almost no possibility of misjudgment.

[0077] Through the cable type automatic identification method of the present application, the intelligent detection method of the cable intelligent detector can be applied to enrich the intelligent detection function of the cable intelligent detector and realize the functional diversification of the cable intelligent detector.

[0078] Embodiment 2

[0079] The present embodiment discloses a parameter detection method of a cable, which can include the thicknesses of the insulation shielding layer, the insulation layer, and the core shielding layer, and relates to a calculation method of the insulation layer thickness of the cable, a calculation method of the insulation shielding layer thickness of the cable, and a calculation method of the conductor shielding layer thickness of the cable. The parameters can also include the area of the core, and relate to a calculation method of the area of the core.

[0080] Please refer to Figure 5 The parameter detection method of the cable of the present embodiment includes the following steps.

[0081] Step S11, converting the to-be-identified image into a luminance grayscale image, wherein the to-be-identified image is a front view image obtained by imaging the cable port of the cable at a target focal length and a target imaging distance at a front view angle.

[0082] Step S12, denoising the luminance grayscale image to remove noise points.

[0083] Step S13, according to the picture position and the picture diameter of the cable port in the reference image, a corresponding picture area one is circled on the brightness gray scale image, the minimum value of the pixel gray scale outside the picture area one is counted and taken as a threshold value, and the brightness gray scale image is binarized according to the threshold value; wherein the reference image is a front view image obtained by imaging the cable port of the cable with the largest size specification at the target focal length and the target imaging distance and under the front view angle.

[0084] Step S14, the overall contour of the cable port is found in the to-be-recognized image after the binarization processing by using a contour finding function, and a corresponding overall area frame, i.e. a cable imaging area, is obtained.

[0085] Step S15, a rectangular area subgraph is obtained at the n×n pixel area position in the center of the cable imaging area, the value of n satisfies that the area of n×n is always within the area of the cable imaging area, the lowest gray scale value in the part of the pixels with the gray scale value greater than N in the rectangular area subgraph is counted as another threshold value, and the image in the cable imaging area is binarized according to the threshold value.

[0086] Step S16, the contour of the wire core area of the cable is found in the cable imaging area after the binarization processing by using a contour finding function, and a corresponding wire core area frame, i.e. a wire core imaging area, is obtained.

[0087] Steps S11-S16 can be respectively referred to steps S11-S16 of embodiment 1, and will not be repeated here.

[0088] Step S27, the center points of the overall area frame 6 and the wire core area frame 7 are calculated respectively, the center point of the overall area frame 6 is moved to the center point of the wire core area frame 7, and the size of the overall area frame 6 is adjusted. The adjustment method is to increase the width and the height of the overall area frame 6 respectively, and the increase amount of the width and the height is the absolute value of the horizontal deviation and the absolute value of the vertical deviation between the two center points respectively.

[0089] Step S28, the width and the height of the adjusted overall area frame are taken as the major axis and the minor axis of an ellipse respectively, and the ellipse is unfolded into an equivalent rectangle one again, according to the mapping relationship between the points on the ellipse and the points on the equivalent rectangle, the pixel coordinate relationship of the adjusted overall area frame is represented as:

[0090]

[0091]

[0092]

[0093]

[0094]

[0095] In the formula, (x, y) represents the coordinates of the pixel on the adjusted overall region frame; θ is the angle of (x, y) on the elliptical coordinate system, (x1, y1) is the coordinates of the pixel on the equivalent rectangle one, a is the long axis of the ellipse, and b is the short axis of the ellipse.

[0096] In step S29, for the equivalent rectangle region one, the width and height are respectively W2 and H2, the region is divided into a narrow strip region every 50 pixels from top to bottom, a total of H2 / 50 narrow strip regions, a subgraph of each narrow strip region is intercepted, and the narrow strip region subgraph is reduced to a narrow strip region subgraph with a height of 1 pixel in a gray average manner. Wherein, W2=a, H2=2*a+2*b.

