CN104515473A - Online diameter detection method of varnished wires - Google Patents

Abstract

The invention discloses an online detection method for the diameter of an enameled wire, which comprises the steps of: collecting a diameter image of the enameled wire; performing noise filtering processing on the diameter image; performing edge detection on the diameter image after the noise filtering processing to obtain an edge image; Calculate the diameter of the enameled wire from the edge image. Since the edge detection method combined with the iterative method for threshold segmentation of the image and the edge tracking method based on image scanning, the obtained edge image can well distinguish the background and the target, and the edge is continuous, there is no false edge, and there is no burr point generation, the edge width is a single pixel, and the edge details are better reserved, so that the present invention has higher detection accuracy; at the same time, compared with the traditional edge detection method, the edge detection method of the present invention is less time-consuming, making the present invention It has faster detection speed; in summary of the above two points, the present invention has strong theoretical value and engineering application promotion value.

Description

An online detection method for enameled wire diameter

technical field

The invention belongs to the field of control and instrumentation, and relates to an online detection method for the diameter of an enameled wire.

Background technique

Enameled wire is a kind of winding wire composed of two parts: conductor and insulating layer. This kind of winding wire is coated with one or more insulating materials on the surface of the conductor. The coating thickness of the insulating material of the enameled wire with good performance is proportional to the diameter of the inner layer conductor, and the coating of the insulating layer is uniform and continuous. Too thin or too thick insulating coating will affect the performance of enameled wire. Therefore, the uniformity of the insulating coating and the uniformity of the wire diameter are one of the important factors that determine the insulation performance of the enameled wire. However, in the production process of enameled wires, for some reason, the enameled wire enameled film coating may be uneven, that is, film defects, thereby affecting the overall insulation performance of enameled wires. Therefore, in the enameled wire production process, it is of great engineering significance to be able to effectively measure the thickness of the enameled wire paint film coating or the diameter of the enameled wire online.

In the past 20 years, the output of enameled wire in my country has grown at an annual rate of more than 10%. It is estimated that by 2015, my country's enameled wire production will reach 1.6 million tons, becoming a major producer of enameled wire. However, in my country's current enameled wire production, there are mostly low-level repetitive production phenomena, "high output, low quality", and the low insulation performance of enameled wires leads to low quality of enameled wires. With the continuous development and improvement of manufacturing and processing technology, higher requirements are placed on measurement accuracy and speed. Common measurement methods, such as: micrometer measurement, laser measurement, CCD imaging methods, etc. can no longer meet the current needs. Moreover, some of these methods are cumbersome and inefficient. In this case, it is very important to measure the diameter of enameled wire quickly, accurately, non-contact and online. Some scholars have proposed a contact-type online detection of paint film, which is easy to damage the surface paint film and affect the insulation performance. Some researchers have designed an enameled wire computer online monitoring system, which uses eddy current devices to detect defect signals.

At present, there are few domestic systems dedicated to measuring the diameter of enameled wires, and most of the enameled wire diameter measurement stays in the stage of manual offline operation. Manual measurement brings great constraints. The main performance is: On the one hand, manual measurement depends on the measurement results Observations are susceptible to subjective factors. And the long-time reading work will inevitably make people tired, reduce the accuracy of readings, and will cause large errors in the measurement results, which will affect the measurement results; on the other hand, the efficiency of manual measurement is limited. quantity, resulting in an increase in cost. At the same time, manual measurement is easy to cause a certain degree of damage to the paint film of the enameled wire, which affects the performance of the enameled wire.

Under the requirements of the above technical background conditions, in order to improve the quality of enameled wire products, improve the production efficiency of enameled wires, and reduce labor costs, we have prompted us to consider using computer digital image processing technology to automatically and quickly measure the diameter of enameled wires on the production line. Provide accurate , useful information.

Contents of the invention

In order to solve the above problems, the present invention provides an online detection method of enameled wire diameter.

