CN112304954B - Part surface defect detection method based on line laser scanning and machine vision - Google Patents
Part surface defect detection method based on line laser scanning and machine vision Download PDFInfo
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Abstract
The invention discloses a part surface defect detection method based on line laser scanning and machine vision, which specifically comprises the following steps: step 1, calibrating a camera, obtaining internal and external parameters of the camera, and shooting an image without laser irradiation as a reference image; step 2, acquiring an image by using an image acquisition system; step 3, sequentially carrying out Gaussian filtering, image difference, gaussian smoothing, stripe center line extraction and coordinate conversion on the image to obtain three-dimensional point cloud data of the surface of the part to be detected; step 4, aiming at the standard parts with the surface without defects, executing the operations of the steps 1-3 to obtain three-dimensional point cloud data images of the standard parts with the surface without defects; and 5, performing difference between the data obtained in the step 3 and the data obtained in the step 4, taking the absolute value of the difference, comparing the absolute value with a set threshold value, and judging whether the surface of the part to be tested has defects. The invention can accurately and efficiently detect the surface of the part.
Description
Technical Field
The invention belongs to the technical field of machine vision, and relates to a part surface defect detection method based on line laser scanning and machine vision.
Background
The detection of the surface defects of the parts is an important technical means for ensuring the use safety of the parts, and when the defects exist on the surfaces of the parts, if the defects are not found in time, the production quality and the production efficiency of the machine can be affected, immeasurable loss is caused, and the life safety of people can be threatened. The traditional manual detection method is characterized in that the defect inspection is carried out on the parts manually, and the method has large workload and low efficiency. In addition, in the process of manual detection, the technical quality and experience of the detection personnel are uneven, so that whether the part is defective or not can be judged according to the personnel. Meanwhile, the subjectivity of manual detection is strong, and the phenomenon of missing detection and false detection is easy to occur.
The machine vision is a modern detection technology for replacing human eyes with an industrial camera CCD, the industrial camera CCD is used for carrying out image processing on a detected object, image information is converted into a digital signal, and then required characteristics are extracted from the digital signal, so that the state of the detected object is detected, and the line laser is used for positioning defects and determining the depth. Machine vision technology is now widely used in many fields and plays an increasingly important role therein.
Disclosure of Invention
The invention aims to provide a part surface defect detection method based on line laser scanning and machine vision, by which the surface of a part can be accurately and efficiently detected.
The technical scheme adopted by the invention is that the part surface defect detection method based on line laser scanning and machine vision specifically comprises the following steps:
step 1, calibrating a camera, obtaining internal and external parameters of the camera, and shooting an image without laser irradiation as a reference image;
step2, acquiring an image by using an image acquisition system;
Step 3, sequentially carrying out Gaussian filtering, image difference, gaussian smoothing, stripe center line extraction and coordinate conversion on the image to obtain three-dimensional point cloud data of the surface of the part to be detected;
Step 4, aiming at the standard parts with the surface without defects, executing the operations of the steps 1-3 to obtain three-dimensional point cloud data images of the standard parts with the surface without defects;
and 5, performing difference between the data obtained in the step 3 and the data obtained in the step 4, taking the absolute value of the difference, comparing the absolute value with a set threshold value, and judging whether the surface of the part to be tested has defects or not according to the comparison result.
The present invention is also characterized in that,
The specific process of camera calibration in step 1 is as follows:
The camera is used for shooting 10-20 chessboard images, the number of angular points contained in each image is detected by using an angular point detection function carried by opencv, the three-dimensional coordinates and pixel coordinates of the angular point coordinates are compared, and the calibration process of the camera is completed, wherein the calibration result comprises an internal parameter matrix of the camera, distortion coefficients, and rotation vectors and translation vectors of each image.
In step 2, the image acquisition system comprises a movable displacement platform for driving the part to do uniform motion, the part to be detected is placed on the movable displacement platform, a CCD industrial camera is arranged right above the part to be detected, a line laser emitter is arranged obliquely above the part to be detected, and the CCD industrial camera is sequentially connected with a computer and a singlechip.
In step3, the specific process of gaussian filtering is as follows:
Each pixel in the image is scanned with a template, and the value of the center pixel point of the template is replaced with the weighted average gray value of the pixels in the neighborhood determined by the template.
