CN102288613A - Surface defect detecting method for fusing grey and depth information - Google Patents
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Abstract
The invention relates to an on-line detecting method for surface defects of an object and a device for realizing the method. The accuracy for the detection and the distinguishing of the defects is improved through the fusion of grey and depth information, and the method and the device can be applied to the detection of the object with a complicated shape and a complicated surface. A grey image and a depth image of the surface of the object are collected by utilizing the combination of a single colored area array CCD (charge-coupled device) camera and a plurality of light sources with different colors, wherein obtaining of the depth information is achieved through a surface structured light way. The division and the defect edge extraction of the images are carried out through the pixel level fusion of the depth image and the grey image, so that the area where the defects are positioned can be detected more accurately. According to the detected area with the defects, the grey characteristics, the texture characteristics and the two-dimensional geometrical characteristics of the defects are extracted from the grey image; the three-dimensional geometrical characteristics of the defects are extracted from the depth image; further, the fusion of characteristic levels is carried out; and a fused characteristic quantity is used as the input of a classifier to classify the defects, thereby achieving the distinguishing of the defects.
Description
Technical field
The device that the present invention relates to a kind of body surface defect online detection method and realize this method, especially the product surface for shape or surface appearance more complicated detects, and can improve the accuracy rate of defects detection.
Background technology
The surface imperfection Automatic Measurement Technique starts from the seventies in 20th century, and to 20 end of the centurys, developed countries such as Germany, the U.S., Japan have developed the machine vision surface on-line detecting system with practical value.Mechanical vision inspection technology is divided by its handled data type, can be divided into the detection based on bianry image, gray level image, coloured image and depth image, and present surface detecting system generally adopts the gray level image detection method.The gray level image detection method according to the variation of defect area on gray scale, is handled gray level image, and is utilized the gray feature of defective that defective is detected and discerns by camera acquisition body surface gray level image.This gray scale detection method exists following problem:
(1) the gray level image detection method is applicable to that the single body surface of background detects, for the background complex surfaces, owing to be difficult to from background, judge the zone at defective place by half-tone information, therefore can produce a large amount of omissions and flase drop, influence the reliability of testing result.
(2) the gray level image detection method is applicable to the surface detection of planar object, is the object of rule or irregular surface for the surface, adopts the gray level image detection method can cause shade even block phenomenon, and influence detects effect, produces omission.
(3) degree of depth of surface imperfection is not only and is characterized defective order of severity important indicator, and is the important parameter that carries out defects detection and identification.Adopt the gray level image detection method can't obtain the quantitative information of depth of defect, can impact the accurate identification of defective and the correct assessment of surface quality.
Can obtain case depth information by depth image, and detect the influence that defective is not subjected to body surface background situation and body form by depth image, thereby solves gray level image and detect the problem that exists, but the application of depth image detection method in the surface is detected also exists following problem:
(1) detects the defective that defective only is applicable to change in depth by depth image, do not have the defective of the degree of depth,, can't detect by depth image as spot class defective for some.
(2) some interfering materials of body surface also can cause the variation on the case depth, adopt the depth image detection method these interfering objects can be identified as defective, cause mistake to know.
(3) present depth image acquisition methods can't reach high resolution, therefore, is difficult to detect some tiny defectives by depth image, as crackle, impression etc.
Therefore, adopt the depth image detection method also can't realize the reliable detection of surface imperfection merely.Gray level image detection method and depth image detection method are combined, by the fusion of half-tone information and depth information, can be in conjunction with the advantage of two kinds of methods, thus improve the reliability of defects detection.
Traditional information fusion often refers to the information fusion of multisensor, if obtain half-tone information and depth information by independent video camera respectively, not only need to increase the quantity of video camera, but also need carry out registration, increased the complicacy of system the image that different cameras collects.The present invention is by the combination of single colourful video camera and many different colored light sources, realized that the separate unit video camera obtains the gray level image and the depth image of body surface simultaneously, not only reduced the quantity of video camera, and gray level image corresponding with depth image be the same area of body surface, need not to carry out the registration of image, reduced the difficulty of information fusion.