Technical scheme of the present invention:

An online detection method for the diameter of an enameled wire, comprising the following steps:

S1, collecting the diameter image of the enameled wire;

S2. Perform noise filtering processing on the diameter image, and use a median filter algorithm to filter noise of the diameter image, further comprising the following steps:

S21. Select a template, and use a pixel traversal method to sequentially overlap the current pixel with the center position of the template for pixels at different positions in the diameter image. Preferably, a template of a 3*3 pixel matrix or a 5*5 pixel matrix is used;

S22. Read out the gray values of all pixels at the positions covered by the template;

S23. Find the median of these gray values;

S24. Assign the obtained median value to the pixel point currently corresponding to the center of the template as the gray value of the point;

S3, performing edge detection on the diameter image after the noise processing in step S2, including the following steps:

S31. Using an iterative method to perform threshold segmentation on the image, further comprising the following steps:

S311. Select an initial threshold T,

$\mathrm{<span\; class="notranslate">\; T</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; )</span>$

In the formula, f _min is the minimum gray value in the image pixel, and f _max is the maximum gray value in the image pixel;

S312. Segment the image by applying an initial threshold T, and divide the image into two parts according to the gray value of the image pixel, an area with a gray value greater than T and an area with a gray value smaller than T;

S313. Calculate the gray mean values u ₁ and u ₂ of the pixels included in the region with gray value greater than T and the region with gray value smaller than T, respectively;

S314. Calculate a new threshold T,

$\mathrm{<span\; class="notranslate">\; T</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$

S315. Steps S312, S313, and S314 are repeated until the difference between the T values obtained by two consecutive calculations is less than or equal to 1;

S32. Perform edge tracking on the image subjected to threshold segmentation in step S31 to obtain an edge image, further comprising the following steps:

S321. First scan the image in order from left to right, find the first pixel with a gray value of 1, and mark it as A ₀ (i, j), where i and j are the abscissas of the pixel A ₀ and the ordinate;

S322. Search the 3*3 neighborhood of the pixel point A ₀ (i, j) in a clockwise direction, and set the first searched pixel point with the same gray value as the pixel point as a new boundary point A _n ;

S323. If the grayscale value of A _n is equal to the grayscale value of the second boundary point _A1 , and the grayscale value of the previous boundary point An _-1 is equal to the grayscale value of the first boundary point _A0 , stop Search, otherwise repeat step S322 to continue searching;

S4. Calculating the diameter of the enameled wire according to the diameter image obtained in step S3, further comprising the following steps:

S41. Determine the actual distance L ₀ corresponding to one pixel in the diameter image through camera calibration;

S42. Calculate the number n of pixels occupied by the diameter of the enameled wire in the image,

n= _{x1－ x2}

In the formula, x ₁ and x ₂ are respectively the abscissas corresponding to two boundary points with different abscissas in the image;

S43. Calculate the enameled wire diameter L=n*L ₀ .

The beneficial effect of adopting the technical scheme of the present invention: when the present invention carries out edge detection to figure, has adopted the edge detection method that iterative method is carried out threshold value segmentation to image and the edge tracking method based on image scanning combines, thus, the edge image that obtains is very The background and the target are well distinguished, and the edges are continuous, there are no false edges, no burr points are generated, the edge width is single pixel, and the edge details are better preserved, so that the present invention has higher detection accuracy; at the same time, compared with the traditional edge detection Compared with the method, the edge detection method of the present invention consumes less time, which makes the present invention have a faster detection speed; in summary of the above two points, the present invention has strong theoretical value and engineering application promotion value.

Description of drawings

Fig. 1 is a schematic flow sheet of the present invention;

Fig. 2 iterative method selects the threshold flow chart;

Fig. 3 is a schematic diagram of the coordinates of the pixels in the 3*3 neighborhood of the pixel A ₀ (i, j);

Fig. 4 is the edge image that adopts edge detection algorithm of the present invention to obtain;

Fig. 5 is the running time statistics of the edge detection algorithm of the present invention and the traditional edge detection algorithm; among the figure Sobel is the Sobel edge detection running time statistical curve, Robert is the Robert edge detection running time statistical curve, Canny is the Canny edge detection running time statistical curve, Our is the edge detection running time statistical curve of the present invention;

Figure 6 is a schematic diagram of the principle of CCD diameter measurement; in the figure, 1 is the lens, 2 is the linear array CCD sensor, f is the focal length of the lens, u is the object distance, v is the image distance, p is the pixel size of the linear array CCD, and L is the measured enameled wire Diameter, n is the number of pixels occupied by the image of the enameled wire passing through the imaging system on the linear CCD target surface.

Detailed ways

The present invention will be further described below in conjunction with the accompanying drawings of the description.

As shown in Figure 1, a kind of online detection method of enameled wire diameter comprises the following steps:

S1, collecting the diameter image of the enameled wire;

S2. Perform noise filtering processing on the diameter image;

S3. Performing edge detection on the diameter image processed by noise filtering in step S2 to obtain an edge image;

S4. Calculate the diameter of the enameled wire according to the edge image.