In step3, the specific process of image difference is as follows:
Step a, traversing the pixel points of the image, and dividing R, G, B minutes of each pixel point in the image
Separating out;
Step b, the pixel points at the corresponding positions of the striped image and the non-striped image are subjected to difference by adopting the following formula (1):
dst(x,y,z)＝src1(x,y,z)-src2(x,y,z) (1)；
Wherein dst (x, y, z) is R, G, B of a pixel point of the image after difference, src1 (x, y, z) is R, G, B of a corresponding pixel point in the image with the stripe pattern, and src2 (x, y, z) is R, G, B of a corresponding pixel point in the image without the stripe pattern
And c, repeating the step b until all the pixel points finish the difference calculation, and obtaining a differential image dst.
In step 3, the specific process of extracting the stripe center line is as follows:
Step a, traversing each pixel point in the image according to the columns, and finding out the brightest pixel point in each column.
And b, detecting a straight line by adopting Hough transformation.
The specific process of the step5 is as follows:
step 5.1, taking the corresponding Z coordinate values of the three-dimensional point cloud data of the surface of the part to be detected and the three-dimensional point cloud data of the standard part under the same X, Y coordinates to make a difference, and if the formula (2) shows that:
Hi＝Z(Xi,Yi)-Z1(Xi,Yi) (2)；
Wherein Z (X i,Yi) is the Z coordinate at the point (X i,Yi) on the standard part with no defects on the surface, Z 1(Xi,Yi) is the Z coordinate at the point (X i,Yi) on the part to be measured, H i is the same X, Y, and the difference between the Z coordinate value of the standard part with no defects on the lower surface of the coordinates and the Z coordinate value of the surface of the part to be measured;
Step 5.2, comparing the absolute value |h i | of H i with a set threshold value Δ; if the absolute value H i is equal to the absolute value of the component to be measured, namely within the error range determined by the threshold value, judging that the surface of the component to be measured is defect-free; if the absolute value H i is equal to the absolute value delta, namely the absolute value delta is out of the error range determined by the threshold value, judging that the surface of the part to be detected is defective.
The detection method provided by the invention has the beneficial effects that the line laser generator in the image acquisition system is adopted to emit line laser to irradiate the surface of the part, and then the CCD camera is used for shooting the line laser stripes which are modulated by the height of the part and deform. Because the whole surface of the part needs to be shot, the part can be driven by the speed controller to do uniform motion, and therefore the line laser can uniformly sweep the surface of the part. The camera transmits the acquired photo to the computer, the computer carries out preprocessing such as median filtering and image threshold segmentation on the acquired photo, and then carries out operations such as stripe center line extraction and coordinate conversion on the preprocessed photo, so that three-dimensional information of the surface of the part can be obtained. Comparing the height data with standard data to find out whether the surface of the part has defects, if so, the computer immediately sends instructions to the singlechip through the serial port to control the on-off of the small lamp on the singlechip, and controls the speed controller to stop through the relay. The invention can accurately and efficiently detect the surface of the part by a visual detection technology.
Drawings
Fig. 1 is a schematic structural diagram of an image acquisition system in a method for detecting surface defects of a part based on line laser scanning and machine vision.
In the figure, 1, a movable displacement platform, 2, a part to be detected, 3, a CCD industrial camera, 4, a line laser transmitter, 5, a computer and 6, a singlechip.
Detailed Description
The invention will be described in detail below with reference to the drawings and the detailed description.
The invention discloses a part surface defect detection method based on line laser scanning and machine vision, which specifically comprises the following steps: step 1, calibrating a camera, obtaining internal and external parameters of the camera, and shooting an image without laser irradiation as a reference image;
In step 1, the specific process of camera calibration is as follows:
The camera is used for shooting 10-20 chessboard images, the number of angular points contained in each image is detected by using an angular point detection function carried by opencv, the three-dimensional coordinates and pixel coordinates of the angular point coordinates are compared, and the calibration process of the camera is completed, wherein the calibration result comprises an internal parameter matrix of the camera, distortion coefficients, and rotation vectors and translation vectors of each image. The result of the camera calibration can be used for correcting the photographed image and for subsequent coordinate conversion.
