CN114897864A - Workpiece detection and defect judgment method based on digital and analog information - Google Patents

Abstract

The invention relates to a workpiece detection and defect judgment method based on digital-analog information, and belongs to the field of image processing. The method comprises the following steps: acquiring complete workpiece image information by using a visual sensor; acquiring a gray scale image of a qualified workpiece, and counting the gray scale value of the qualified workpiece; acquiring the corresponding situation of the workpiece position on the image on a digital-analog coordinate system; designing a corresponding image position frame; carrying out edge detection; detecting and acquiring an image edge; determining the deviation range of the movement of the workpiece; obtaining a normal characteristic image and a defect characteristic image with the same size; confirming whether the workpiece meets digital-analog form information or not, and determining whether the workpiece is a workpiece to be detected of the template or not; judging whether a workpiece exists or not, and whether the size and the shape of the workpiece meet the preset design requirements or not; counting the gray values of all pixel points in the selected area, and calculating the gray average value and the standard deviation value of the selected area; and judging whether the workpiece is qualified or not, and outputting the result obtained by comparison. The invention realizes the auxiliary detection of the actual workpiece image.

Description

Workpiece detection and defect judgment method based on digital and analog information

technical field

The invention belongs to the field of image processing, and relates to a workpiece detection and defect judgment method based on digital-analog information.

Background technique

With the advancement of computer science and the upgrading and development of industrial manufacturing, computer vision is widely used in the industrial field, and industrial design software is gradually improving its functions. Before industrial manufacturing, industrial design software such as AutoCAD and CATIA are generally used to design the shape of the workpiece, and mark some feature points on it. Industrial design is the modeling design of industrial products in order to better reflect the product characteristics. Designers can use design software to complete product design work according to actual needs. Digital and analog information is mainly used for the design and manufacture of workpieces. This is beneficial to provide assistance in the relevant inspection of the workpiece after it is manufactured. But few studies have combined digital-analog information with subsequent image processing to provide more additional information. Using digital and analog information to assist detection is a new development direction of coordinate measurement technology.

SUMMARY OF THE INVENTION

In view of this, the purpose of the present invention is to provide a workpiece detection and defect judgment method based on digital-analog information.

To achieve the above object, the present invention provides the following technical solutions:

A workpiece detection and defect judgment method based on digital and analog information, the method includes the following steps:

S1: Use the vision sensor to obtain complete workpiece image information;

S2: Extract the required digital-analog information, save the number of workpieces, coordinates and shape information in the corresponding file, obtain the grayscale map of the qualified workpiece, and count its grayscale values;

S3: Establish a corresponding coordinate system for the image, align the digital-analog coordinate information with the image coordinate information, and obtain the correspondence of the workpiece position on the digital-analog coordinate system on the image;

S4: According to the image coordinate information after alignment, select and design the corresponding image position frame;

S5: correspond the search position to the image target position frame, and perform edge detection on the workpiece on it;

S6: Use edge detection to obtain the target workpiece, and detect and obtain the edge of the image by using the edge position of the workpiece object and the gray-scale step change in other positions;

S7: Determine the deviation range of the workpiece movement according to the errors that may occur in actual manufacturing;

S8 : extracting regions of interest for the normal-shaped object-to-be-tested image and the defective-to-be-tested object image, respectively, to obtain a normal feature image and a defect feature image with the same size;

S9: Match and compare the obtained workpiece with the template, confirm whether the workpiece satisfies the digital-analog shape information, and determine whether it is the workpiece to be detected by the template;

S10: Compare the obtained edge condition of the workpiece with the digital-analog information to determine whether there is a workpiece and whether the size and shape of the workpiece meet the pre-design requirements;

S11: Obtain a grayscale image of the workpiece image, establish a selection area in the grayscale image according to a preset size, count the grayscale values of all pixels in the selection area, and calculate the grayscale mean value and standard deviation value of the selection area;

S12: judging whether the gray value of the workpiece image conforms to the standard image, thereby judging whether the workpiece is a qualified workpiece, synthesizing the result of template matching, and outputting the result obtained by comparison;

If it is partially judged that the range of pixels containing gray values exceeds the range of pixels containing gray values of the standard workpiece, it is considered that the shape of the workpiece is too large;

If it is partially judged that the pixel points containing the gray value are less than the pixel range of the standard workpiece containing the gray value, it is considered that the workpiece is morphologically defective;

If it is judged that the gray value of the pixels on the entire surface of the workpiece is unbalanced, it is considered that the surface of the workpiece is not smooth; if the gray value of some pixels is judged to be less than the standard gray value, it is considered that the workpiece is concave;

If it is judged that the gray value of some pixel points is greater than the standard gray value, it is considered that the workpiece is convex;

If it is judged that there are various morphological problems in the image to be inspected, the defects that are more different from the standard workpiece will be listed first.