Wherein, when collecting the diameter image of the enameled wire in step S1, a linear array CCD industrial camera is used to collect the image, and the light source is selected as a high-brightness LED parallel light source; when collecting the diameter image of the enameled wire, environmental conditions and the quality of the sensing components themselves will cause image noise Due to the randomness and unpredictability of the noise, the probability density function is commonly used to approximate the noise in order to better resist the noise. It can be seen from the analysis that the image noise in this embodiment is mainly random noise and salt and pepper noise.

In step S2, the median filter algorithm is used to perform noise processing on the collected diameter image. The principle is to sort the pixels in the neighborhood of the image according to the gray level, and then select the middle gray value as the gray output and assign it to the current pixel point, including the following steps:

S21. Use the method of pixel traversal to sequentially make the current pixel coincide with the central position of the template for the pixels at different positions in the diameter image. Preferably, the template is a template of a 3*3 pixel matrix or a 5*5 pixel matrix;

S22. Read out the gray values of all pixels at the positions covered by the template;

S23. Find the median of these gray values;

S24. Assign the obtained median value to the current pixel point in the center of the template as the gray value of the point.

The effect of median filtering to eliminate noise is closely related to the size of the template and the number of pixels involved in the calculation in the template. In the process of median filtering, the template can only select a part of the pixels for calculation, so as to reduce the amount of calculation , to improve the calculation speed; from the principle of median filtering, we can see that median filtering has the function of smoothing images, and its main purpose is to eliminate isolated noise points; median filtering is a nonlinear filtering method, which can overcome the problems caused by linear filtering to a certain extent. For the image blur problem, it has a good filtering effect on random noise and salt and pepper noise, and can better preserve the edge information of the image.

When performing edge detection on the diameter image after the noise filtering process in step S3, an iterative method is used to perform threshold segmentation on the image and an edge detection method that combines an edge tracking method based on image scanning, namely:

First, S31. Using an iterative method to perform threshold segmentation on the diameter image, as shown in FIG. 2 , the threshold segmentation specifically includes the following steps:

S311. Select an initial threshold T,

$\mathrm{<span\; class="notranslate">\; T</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; min</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; f</span>}}_{\mathrm{<span\; class="notranslate">\; max</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span>$

In the formula, f _min is the minimum gray value in the image pixel, and f _max is the maximum gray value in the image pixel;

S312. Segment the image by applying an initial threshold T, and divide the image into two parts according to the gray value of the image pixel, an area with a gray value greater than T and an area with a gray value smaller than T;

S313. Calculate the gray mean values u ₁ and u ₂ of the pixels included in the region with gray value greater than T and the region with gray value smaller than T, respectively;

S314. Calculate a new threshold T,

$\mathrm{<span\; class="notranslate">\; T</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; +</span>{\mathrm{<span\; class="notranslate">\; u</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 2</span>}<span\; class="notranslate">\; )</span>$

S315. Steps S312, S313, and S314 are repeated until the difference between the T values obtained by two consecutive calculations is less than or equal to 1.

Then, S32, carry out edge tracking to the image after the threshold segmentation in step S31, edge tracking is to find out the coordinates of the edge point after the edge of the image is extracted, and mark on the image; the edge tracking method commonly used now is image Scanning method, by scanning the entire image to find out the point with a gray value of 1, and mark the corresponding coordinates; since this method needs to scan the entire image, its efficiency is relatively low, combined with the enameled wire diameter collected in this embodiment Image edge is the feature of two straight lines, the present invention improves on the basis of image scanning method, specifically comprises the following steps:

S321. First scan the image in order from left to right, find the first pixel with a gray value of 1, and mark it as A ₀ (i, j), where i and j are the abscissas of the pixel A ₀ and the ordinate;

S322. Search the 3*3 neighborhood of the pixel point A ₀ (i, j) in a clockwise direction, the coordinates of the pixels in the 3*3 neighborhood of A ₀ (i, j) are shown in FIG. 3 , and the searched The first pixel point with the same gray value as the pixel point is defined as the new boundary point A _n ;

S323. If the grayscale value of A _n is equal to the grayscale value of the second boundary point _A1 , and the grayscale value of the previous boundary point An _-1 is equal to the grayscale value of the first boundary point _A0 , stop Search, otherwise repeat step S322 to continue searching.