Step2, acquiring an image by using an image acquisition system;
As shown in fig. 1, the image acquisition system comprises a mobile displacement platform 1 for driving a part to do uniform motion, a part to be detected 2, a CCD industrial camera 3 arranged right above the part and used for image acquisition, and a line laser emitter 4 arranged obliquely above the part, wherein a computer 5 used for performing a series of image processing, the CCD industrial camera 3 is connected with the computer 5, and also comprises a 51 singlechip 6 used for fault display, and the singlechip 6 is connected with the computer 5; the model of the singlechip 6 is 89C51 singlechip.
The line laser emitted by the line laser is as narrow as possible, so that the workload of the subsequent computer for image processing can be reduced as much as possible.
The arrangement position of the CCD camera is located right above the part as far as possible, so that the CCD camera can clearly shoot the surface morphology information of the part.
The working principle of the image acquisition system is as follows: the computer 5 carries out image processing software and is written by VS2013+opencv2.4.9, so that the detection of the surface defects of the parts is realized, and when the defects exist on the surfaces of the parts, the defects are displayed by the on-off of a small lamp on the singlechip and the speed controller is controlled by a relay to stop the machine. Secondly, the arrangement positions of the CCD industrial camera 3 and the line laser transmitter 4 ensure that the CCD camera can clearly shoot a stripe pattern deformed by the high modulation of an object, and meanwhile, the shot image is subjected to Gaussian filtering, image difference, gaussian smoothing, stripe center line extraction and coordinate conversion to obtain three-dimensional point cloud data of the surface of the part.
Step 3, sequentially carrying out Gaussian filtering, image difference, gaussian smoothing, stripe center line extraction and coordinate conversion on the image to obtain three-dimensional point cloud data of the surface of the part to be detected;
the specific process of Gaussian filtering is as follows:
Each pixel in the image is scanned with a template (or convolution, mask), and the value of the center pixel point of the template is replaced with the weighted average gray value of the pixels in the neighborhood determined by the template.
The specific process of image difference is as follows:
the image difference is to perform subtraction operation on two images, that is, the gray values or color components of the corresponding pixels of the two images are subtracted.
(1) Traversing the image pixels, and dividing R, G, B points of each pixel in the image
Separating out;
(2) The pixel points of the corresponding positions of the striped image and the non-striped image are subjected to difference:
dst(x,y,z)＝src1(x,y,z)-src2(x,y,z) (1)；
Wherein dst (x, y, z) is R, G, B of a pixel point of the image after difference, src1 (x, y, z) is R, G, B of a corresponding pixel point in the image with the stripe pattern, and src2 (x, y, z) is R, G, B of a corresponding pixel point in the image without the stripe pattern;
(3) And (3) repeating the step (2) until all pixel points complete the related calculation, and obtaining a differential image dst.
The specific process of extracting the stripe center line is as follows:
(1) Each pixel in the image is traversed by column, and the brightest pixel in each column is found.
(2) Hough transformation detection straight line
The specific process of detecting straight lines by Hough transformation is as follows:
(a) Obtaining edge information of the image;
(b) Drawing a straight line in k-b space for each point in the edge image;
(c) For points on each line we take the method of "voting" (vot), i.e. accumulating: a straight line passes through this point, the value of which is increased by 1;
(d) Traversing k-b space to find out local maximum points, and the coordinates (k, b) of the points are the slope and intercept of the straight line in the original image.
The principle of coordinate transformation is a laser triangulation method;
Step 4, aiming at the standard parts with the surface without defects, executing the operations of the steps 1-3 to obtain three-dimensional point cloud data images of the standard parts with the surface without defects;
and 5, performing difference between the data obtained in the step 3 and the data obtained in the step 4, taking the absolute value of the difference, comparing the absolute value with a set threshold value, and judging whether the surface of the part to be tested has defects or not according to the comparison result.
The specific process of the step5 is as follows:
step 5.1, taking the corresponding Z coordinate values of the three-dimensional point cloud data of the surface of the part to be detected and the three-dimensional point cloud data of the standard part under the same X, Y coordinates to make a difference, and if the formula (2) shows that:
Hi＝Z(Xi,Yi)-Z1(Xi,Yi) (2)；
Wherein Z (X i,Yi) is the Z coordinate at the point (X i,Yi) on the standard part with no defects on the surface, Z 1(Xi,Yi) is the Z coordinate at the point (X i,Yi) on the part to be measured, H i is the same X, Y, and the difference between the Z coordinate value of the standard part with no defects on the lower surface of the coordinates and the Z coordinate value of the surface of the part to be measured;
Step 5.2, comparing the absolute value |h i | of H i with a set threshold value Δ; if the absolute value H i is equal to the absolute value of the component to be measured, namely within the error range determined by the threshold value, judging that the surface of the component to be measured is defect-free; if the absolute value H i is equal to the absolute value delta, namely the absolute value delta is out of the error range determined by the threshold value, judging that the surface of the part to be detected is defective.