Optionally, the workpiece detection and defect judgment methods are specifically:

Read the shape information of the number of workpieces n, position [X _W Y _W Z _W ] and size s ₁ from the digital-analog file. The relevant size information in the digital-analog design is consistent with the actual information, and the origin is designed to be any arbitrary value on the digital-analog image. One point, regard the digital-analog coordinate system as the world coordinate system;

The pixel coordinate system u-v and the digital-analog coordinate system x-y on the image are respectively established, and the two coordinate systems are converted and aligned to obtain the specific coordinate position of the corresponding position of the digital-analog coordinate point on the image;

The process of converting the world coordinate system to the pixel coordinate system is the process of converting world coordinates to camera coordinates, camera coordinates to image coordinates, and image coordinates to pixel coordinates; it is the mutual conversion between the four coordinate systems;

where world coordinates are converted to camera coordinates:

Convert camera coordinates to image coordinates:

Convert image coordinates to pixel coordinates:

The process of directly converting world coordinates to pixel coordinates is:

相机外参为

Wherein the world coordinates are [X _W Y _W Z _W ], the camera coordinates are [X _C Y _C Z _C ], the image coordinates: [xy], the pixel coordinates: [uv], the camera focal length is f, the rotation matrix is R, the translation The matrix is T, and the camera internal parameters are

Camera external parameters are

Put the workpiece position coordinates [uv] and size s ₂ information on the image, and mark the pre-selection box on the image;

Perform image edge detection on the pre-selected frame of the image to obtain the corresponding workpiece information;

The edge detection is performed by the Canny edge detection algorithm, including graying the image; smoothing the image with a Gaussian filter; calculating the magnitude and direction of the gradient with the finite difference of the first-order partial derivative; non-maximizing the magnitude of the gradient Value suppression; detect and connect edges with a dual threshold algorithm;

The gray value after Gaussian filtering becomes:

The gradient strength and direction of each pixel are:

Multiply each pixel and its neighborhood by a Gaussian matrix, and take its weighted average as the final gray value;

Filter non-maximum values, use a rule to filter points that are not edges, make the width of the edge 1 pixel, and form an edge line;

The obtained edge conditions are compared with the digital and analog information to determine whether there is a workpiece and whether the size and shape of the workpiece meet the pre-design requirements;

According to the previously detected gray value, an upper threshold and a lower threshold in the image are obtained. Those greater than the upper threshold are detected as edges, and those lower than the lower threshold are detected as non-edges; for the middle If it is adjacent to the pixel point determined as an edge, it is determined to be an edge; otherwise, it is a non-edge;

Using the template matching method, the template image is compared with the image to be tested or the image of a certain area, and the process of judging whether it is the same or similar;

In template matching, the template is a known small image, and template matching is to search for a target in a large image. It is known that there is a target to be found in the image, and the target has the same scale and direction as the template. and image elements, find the target in the image through a certain algorithm;

Template matching using normalized correlation coefficient matching method

The standard workpiece or digital-analog image is used as a template image to match and compare with the image to be detected. For the error caused by the shooting angle or other reasons, it can be improved by adjusting the matching threshold to determine whether the color of the workpiece meets the pre-design requirements;

Use the H, S, V values of the workpiece to detect the workpiece, and obtain the center coordinate position of the target workpiece;

Compare the workpiece center coordinates obtained by edge detection with the center coordinates obtained by color detection, and correct the workpiece position coordinates on the actual image;

The region of interest is extracted for the normal shape of the test object image and the defective test object image respectively, and the normal feature image and the defect feature image with the same size are obtained;

Match and compare the obtained workpiece with the template, confirm whether the workpiece satisfies the digital-analog shape information, and determine whether it is the workpiece to be detected by the template;

Match and compare the obtained workpiece with the template to determine whether there is a workpiece and whether the size and shape of the workpiece meet the pre-design requirements; and then use the image gray value detection method to detect whether the shape of the workpiece is qualified;

Create a selection area in the grayscale image according to the preset size, count the grayscale values of all pixels in the selection area, and calculate the grayscale mean and standard deviation of the selection area;

Judging whether the gray value of the workpiece image conforms to the standard image, so as to determine whether the workpiece is a qualified workpiece, synthesizing the results of template matching, and outputting the results obtained by comparison.