[0097] In step S210, the gray difference value of each pixel on the left side of the narrow strip region subgraph is calculated, and the corresponding gradient graph is obtained.

[0098] In step S211, all gradient values of the gradient graph of each narrow strip region subgraph are traversed from left to right, and the maximum points of multiple gradient values with the maximum positive change and the maximum negative change are obtained. If the multiple gradient maximum points from the outermost edge of the cable imaging region to the middle part of the core imaging region meet the positive (coded as No. 1), negative (coded as No. 2), positive (coded as No. 3), and negative (coded as No. 4), and each gradient maximum point is within the predetermined interval of the overall region frame 6 and the core region frame 7 (i.e. near the adjusted cable imaging region and the core imaging region), it is determined as the edge of the insulation layer and the edge of the shielding layer. In this embodiment, "nearby" means that the interval between each gradient maximum point and the nearest frame is less than one tenth of the interval between two frames. The interval between the adjacent two positive and negative gradient maximum points is determined as the edge of one layer.

[0099] If the cable is a power cable, the adjacent two positive and negative gradient maximum points are sequentially determined as the insulation shielding layer, the insulation layer, the core shielding layer, and the core from left to right. If the cable is an overhead cable, the adjacent two positive and negative gradient maximum points are sequentially determined as the insulation layer, the core shielding layer, and the core from left to right.

[0100] In step S212, the distance between the two edges of each layer is determined under the pixel coordinate relationship, and the corresponding thickness is calculated according to the distance multiplied by the proportional relationship between the pixel and the actual size.

[0101] The proportion relationship between the pixel and the actual size is calculated according to the imaging rule, and the thicknesses of the insulation layer and the shielding layer are calculated according to the found insulation layer edge and shielding layer edge. The imaging rule in this embodiment refers to the fixed focal length, the imaging distance and the proportion relationship of the actual size. According to the pixel coordinate relationship, the coordinates of the insulation layer edge and the shielding layer edge in the image to be recognized are known, so that the corresponding image thicknesses between the insulation layer, the insulation shielding layer and the conductor shielding layer in the image to be recognized are obtained, and then the actual thicknesses of the insulation layer, the insulation shielding layer and the conductor shielding layer are obtained by multiplying the proportion relationship between the pixel and the actual size.

[0102] In step S213, one layer to be calculated is defined as layer A, the thickness of layer A in H2 / 50 narrow strip regions is counted, and the narrow strip region with the minimum thickness of layer A is found out. Based on this, the data of other narrow strip regions are obtained every 60 degrees, and a total of 6 measurement data of layer A in equally spaced narrow strip regions are returned.

[0103] According to the national standard, 6 measurement data are calculated every 60 degrees. For example, the thicknesses of the insulation layer in H2 / 50 narrow strip regions are counted, the narrow strip region with the minimum thickness is found out, and based on this, the data of other narrow strip regions are obtained every 60 degrees, and a total of 6 measurement data of the thicknesses of the insulation layer in equally spaced narrow strip regions are returned. Therefore, the final measurement data of the thickness of the insulation layer have 6 groups. In this way, the cable intelligent detector can provide 6 groups of data, each group of data including the thicknesses of the insulation layer, the insulation shielding layer and the core shielding layer, and the measurement relationship between adjacent two groups of data is 60 degrees.

[0104] The application can accurately calculate and measure the thicknesses of the insulation layer, the insulation shielding layer and the core shielding layer by distinguishing the insulation layer edge and the shielding layer edge. The parameter detection method of the cable can be applied to the intelligent detection method of the cable intelligent detector, so that the intelligent detection function of the cable intelligent detector can be enriched, and the diversification of the cable intelligent detector can be realized.

[0105] The detection method of the cable intelligent detector can also calculate the area of the core. Specifically, the core imaging region is processed as an ellipse, and the width and height of the core imaging region are respectively taken as the major axis and the minor axis of the corresponding ellipse. The area of the ellipse corresponding to the core imaging region is calculated according to the proportion relationship between the pixel and the actual size, and is taken as the area of the core.