The edge image obtained by using the edge detection method in the embodiment of the present invention is shown in Figure 4. It can be seen from the figure that the obtained edge image can distinguish the background and the target very well, and the edge is continuous, there are no false edges, and there are no glitch points Produce, and edge width is single pixel, preferably has reserved edge detail; Traditional edge detection algorithm has Sobel edge detection, Robert edge detection and Canny edge detection, compared with edge detection algorithm of the present invention, edge detection algorithm of the present invention consumes It takes less time, as shown in Figure 5. Each of the above algorithms is tested 5 times and the average value is taken. It can be seen from the figure that the average time-consuming of Canny edge detection is about 11s, followed by Robert edge detection and Sobel edge detection. The average time consumption of the two is similar to about 3s, while the edge detection algorithm of the present invention takes only 0.33s, greatly reducing the running time. The enameled wire diameter can be calculated using the edge image.

The calculation of the diameter of the enameled wire in step S4 is based on the principle that uniformly scattered parallel light is used to irradiate the enameled wire from the back, and the enameled wire is imaged on the CCD industrial camera sensor through the optical system, and the imaging size is multiplied by a coefficient determined by the optical system, which is The actual size of the enameled wire, as shown in Figure 6,

According to the imaging formula:

$\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; +</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; v</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; 1</span>}}{\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 3</span>}<span\; class="notranslate">\; )</span>$

$\mathrm{<span\; class="notranslate">\; \&beta;</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; v</span>}}{\mathrm{<span\; class="notranslate">\; u</span>}}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; np</span>}}{\mathrm{<span\; class="notranslate">\; L</span>}}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 4</span>}<span\; class="notranslate">\; )</span>$

In the formula (3), f is the focal length of the lens, u is the object distance, v is the image distance, in the formula (4), β is the lens magnification, p is the pixel size of the linear array CCD, L is the diameter of the enameled wire to be measured, and n is the enameled wire passing through The number of pixels occupied by the image on the linear array CCD target surface of the imaging system can be calculated from the formula (3) and formula (4) to obtain the diameter L of the measured enameled wire:

$\mathrm{<span\; class="notranslate">\; L</span>}<span\; class="notranslate">\; =</span>\frac{\mathrm{<span\; class="notranslate">\; np</span>}}{\mathrm{<span\; class="notranslate">\; \&beta;</span>}}<span\; class="notranslate">\; =</span><span\; class="notranslate">\; (</span>\frac{\mathrm{<span\; class="notranslate">\; u</span>}}{\mathrm{<span\; class="notranslate">\; f</span>}}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; \&CenterDot;</span>\mathrm{<span\; class="notranslate">\; no</span>}<span\; class="notranslate">\; \&CenterDot;</span>\mathrm{<span\; class="notranslate">\; p</span>}<span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; -</span><span\; class="notranslate">\; (</span>\mathrm{<span\; class="notranslate">\; 5</span>}<span\; class="notranslate">\; )</span>$

It can be seen from formula (5) that since u, f, and p have been determined during the design of the measurement system, the diameter of the enameled wire actually becomes the number of pixels occupied by the image of the enameled wire passing through the imaging system on the linear CCD target surface, that is, n Size, and determine the two edges of the measured enameled wire to calculate n, so the problem of measuring the diameter of the CCD is transformed into the identification and counting of the enameled wire diameter image edge.

Specifically, calculating the diameter of the enameled wire includes the following steps:

S41. Determine the actual distance L ₀ corresponding to a pixel in the edge image through camera calibration:

The pixel size of the linear array CCD used in this embodiment is H4.7×V4.7um. In order to improve the measurement accuracy of the system, a sleeve that can be used to adjust the distance between the lens and the camera is added during the design of the system hardware. cylinder, according to the imaging formula gain And the clarity of the enameled wire images collected, it is determined that the object distance of the system is u = 128mm, and the image distance is v = 240mm;

After determining the distance between the camera and the lens (image distance) and the distance between the lens and the enameled wire (object distance), respectively use 3mm, 3.5mm, 5mm, 8mm high-precision calibration blocks as calibration reference objects, according to the actual size of the punctuation block and the image According to the number of corresponding pixels, the corresponding relationship between image pixels and distances is finally obtained, and the actual distance of an object corresponding to one pixel in the image of this system is obtained as L ₀ =0.0025mm.

S42. Calculate the number n of pixels occupied by the diameter of the enameled wire in the edge image,

n= _{x1－ x2}

In the formula, x ₁ and x ₂ are respectively the abscissa coordinates of the points on the two edges in the edge image.