Optionally, the defects include whether there is a conforming workpiece, whether the shape of the workpiece conforms to the actual situation, whether the position of the workpiece conforms to the actual situation, whether the color of the workpiece conforms to the actual situation, whether the size conforms to the standard, whether there is unevenness, and whether the surface is smooth.

The beneficial effect of the present invention is that the ideal information in the digital model is used to assist in the detection of the actual workpiece image.

Other advantages, objects, and features of the present invention will be set forth in the description that follows, and will be apparent to those skilled in the art based on a study of the following, to the extent that is taught in the practice of the present invention. The objectives and other advantages of the present invention may be realized and attained by the following description.

Description of drawings

In order to make the objectives, technical solutions and advantages of the present invention clearer, the present invention will be preferably described in detail below with reference to the accompanying drawings, wherein:

1 is a flowchart of an image detection processing method based on digital-analog information;

Fig. 2 is the flow chart of the image method based on digital-analog color;

Figure 3 is a flow chart of workpiece qualification judgment.

Detailed ways

The embodiments of the present invention are described below through specific specific examples, and those skilled in the art can easily understand other advantages and effects of the present invention from the contents disclosed in this specification. The present invention can also be implemented or applied through other different specific embodiments, and various details in this specification can also be modified or changed based on different viewpoints and applications without departing from the spirit of the present invention. It should be noted that the drawings provided in the following embodiments are only used to illustrate the basic idea of the present invention in a schematic manner, and the following embodiments and features in the embodiments can be combined with each other without conflict.

Among them, the accompanying drawings are only used for exemplary description, and represent only schematic diagrams, not physical drawings, and should not be construed as limitations of the present invention; in order to better illustrate the embodiments of the present invention, some parts of the accompanying drawings will be omitted, The enlargement or reduction does not represent the size of the actual product; it is understandable to those skilled in the art that some well-known structures and their descriptions in the accompanying drawings may be omitted.

The same or similar numbers in the drawings of the embodiments of the present invention correspond to the same or similar components; in the description of the present invention, it should be understood that if there are terms “upper”, “lower”, “left” and “right” , "front", "rear" and other indicated orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings, and are only for the convenience of describing the present invention and simplifying the description, rather than indicating or implying that the indicated device or element must be It has a specific orientation, is constructed and operated in a specific orientation, so the terms describing the positional relationship in the accompanying drawings are only used for exemplary illustration, and should not be construed as a limitation of the present invention. situation to understand the specific meaning of the above terms.

Please refer to Figure 1 to Figure 3. At present, the image detection of objects either relies on template matching or training of a large number of data sets using deep learning, while the model-dependent matching is mainly based on morphology, and the detection of deep learning is mainly based on training images. Image features are detected, and few detection methods use the coordinate information in the digital model of the workpiece before design into the detection. The digital and analog information includes the position information and workpiece shape information of each work on the background plate. Input these precondition information as an input quantity together with the image to be tested, and then use various image processing methods to detect the image to be tested according to the edge information of the workpiece, so as to obtain the detection result more quickly and accurately.

The invention refers to the AOI detection method in the SMT of industrial components, and uses the optical detection method to check whether various components exist in the corresponding positions through the reflection of light, and whether the mounting is correct, whether the welding is good, and whether there are various bad states.

The invention uses the method of model matching to match the acquired image with the digital-analog image, so as to complete the image detection of the workpiece.

The present invention comprises an imaging system which may consist of one or more cameras to provide the possibility of better images. The camera should also be able to move under software control for a sharper, more complete picture.