Claims (1)
1. A part surface defect detection method based on line laser scanning and machine vision is characterized in that: the method specifically comprises the following steps:
step 1, calibrating a camera, obtaining internal and external parameters of the camera, and shooting an image without laser irradiation as a reference image;
the specific process of camera calibration in the step 1 is as follows:
Shooting 10-20 chessboard images by using a camera, detecting the number of corner points contained in each picture by using a corner point detection function carried by opencv, and comparing the three-dimensional coordinates and pixel coordinates of the corner point coordinates to finish the calibration process of the camera, wherein the calibration result comprises an internal parameter matrix of the camera, a distortion coefficient, a rotation vector and a translation vector of each image;
step2, acquiring an image by using an image acquisition system;
In the step 2, the image acquisition system comprises a mobile displacement platform for driving the part to do uniform motion, the part to be detected is placed on the mobile displacement platform, a CCD industrial camera is arranged right above the part to be detected, a line laser transmitter is arranged obliquely above the part to be detected, and the CCD industrial camera is sequentially connected with a computer and a singlechip;
Step 3, sequentially carrying out Gaussian filtering, image difference, gaussian smoothing, stripe center line extraction and coordinate conversion on the image to obtain three-dimensional point cloud data of the surface of the part to be detected;
In the step 3, the specific process of gaussian filtering is as follows:
scanning each pixel in the image by using a template, and replacing the value of the central pixel point of the template by using the weighted average gray value of the pixels in the neighborhood determined by the template;
in the step 3, the specific process of image difference is as follows:
step a, traversing the pixel points of the image, and separating R, G, B of each pixel point in the image;
Step b, the pixel points at the corresponding positions of the striped image and the non-striped image are subjected to difference by adopting the following formula (1):
dst(x,y,z)＝src1(x,y,z)-src2(x,y,z) (1)；
Wherein dst (x, y, z) is R, G, B of a pixel point of the image after difference, src1 (x, y, z) is R, G, B of a corresponding pixel point in the image with the stripe pattern, and src2 (x, y, z) is R, G, B of a corresponding pixel point in the image without the stripe pattern
Step c, repeating the step b until all pixel points finish difference calculation, and obtaining a differential image dst;
In the step 3, the specific process of extracting the stripe center line is as follows:
Step a, traversing each pixel point in the image according to the columns, and finding out the brightest pixel point in each column;
Step b, detecting a straight line by adopting Hough transformation;
Step 4, aiming at the standard parts with the surface without defects, executing the operations of the steps 1-3 to obtain three-dimensional point cloud data images of the standard parts with the surface without defects;
step 5, the data obtained in the step 3 and the data obtained in the step 4 are subjected to difference, the absolute value of the difference is taken, the absolute value is compared with a set threshold value, and whether the surface of the part to be tested has defects or not is judged according to the comparison result;
the specific process of the step 5 is as follows:
step 5.1, taking the corresponding Z coordinate values of the three-dimensional point cloud data of the surface of the part to be detected and the three-dimensional point cloud data of the standard part under the same X, Y coordinates to make a difference, and if the formula (2) shows that:
Hi＝Z(Xi,Yi)-Z1(Xi,Yi) (2)；
Wherein Z (X i,Yi) is the Z coordinate at the point (X i,Yi) on the standard part with no defects on the surface, Z 1(Xi,Yi) is the Z coordinate at the point (X i,Yi) on the part to be measured, H i is the same X, Y, and the difference between the Z coordinate value of the standard part with no defects on the lower surface of the coordinates and the Z coordinate value of the surface of the part to be measured;
Step 5.2, comparing the absolute value |h i | of H i with a set threshold value Δ; if the absolute value H i is equal to the absolute value of the component to be measured, namely within the error range determined by the threshold value, judging that the surface of the component to be measured is defect-free; if |H i | > DELTA, i.e. outside the error range determined by the threshold value, the surface of the part to be tested is judged to be defective.
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