Summary of the invention
The present invention utilizes the separate unit video camera to obtain the gray level image and the depth image of body surface simultaneously, the zone of the fusion detection defective by half-tone information and depth information, and defective discerned, thereby improve the accuracy rate of defects detection and identification.
The present invention is achieved in that
The present invention gathers the gray level image and the depth image of body surface by the combination of single colourful Array CCD Camera and blue light or red illumination light source, the same area of described blue light or red illumination light source irradiation object, described depth image is to obtain by the area-structure light mode, the light source green glow of area-structure light, the lighting source of described gray level image is with blue light or ruddiness, from the coloured image that collects, isolate the image of blue light or ruddiness and green glow, wherein blue light or ruddiness image are exactly surperficial gray level image, and the green glow image is exactly the area-structure light projected image; Pixel-level fusion by depth image and gray level image is carried out cutting apart and the defective edge extracting of image, thereby can detect the zone at defective place more accurately, according to detected defect area, from gray level image, extract gray feature, textural characteristics and the two-dimensional geometry feature of defective, from depth image, extract the three-dimensional geometry feature of defective, and carry out the fusion of feature level, the characteristic quantity after merging is carried out the Classification and Identification of defective as the input of sorter.
Another technical scheme of the present invention is that above-mentioned feature level fusion is to carry out cutting apart and the defective edge extracting of image by the fusion of depth image and gray level image, because surface imperfection has caused the variation of object surface depth, therefore in depth image, seek the point that the degree of depth is undergone mutation, according to these degree of depth catastrophe points image is cut apart, because the resolution of gray level image is than depth map image height, therefore can in gray level image, carry out the edge of defective, thereby obtain the accurate edge of defective according to the segmentation result of depth image.
Another technical scheme of the present invention is that the Classification and Identification of above-mentioned defective is that feature level by depth image and gray level image merges, extract defective gray feature, textural characteristics and two-dimensional geometry feature from gray level image, described defective gray feature comprises mean value, variance, flexure, kurtosis, energy, the entropy of gray scale; Described textural characteristics comprises second moment, entropy, contrast and the homogeneity of texture; Described two-dimensional geometry feature comprises simple descriptor, shape description symbols and invariant moments etc., and the described three-dimensional geometry feature of extracting defective from depth image comprises invariant moments and Fourier descriptor.
The present invention mainly improves the reliability of defects detection and identification by following two aspects:
(1) in carrying out the Pixel-level fusion process of half-tone information and depth information, utilize the depth information on surface that gray level image is cut apart, and extract the edge of defective, can improve gray level image like this and cut apart accuracy with the defective edge extracting.The difficult point of image segmentation is the uncertainty of half-tone information, just the space three-dimensional object is projected to two dimensional image, has lost a lot of information.Utilizing depth information to carry out gray level image cuts apart and has then improved the accuracy cut apart and the efficient of partitioning algorithm.Edge extracting also is the difficult point in the Flame Image Process, because the edge of defective can produce shade or uneven illumination phenomenon, is difficult to accurately extract complete defective edge.The accuracy that improves image segmentation can reduce the omission of defective, and the accurate extraction that the accuracy of raising defective edge extracting then can be defect characteristic provides the basis, thereby reduces the flase drop of defective.
(2) with traditional defect characteristic based on gray level image, as gray feature, textural characteristics, two-dimensional geometry feature etc. and the defective three-dimensional geometry Feature Fusion of extracting by depth information, can improve the accuracy of classification of defects, reduce the mistake of defective and know.
Meanwhile, the stripe edge that half-tone information is used for the area-structure light projected image is extracted reliability, precision and the efficient that also can improve the stripe edge extraction, thereby improves accuracy and efficient that depth information extracts.
Adopting blue light is the wavelength weak point of blue light as the purpose of gray level image lighting source, for small defective better detection effect will be arranged.Adopting green glow is when being used for the illumination of high temp objects as the purpose of area-structure light light source, because the ruddiness and the infrared light of high temp objects radiation exist the different of wave band with the green glow of lighting source, green glow can be extracted from radiant light, thereby avoid the influence of bias light.Need surface bright, that the details in a play not acted out on stage, but told through dialogues combination detects for some, can be with the light source of blue diffused light as bright field illumination, red directional light is as the light source of dark ground illumination, camera acquisition to B and R passage be respectively light field image and darkfield image, the reflection of two kinds of images all be the half-tone information on surface.The depth information that obtains in conjunction with the G passage detects defective simultaneously, can further improve the accuracy of defects detection.