The method includes the following steps:

Use vision sensors to obtain complete workpiece image information;

Read morphological information such as the number of workpieces n, position [X _W Y _W Z _W ] and size s ₁ from the digital-analog file. Since the relevant size information in the digital-analog design is consistent with the actual information, the origin may be designed as a digital-analog image. on any point, so the digital-analog coordinate system can be understood as the world coordinate system;

Establish the pixel coordinate system u-v and the digital-analog (world) coordinate system x-y on the image respectively, and convert and align the two coordinate systems to obtain the specific coordinate position of the corresponding position of the digital-analog coordinate point on the image;

The process of converting the world coordinate system to the pixel coordinate system is the process of converting world coordinates to camera coordinates, camera coordinates to image coordinates, and image coordinates to pixel coordinates. It is the mutual conversion between the four coordinate systems.

where world coordinates are converted to camera coordinates

Convert camera coordinates to image coordinates

Convert image coordinates to pixel coordinates

The process of directly converting world coordinates to pixel coordinates is

相机外参为

Wherein the world coordinates are [X _W Y _W Z _W ], the camera coordinates are [X _C Y _C Z _C ], the image coordinates: [xy], the pixel coordinates: [uv], the camera focal length is f, the rotation matrix is R, the translation The matrix is T, and the camera internal parameters are

Camera external parameters are

The workpiece position coordinates [uv], size s ₂ and other information on the image, and mark the pre-selection box on the image;

Perform image edge detection on the pre-selected frame of the image to obtain the corresponding workpiece information;

The edge detection is composed of multiple steps of the Canny edge detection algorithm, including graying the image; smoothing the image with a Gaussian filter; using the finite difference of the first-order partial derivative to calculate the magnitude and direction of the gradient; Non-maximum suppression; detects and connects edges with a two-threshold algorithm.

The gray value after Gaussian filtering will become:

The gradient strength and direction of each pixel point are

It can be understood as multiplying each pixel and its neighborhood by a Gaussian matrix, and taking its weighted average as the final gray value.

Filter non-maximum values, use a rule to filter points that are not edges, make the width of the edge as 1 pixel as possible, and form an edge line.

The obtained edge cases are compared with the digital and analog information to determine whether there is a workpiece and whether the size and shape of the workpiece meet the pre-design requirements.

According to the previously detected gray value, an upper threshold value and a lower threshold value in the image are obtained. Anything greater than the upper threshold value is detected as an edge, and anything lower than the lower threshold value is detected as a non-edge. For a pixel in the middle, if it is adjacent to a pixel determined to be an edge, it is determined to be an edge; otherwise, it is a non-edge. This makes it possible to improve accuracy.

Using the template matching method, the template image is compared with the image to be tested or the image of a certain area, and it is a process of judging whether they are the same or similar.

In template matching, the template is a known small image, and template matching is to search for a target in a large image. It is known that there is a target to be found in the image, and the target has the same scale and direction as the template. and image elements, the target can be found in the image through a certain algorithm.

Template matching using normalized correlation coefficient matching method

The standard workpiece or digital-analog image is used as a template image to match and compare with the image to be detected. For the error caused by the shooting angle or other reasons, it can be improved by adjusting the matching threshold to determine whether the color of the workpiece meets the pre-design requirements.

Use the H, S, V values of the workpiece to detect the workpiece, and obtain the center coordinate position of the target workpiece.

Compare the workpiece center coordinates obtained by edge detection with the center coordinates obtained by color detection, and correct the workpiece position coordinates on the actual image.

The region of interest is extracted for the normal shape of the test object image and the defective test object image, respectively, to obtain the normal feature image and the defect feature image with the same size.

Match and compare the obtained workpiece with the template, confirm whether the workpiece satisfies the digital-analog shape information, and determine whether it is the workpiece to be detected by the template;

Match and compare the obtained workpiece with the template to determine whether there is a workpiece and whether the size and shape of the workpiece meet the pre-design requirements; and then use the image gray value detection method to detect whether the shape of the workpiece is qualified.

Create a selection area in the grayscale image according to the preset size, count the grayscale values of all pixels in the selection area, and calculate the grayscale mean and standard deviation of the selection area;

Judging whether the gray value of the workpiece image conforms to the standard image, so as to determine whether the workpiece is a qualified workpiece, synthesizing the results of template matching, and outputting the results obtained by comparison.

Among them, if the range of pixels that are partially judged to contain gray values exceeds the range of pixels that contain gray values of the standard workpiece, it is considered that the shape of the workpiece is too large. If it is judged that the gray value of the pixels on the entire surface of the workpiece is unbalanced, it is considered that the surface of the workpiece is not smooth. If the gray value of some pixels is judged to be less than the standard gray value, it is considered that the workpiece exists. In the case of concave, if it is judged that the gray value of some pixel points is greater than the standard gray value, it is considered that the workpiece is convex.