[0106] Embodiment 3

[0107] The embodiment discloses a core counting method of a cable. Figure 6 The core counting method of the cable of the embodiment includes the following steps.

[0108] Step S11, converting the image to be identified into a luminance gray scale image, wherein the image to be identified is a front view image obtained by imaging the cable port in the center area of the picture at a target focal length and a target imaging distance with a front view angle.

[0109] Step S12, denoising the luminance gray scale image to remove noise points.

[0110] If the cable port is not necessarily in the center area of the picture, step S13 is performed, and a corresponding picture area I is circled on the luminance gray scale image according to the picture position and the picture diameter of the cable port in the reference image, the minimum value of the pixel gray scale outside the picture area I is counted as a threshold value, and the luminance gray scale image is binarized in this way; wherein the reference image is a front view image obtained by imaging the cable port of the largest size specification cable at the target focal length and the target imaging distance with a front view angle.

[0111] Step S14, using a contour finding function to find the overall contour of the cable port in the image to be identified after binarization, and to find the corresponding overall area frame, i.e. the cable imaging area.

[0112] Step S15, obtaining a rectangular region sub-image at the center n x n pixel region position of the luminance gray scale image, the value of n satisfies that the area of n x n is always within the area of the cable imaging area, and the minimum gray scale value in the part of the pixels with a gray scale value greater than N in the rectangular region sub-image is counted as another threshold value, and the luminance gray scale image is binarized in this way.

[0113] Step S16, using a contour finding function to find the wire core area contour in the luminance gray scale image after binarization, and to find the corresponding wire core area frame, i.e. the wire core imaging area.

[0114] Steps S11-S16 can be referred to steps S11-S16 of embodiment 1, which will not be repeated here.

[0115] Please refer to Figure 7 Step S38, taking the width and height of the wire core imaging area (i.e. the wire core area frame 7) as the major axis and the minor axis of an ellipse respectively, and unfolding the formed ellipse into an equivalent rectangle II.

[0116] Step S39, reducing the equivalent rectangle II to 1 column to the left in a gray scale average manner, counting the minimum gray scale value I therein to obtain the horizontal segmentation line of each layer of wire core in the horizontal direction.

[0117] Step S310, divide the two equivalent rectangles into subgraphs of each layer with the transverse division line as the boundary, and reduce each layer to one row in the vertical direction by the gray scale average method, and count the two gray scale minimum values.

[0118] Step S311, count the number of pixels with a gray scale minimum value less than the target threshold value as the gap between the inner cores of the wire core.

[0119] Step S312, calculate the distance between the two gray scale minimum values, and count the number of the same distance by histogram method, and take the maximum number of distance values as the width of the inner core.

[0120] Step S313, calculate the number of inner cores of each layer according to the total width of the two equivalent rectangles minus the gap number of the inner core, and then divided by the width of the inner core.

[0121] Step S314, aggregate the multi-layer to obtain the total number of inner cores.

[0122] Please refer to Figure 7 The inner core counting method of the cable of the present application can accurately count even if the outline between the inner cores is not clear. By applying the inner core counting method of the cable of the present application to the intelligent detection method of the cable intelligent detector, the intelligent detection function of the cable intelligent detector can be enriched, and the diversification of the function of the cable intelligent detector can be realized.

[0123] Embodiment 4

[0124] The cable intelligent detector of the present embodiment can adopt the automatic recognition method of the cable type of embodiment 1, and can also adopt the parameter detection method of the cable of embodiment 2, and can also adopt the inner core counting method of the cable of embodiment 3. Please refer to Figure 8 In addition to the intelligent detection method of the cable intelligent detector, the following steps can be included.

[0125] Step S315, the cable intelligent detector can be networked through a 4G module, linked to the background, and remotely upgraded. Step S315 can ensure that the version of the cable intelligent detector is updated in time.