The invention has the beneficial effects as follows: by the fusion of gray scale and depth information, not only can improve the accuracy rate of defects detection, and can improve the efficient of algorithm, satisfy the requirement of online detection.
Description of drawings
Fig. 1 is the device that obtains surperficial gray level image and depth image.Among Fig. 1: 1 is object to be detected, and 2 is colored Array CCD Camera, and 3 is blue diffused light light source, and 4 is green area-structure light light source.
Fig. 2 is the detection method of surface flaw process flow diagram that half-tone information and depth information merge.
Embodiment
Among Fig. 1, blue diffused light light source 3 and green area-structure light light source 4 shine the same area on examined object 1 surface, and colored Array CCD Camera 2 is gathered the image of examined object 1 surperficial light area.In the coloured image that video camera 2 collects, blue channel (being the B passage) is exactly the reflected light image that light source 3 shines object 1 surface, and green channel (G passage) is exactly the reflected light image that light source 4 shines object 1 surface.Because light source 3 emissions is diffused light, so the B channel image is the surperficial gray level image of object 1.Because light source 3 emissions is area-structure light, so the G channel image is the area-structure light image on object 1 surface.The area-structure light projected image also need carry out depth extraction step, just can convert the depth image on surface to.Its step is as follows:
(1) extract area-structure light projected image stripe edge coordinate, this coordinate is the image coordinate of stripe edge in the two dimensional image plane.
(2) calculate the three dimensional space coordinate of stripe edge according to formula 1:
In the formula: s is a scale factor, (X
Wi, T
Wi, Z
Wi) be the three dimensional space coordinate of i unique point; (u
i, v
i) be the image coordinate of i unique point; m
IjBe the camera parameters that obtains through demarcation in advance.
(3) Z that step 2 is obtained
WiNormalize to [0,255],
Will
Project to (u
i, v
i) plane, with
Be gray-scale value, then can obtain the depth image on surface.
Because what depth image was corresponding with gray level image is the same area of body surface, need not registration and just can carry out image co-registration.The image co-registration process is divided into two processes:
(1) Pixel-level merges.Pixel-level merges the zone that is used to detect the defective place, and its main performing step is image segmentation and defective edge extracting.Image segmentation step is finished by depth image, and its principle is because surface imperfection has caused the variation of object surface depth, therefore seeks the point that the degree of depth is undergone mutation in depth image, according to these degree of depth catastrophe points image is cut apart.The advantage of carrying out image segmentation in depth image is that depth image is not subjected to the influence of body surface situation and surround lighting, the reliability height of segmentation result.But, because the resolution of depth image is low, therefore in depth image, be difficult to extract the accurate edge of defective, need in gray level image, carry out the edge extracting of defective according to the segmentation result of depth image.Because what depth image was corresponding with gray level image is the same area, so the segmentation result of depth image can be directly used in the defective edge extracting in the gray level image.
(2) the feature level merges.The feature level merges the Classification and Identification that is used for defective, and the two dimensional character that its main step is a defective extracts with three-dimensional feature and extracts, and two dimensional character and three-dimensional feature fusion back are classified to defective as characteristic quantity.According to detected defect area, from gray level image, extract gray feature, textural characteristics and the two-dimensional geometry feature of defective, from depth image, extract the three-dimensional geometry feature of defective, and carry out the fusion of feature level, characteristic quantity after merging is carried out classification of defects as the input of sorter, thereby realize the identification of defective.
Gray feature obtains by the grey level histogram of image, comprising:
Mean value:
Variance:
Flexure:
Kurtosis:
Energy:
Entropy:
Among the formula 2-7, P (b) is the single order probability distribution of gradation of image, and it is defined as:
P(b)=P{g(i,j)=b}(0≤b≤L-1) (8)
In the formula 8, b is the quantized level of image, is total to the L level, and (i j) is pixel (i, gray-scale value j) to g.