If it is judged that there are various morphological problems in the image to be inspected, the defects that are more different from the standard workpiece will be listed first.

The detection method can detect workpieces with corresponding digital-analog information and obvious color contrast, and determine the deviation from the ideal digital-analog information, detect corresponding defects on the workpiece, and can detect without interrupting the production process. . Obtaining the inspection information of the workpiece in production can achieve better process control. Early detection of defects will avoid the assembly and subassembly of the workpiece in the later stage, and the workpiece can be repaired in advance.

The defects here mainly include whether there is a conforming workpiece, whether the shape of the workpiece is consistent with the actual, whether the position of the workpiece is consistent with the actual, whether the color of the workpiece is consistent with the actual, whether the size is consistent with the standard, whether there is unevenness, whether the surface is smooth, etc.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be Modifications or equivalent replacements, without departing from the spirit and scope of the technical solution, should all be included in the scope of the claims of the present invention.

Claims (3)

1. The workpiece detection and defect judgment method based on the digital-analog information is characterized by comprising the following steps of: the method comprises the following steps:

s1: acquiring complete workpiece image information by using a visual sensor;

s2: extracting required digital-analog information, storing the number, coordinates and form information of the workpieces in corresponding files, acquiring a gray-scale image of the qualified workpiece, and counting the gray-scale value of the qualified workpiece;

s3: establishing a corresponding coordinate system aiming at the image, aligning the digital-analog coordinate information with the image coordinate information, and acquiring the corresponding condition of the workpiece position on the image on the digital-analog coordinate system;

s4: selecting and designing a corresponding image position frame according to the aligned image coordinate information;

s5: corresponding the search position to an image target position frame, and carrying out edge detection on the workpiece on the image target position frame;

s6: acquiring a target workpiece by utilizing edge detection, and detecting and acquiring an image edge by utilizing the gray level step change of the edge position and other positions of a workpiece object;

s7: determining the deviation range of the workpiece movement according to errors possibly generated by actual manufacturing;

s8: respectively extracting interested areas of the normal form image of the object to be detected and the defective image of the object to be detected to obtain a normal characteristic image and a defective characteristic image with the same size;

s9: matching and comparing the obtained workpiece with the template, confirming whether the workpiece meets digital-analog form information, and determining whether the workpiece is a workpiece to be detected of the template;

s10: comparing the obtained edge condition of the workpiece with the digital-analog information, and judging whether the workpiece and the size and the shape of the workpiece meet the preset design requirements or not;

s11: obtaining a gray level image of the workpiece image, establishing a selection area in the gray level image according to a preset size, counting gray levels of all pixel points in the selection area, and calculating a gray level mean value and a standard deviation value of the selection area;

s12: judging whether the gray value of the workpiece image meets the standard image or not so as to judge whether the workpiece is a qualified workpiece or not, integrating the template matching result, and outputting the result obtained by comparison;

if the pixel point range containing the gray value exceeds the pixel point range containing the gray value of the standard workpiece, the workpiece form is considered to be overlarge;

if the part judges that the pixel point containing the gray value is less than the pixel point range of the standard workpiece containing the gray value, the shape of the workpiece is determined to be defective;

if the gray values of the pixels on the surface of the whole workpiece are judged to be unbalanced, the surface of the workpiece is considered to be unsmooth, and if the gray values of the pixels on the judged part are smaller than the standard gray value, the workpiece is considered to have a concave condition;

if the gray value of the partial pixel points is judged to be greater than the standard gray value, the workpiece is considered to have a convex condition;

if the image to be detected has various morphological problems, the defect with larger difference with the standard workpiece is preferentially listed.