[0126] Step S316, the pictures that cannot be recognized by the cable intelligent detector can be returned through the 4G network, and the background can update the detector database by training. Step S316 can gradually improve the database and improve the recognition rate of the cable intelligent detector.

[0127] In the present embodiment, the detection box of the cable intelligent detector can also be designed as an integrated detector, which can reasonably utilize the space of the detection box, reduce the overall volume of the detector, and reduce the weight of the detector.

[0128] From the above four embodiments, it can be seen that the cable intelligent detector of the application can intelligently identify the cable type, making the detection more convenient and accurate; the calculation functions of the shielding layer, the core area (i.e. the wire core area), and the number of cores (i.e. the number of inner cores) are added, these parameters are important detection indexes in cable quality detection, so that the cable intelligent detector has more complete functions and better performance. The 4G communication function is added, so that the cable intelligent detector can be online at any time, update the latest detection software in time, process the cable that cannot be identified in time, and improve the product detection efficiency and applicability. The lightweight design of the cable intelligent detector reduces the weight of the equipment, and the equipment can be carried to the scene for on-site detection.

[0129] The advantages of the application are:

[0130] (1) The application provides an automatic identification method of cable type: judging whether the cable is a power cable or an overhead cable according to the different layered types of power cables and overhead cables; then identifying the cross-sectional area of the cable, and quickly identifying the cable model according to the area comparison with the cable area of the cable library.

[0131] (2) The application provides a parameter detection method of the cable, specifically a calculation method of the thickness of the insulation layer of the cable, a calculation method of the thickness of the insulation shielding layer of the cable, and a calculation method of the thickness of the conductor shielding layer of the cable, which accurately distinguishes the edges of the insulation layer and the shielding layer, and accurately calculates and measures the thickness of the insulation layer and the shielding layer.

[0132] (3) The application provides a calculation method of the area of the wire core, which processes the wire core imaging area as an ellipse, and corresponds to the major and minor axes of the ellipse for the outer frame of the wire core area, calculates the area of the wire core according to the proportional relationship between the pixels and the actual size.

[0133] (4) The application provides a method for counting the inner cores of the cable, which distinguishes the edges of the inner cores, and counts the number of wire cores according to the area.

[0134] (5) The application updates the intelligent detection method of the cable intelligent detector in real time: the equipment can be linked to the background through the 4G module, and the machine can be remotely upgraded to ensure that the equipment version is the latest.

[0135] (6) The application updates the database of the cable type or the cable model that cannot be identified by the cable intelligent detector in time, returns the unidentifiable pictures through the 4G network, updates the detector database through training in the background, gradually improves the database, and improves the identification rate of the detector.

[0136] (7) The application uses a detection box as a box body to design an integrated detector, reasonably utilizes the space of the detection box, reduces the overall volume of the detector, and reduces the weight of the detector.

[0137] Therefore, the cable intelligent detector has the following functions: (1) a function of automatically identifying the cable type; (2) a function of identifying the thickness of the cable shielding layer; (3) a function of calculating the cross section of the core (i.e. the wire core); (4) a function of calculating the number of the core wire core (i.e. the core inside the wire core); (5) a function of remotely upgrading the cable intelligent detector by the system (i.e. the background); (6) a function of manually identifying the unrecognized picture returned by the cable intelligent detector by the system and remotely updating the database of the cable intelligent detector.

[0138] The above-mentioned embodiments only express several embodiments of the present application, and the description is more specific and detailed, but it should not be understood as a limitation on the scope of the patent. It should be pointed out that for ordinary skilled in the art, without departing from the concept of the present application, a number of modifications and improvements can be made, which are all within the scope of protection of the present application. Therefore, the scope of protection of the present application patent should be subject to the appended claims.