S43. Calculate the enameled wire diameter L=n*L ₀ .

The present invention adopts an iterative method for image threshold segmentation and an edge detection method based on an image scanning edge tracking method, so the obtained edge image can distinguish the background and the target well, and the edge is continuous, without false edges, and without burrs point generation, the edge width is a single pixel, and the edge details are better reserved, so that the present invention has higher detection accuracy; at the same time, compared with the traditional edge detection method, the edge detection method of the present invention consumes less time, making the present invention It has faster detection speed; in summary of the above two points, the present invention has strong theoretical value and engineering application promotion value.

Those skilled in the art will appreciate that the embodiments described here are to help readers understand the principles of the present invention, and it should be understood that the protection scope of the present invention is not limited to such specific statements and embodiments. Those skilled in the art can make various other specific modifications and combinations based on the technical revelations disclosed in the present invention without departing from the essence of the present invention, and these modifications and combinations are still within the protection scope of the present invention.

Claims (8)

1. an online test method for enameled wire diameter, is characterized in that comprising the following steps:

The diameter image of S1, collection enameled wire;

S2, noise filtering process is carried out to this diameter image;

S3, rim detection is carried out to the diameter image in step S2 after noise filtering process, obtain edge image;

S4, calculate the diameter of enameled wire according to edge image.

2. method according to claim 1, is characterized in that: be the noise utilizing median filtering algorithm to carry out filtering diameter image in step S2.

3. method according to claim 2, is characterized in that, the noise utilizing median filtering algorithm to carry out filtering diameter image specifically comprises the following steps:

S21, choose template, by the pixel of diverse location in diameter image, adopt the method for pixel traversal to make current pixel point and template center position overlap successively;

S22, the gray-scale value of all pixels of template covering position to be read;

S23, find out the intermediate value of these gray-scale values;

S24, the intermediate value obtained is assigned to the pixel of the current correspondence of template center, as the gray-scale value of this point.

4. method according to claim 3, is characterized in that: in step S21, template adopts the template of 3*3 picture element matrix or 5*5 picture element matrix.

5. method according to claim 1, is characterized in that in step S3, rim detection comprises the following steps:

S31, employing process of iteration carry out Threshold segmentation to diameter image;

S32, carrying out Edge track to carrying out the image after Threshold segmentation in step S31, namely obtaining edge image.

6. method according to claim 5, is characterized in that step S31 specifically comprises the following steps:

S311, a selection initial threshold T,

$T=\frac{1}{2}(f_{\min }+f_{\max })$

F in formula _minfor the minimum gradation value in image slices vegetarian refreshments, f _maxfor the maximum gradation value in image slices vegetarian refreshments;

S312, application initial threshold T are to Image Segmentation Using, and according to the gray-scale value of image slices vegetarian refreshments, be two parts by Iamge Segmentation, gray-scale value is greater than the region of T and the region of the little T of gray-scale value;

The gray average u of the pixel that S313, the region calculating region and the little T of gray-scale value that gray-scale value is greater than T respectively comprise ₁and u ₂;

S314, calculate new threshold value T,

$T=\frac{1}{2}(u_1+u_2);$

S315, repetition step S312, S313, S314, the difference of the T value calculated until double is satisfied is less than or equal to 1.

7. method according to claim 5, is characterized in that in step S32, Edge track comprises the following steps:

S321, first by order scan image from left to right, finding first gray-scale value is the pixel of 1, and is labeled as A ₀(i, j), wherein i, j are respectively pixel A ₀horizontal ordinate and ordinate;

S322, in the direction of the clock search pixel point A ₀the 3*3 neighborhood of (i, j), is decided to be new frontier point An by search first pixel identical with this pixel gray-scale value;

If S323 is A _ngray-scale value equal second frontier point A ₁gray-scale value and previous frontier point A _n-1gray-scale value equal first frontier point A ₀gray-scale value, then stop search, otherwise repeat step S322 continue search.

8. method according to claim 1, is characterized in that the diameter calculating enameled wire in step S4 comprises the following steps:

S41, demarcate by shooting the actual range L determining in edge image corresponding to a pixel ₀;

Pixel number n shared by enameled wire diameter in S42, edge calculation image,

n＝x ₁－x ₂

X in formula ₁, x ₂the horizontal ordinate of the point in edge image on two edges respectively;

S43, calculating enameled wire diameter L=n*L ₀.