2. The method according to claim 1, wherein the method comprises: the workpiece detection and defect judgment method specifically comprises the following steps:

reading the number n and the position [ X ] of the workpieces from the digital-analog file _W Y _W Z _W ]And size s ₁ The relevant dimension information in the digital-analog design conforms to the actual information, the origin is designed to be any point on the digital-analog image, and the digital-analog coordinate system is regarded as a world coordinate system;

respectively establishing a pixel coordinate system u-v and a digital-analog coordinate system x-y on the image, and converting and aligning the two coordinate systems to obtain a specific coordinate position point of the corresponding position of the digital-analog coordinate point on the image;

the process of converting the world coordinate system into the pixel coordinate system is a process of converting the world coordinate into a camera coordinate, converting the camera coordinate into an image coordinate, and converting the image coordinate into a pixel coordinate; is the interconversion between four coordinate systems;

wherein world coordinates are converted to camera coordinates:

camera coordinates are converted to image coordinates:

image coordinates are converted to pixel coordinates:

the process of directly converting world coordinates into pixel coordinates is as follows:

wherein the world coordinate is [ X ] _W Y _W Z _W ]And the camera coordinate is [ X ] _C Y _C Z _C ]And image coordinates: [ x y]Pixel coordinates: [ u v]The focal length of the camera is f, the rotation matrix is R, the translation matrix is T, and the internal parameters of the camera are

The external parameter of the camera is

Coordinates of the position of the workpiece on the image [ u v]And size s ₂ Information, and marking a preselection frame on the image;

detecting the edge of an image aiming at a preselected frame of the image to acquire and judge corresponding workpiece information;

the edge detection is carried out by using a Canny edge detection algorithm, and comprises the step of carrying out graying on an image; smoothing the image with a gaussian filter; calculating the magnitude and direction of the gradient by using the finite difference of the first-order partial derivatives; carrying out non-maximum suppression on the gradient amplitude; detecting and connecting edges by using a dual-threshold algorithm;

wherein the gray value after gaussian filtering becomes:

the gradient strength and direction of each pixel point are as follows:

multiplying each pixel point and the neighborhood thereof by a Gaussian matrix, and taking the average value with the weight as the final gray value;

filtering non-maximum values, and filtering points which are not edges by using a rule to enable the width of the edges to be 1 pixel point to form edge lines;

comparing the obtained edge condition with the numerical model information, and judging whether the sizes and the shapes of the workpieces meet the preset design requirements or not;

according to the detected gray value, an upper threshold value and a lower threshold value in the image are obtained, wherein the upper threshold value and the lower threshold value are detected as edges, and the lower threshold value and the upper threshold value are detected as non-edges; for the middle pixel point, if the middle pixel point is adjacent to the pixel point determined as the edge, the edge is determined; otherwise, the edge is not;

comparing the template image with the image to be detected or the image of a certain area by using a template matching method, and judging whether the images are the same or similar;

in the template matching, the template is a known small image, the template matching is to search a target in a large image, the target to be searched is known to exist in the image, the target and the template have the same size, direction and image elements, and the target is found in the image through a certain algorithm;

template matching using normalized correlation coefficient matching method

The standard workpiece or the digital-analog image is used as a template image to be matched and compared with an image to be detected, errors caused by shooting angles or other reasons are improved by adjusting a matching threshold value, and whether the color of the workpiece meets the preset design requirement or not is judged;

detecting the workpiece by using H, S and V values of the workpiece to obtain a central coordinate position of the target workpiece;

comparing the center coordinates of the workpiece obtained by edge detection with the center coordinates obtained by color detection, and correcting to obtain the position coordinates of the workpiece on the actual image;

respectively extracting interested areas of the normal-form image of the object to be detected and the image of the object to be detected with defects to obtain a normal characteristic image and a defect characteristic image with the same size;

matching and comparing the obtained workpiece with the template, confirming whether the workpiece meets digital-analog form information, and determining whether the workpiece is a workpiece to be detected of the template;

matching and comparing the obtained workpiece with the template, and judging whether the workpiece exists or not and whether the size and the shape of the workpiece meet the preset design requirements or not; then, detecting whether the shape of the workpiece is qualified or not by using an image gray value detection method;

establishing a selection area in the gray-scale image according to a preset size, counting gray-scale values of all pixel points in the selection area, and calculating a gray-scale mean value and a standard deviation value of the selection area;

and judging whether the gray value of the workpiece image meets the standard image or not so as to judge whether the workpiece is a qualified workpiece or not, integrating the template matching result, and outputting the result obtained by comparison.

3. The method according to claim 2, wherein the method comprises: the defects comprise whether the workpiece conforms to the standard or not, whether the shape of the workpiece conforms to the reality or not, whether the position of the workpiece conforms to the reality or not, whether the color of the workpiece conforms to the reality or not, whether the size conforms to the standard or not, and whether the surface is smooth or not.

Images