Claims

1. A method of counting the inner core of a cable, characterized by, The inner core counting method comprises the following steps: Step S11, converting the image to be recognized into a luminance gray scale image, wherein the image to be recognized is a front view image obtained by imaging the cable port in the center area of the picture at a target focal length and a target imaging distance with a front view angle; Step S15, obtaining a rectangular region subgraph at the n*n pixel region position in the center of the luminance gray scale image, the value of n satisfying that the area of n*n is always within the cable imaging area, and the minimum gray scale value in the part of pixels with a gray scale value greater than N in the rectangular region subgraph being counted as another threshold value, so as to perform binaryzation processing on the luminance gray scale image; Step S16, finding the wire core area contour of the cable in the luminance gray scale image after binaryzation processing by using a contour finding function, and obtaining the corresponding wire core area frame, that is, the wire core imaging area; Step S38, taking the width and height of the wire core imaging area as the major axis and the minor axis of an ellipse respectively, and unfolding the formed ellipse into an equivalent rectangle II; Step S39, reducing the equivalent rectangle II to 1 column to the left in a gray scale average manner, counting the minimum gray scale value I in the equivalent rectangle II, and obtaining the horizontal segmentation line of each layer of wire core in the horizontal direction; Step S310, dividing the equivalent rectangle II into subgraphs of each layer by taking the horizontal segmentation line as a boundary, reducing each layer to 1 row downward in a gray scale average manner in the vertical direction, and counting the minimum gray scale value II; Step S311, counting the number of pixels with a gray scale value II less than a target threshold value as the gap between the inner cores of the wire core; Step S312, calculating the interval between the minimum gray scale values II, performing the same interval quantity statistics in a histogram manner, and taking the maximum quantity interval value as the width of the inner core; Step S313, calculating the number of inner cores of each layer according to the total width of the equivalent rectangle II minus the gap quantity of the inner core, and then divided by the width of the inner core; Step S314, multi-layerly collecting to obtain the total inner core count.

2. The cable core count method of claim 1, wherein, Before step S15, the following steps are further included: Step S12, performing noise reduction on the luminance gray scale image to remove noise points.

3. The cable core count method of claim 1, wherein, The value of N is 128.

4. The cable core count method of claim 1, wherein, The value of n is 100.

5. The cable core count method of claim 1, wherein, If the cable port is not necessarily in the picture center area, the inner core counting method comprises the following steps: Step S13, according to the picture position and picture diameter of the cable port in the reference image, circumscribing a corresponding picture area I on the luminance gray scale image, counting the minimum pixel gray scale value outside the picture area I as a threshold value, and performing binaryzation processing on the luminance gray scale image; wherein the reference image is a front view image obtained by imaging the cable port of the cable with the largest size specification at a target focal length and a target imaging distance with a front view angle; Step S14, finding the overall contour of the cable port in the image to be recognized after binaryzation processing by using a contour finding function, and obtaining the corresponding overall area frame, that is, the cable imaging area; And in step S15, a rectangular region subgraph is obtained at the n*n pixel region position in the center of the cable imaging area.

6. The method of claim 5, wherein, In step S13, the minimum value of the pixel gray scale outside the picture area and located at one corner of the gray scale image is counted as the threshold value.

7. A cable intelligence detector, characterized by, The method comprises the steps of: adopting the cable core counting method according to any one of claims 1 to 6.

8. A detection method of a cable intelligent detector, characterized in that, The method comprises the steps of: adopting the cable core counting method according to any one of claims 1 to 6.

9. The method of claim 8, wherein the step of detecting the cable comprises the steps of: detecting the cable by using the first sensor; and detecting the cable by using the second sensor. The detection method further comprises the following steps: Step S315, the cable intelligent detection instrument is networked through the 4G module, linked to the background, and remotely upgraded.

10. The method of claim 9, wherein the cable intelligent detector is a cable detector. The detection method further comprises the following steps: Step S316, the pictures that cannot be recognized by the cable intelligent detection instrument are returned through the 4G network, and the background updates the detection instrument database through training.

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