Textural characteristics adopts statistic law, by gray level co-occurrence matrixes P (g
1, g
2) obtain P (g
1, g
2) expression normalization after gray-scale value be respectively g
1And g
2Pixel to number.Textural characteristics comprises:
Second moment
Entropy
Contrast
Homogeneity
In the formula 12, k is a non-zero constant.
Two-dimentional geometrical shape waits by simple descriptor, shape description symbols and invariant moments represents that simple descriptor comprises region area, border girth, regional barycenter etc., and shape description symbols comprises compactedness, excentricity, spherical property and circle.The three-dimensional geometry feature comprises the invariant moments and the Fourier descriptor of defect area in the depth image.
Claims (3)
1. the detection method of surface flaw that merges of gray scale and depth information, it is characterized in that: gray level image and the depth image of gathering body surface by the combination of single colourful Array CCD Camera and blue light or red illumination light source, the same area of described blue light or red illumination light source irradiation object, described depth image is to obtain by the area-structure light mode, the light source green glow of area-structure light, the lighting source of described gray level image is with blue light or ruddiness, from the coloured image that collects, isolate the image of blue light or ruddiness and green glow, wherein blue light or ruddiness image are exactly surperficial gray level image, and the green glow image is exactly the area-structure light projected image; Pixel-level fusion by depth image and gray level image is carried out cutting apart and the defective edge extracting of image, thereby can detect the zone at defective place more accurately, according to detected defect area, from gray level image, extract gray feature, textural characteristics and the two-dimensional geometry feature of defective, from depth image, extract the three-dimensional geometry feature of defective, and carry out the fusion of feature level, the characteristic quantity after merging is carried out the Classification and Identification of defective as the input of sorter.
2. the detection method of surface flaw that a kind of gray scale as claimed in claim 1 and depth information merge, it is characterized in that: it is to carry out cutting apart and the defective edge extracting of image by the fusion of depth image and gray level image that described feature level merges, because surface imperfection has caused the variation of object surface depth, therefore in depth image, seek the point that the degree of depth is undergone mutation, according to these degree of depth catastrophe points image is cut apart, because the resolution of gray level image is than depth map image height, therefore can in gray level image, carry out the edge of defective, thereby obtain the accurate edge of defective according to the segmentation result of depth image.
3. the detection method of surface flaw that a kind of gray scale as claimed in claim 1 and depth information merge, it is characterized in that: the Classification and Identification of described defective is the feature level fusion by depth image and gray level image, extract defective gray feature, textural characteristics and two-dimensional geometry feature from gray level image, described defective gray feature comprises mean value, variance, flexure, kurtosis, energy, the entropy of gray scale; Described textural characteristics comprises second moment, entropy, contrast and the homogeneity of texture; Described two-dimensional geometry feature comprises simple descriptor, shape description symbols and invariant moments etc., and the described three-dimensional geometry feature of extracting defective from depth image comprises invariant moments and Fourier descriptor.
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Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20030235344A1 (en) * | 2002-06-15 | 2003-12-25 | Kang Sing Bing | System and method deghosting mosaics using multiperspective plane sweep |
CN101639452A (en) * | 2009-09-11 | 2010-02-03 | 北京科技大学 | 3D detection method for rail surface defects |
CN101871895A (en) * | 2010-05-10 | 2010-10-27 | 重庆大学 | Laser scanning imaging nondestructive inspection method for hot continuous casting blank surface defects |
-
2011
- 2011-05-11 CN CN201110121520.4A patent/CN102288613B/en not_active Expired - Fee Related
Patent Citations (3)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
US20030235344A1 (en) * | 2002-06-15 | 2003-12-25 | Kang Sing Bing | System and method deghosting mosaics using multiperspective plane sweep |
CN101639452A (en) * | 2009-09-11 | 2010-02-03 | 北京科技大学 | 3D detection method for rail surface defects |
CN101871895A (en) * | 2010-05-10 | 2010-10-27 | 重庆大学 | Laser scanning imaging nondestructive inspection method for hot continuous casting blank surface defects |
Non-Patent Citations (2)
Title |
---|
徐科等: "基于激光线光源的钢轨表面缺陷三维检测方法", 《机械工程学报》 * |
皮敏捷等: "基于多条激光线的钢板表面缺陷三维检测方法", 《机电产品开发与创新》 * |
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