JP3688520B2 - Surface inspection apparatus and surface inspection method - Google Patents

Description

で表わされる。その変換係数は図８に示されている通りである。
【００３５】
図５は、Ｓｏｂｅｌ変換を行なった後の画像（Ｓｔｅｐ１：第１変換画像）を示すものである。この画像は、原画像における各画素の勾配値を表わすものであり、原画像と同じく８ビットの濃淡画像データとして示される。つまり明るい部分ほど勾配が大きいことを示している。また、Ｓｏｂｅｌ変換の演算において２５５を超える値が演算された場合には、その値は２５５にする。
【００３６】
次に、図３の第１欠陥候補抽出部４２における処理内容を説明する。この第１欠陥候補抽出部４２においては、図２（ｂ）に示すように、第１しきい値と第２しきい値とを設定しており、第１しきい値よりも暗い部分と第２しきい値よりも明るい部分を除去する、即ち、該当する画素を０レベルに変換するものである。これは、表面欠陥が存在すると、暗部２ｂと明部２ａの境界が緩やかになり中間調のグレーになるため、この中間調の画素を１レベルに変換して抽出しようとするものである。なお、これら第１、第２しきい値の値は被検体１の種類に応じて設定変更可能にするのが好ましい。画像変換をするには、予め画像変換用のテーブル（ＬＵＴ）を用意しておくことにより効率よく処理することができる。
【００３７】
次に、第２欠陥抽出部４３（図３参照）において、前述した第１変換画像のうち、勾配の大きい部分を除去、すなわち、０レベルに変換する。この勾配が大きいと言うことは、明部２ａから暗部２ｂへの変化、あるいは、暗部２ｂから明部２ａへの変化が急激であることを意味するものであり、即ち、表面欠陥が存在しないことを意味するものである。つまり、図２（ｂ）にも示されるように、表面欠陥が存在すると勾配の値が小さくなるので、勾配の大きな部分を除去することで、表面欠陥の部分を抽出することができる。この第２欠陥抽出部４３における処理も、予め画像変換用のテーブル（ＬＵＴ）を用意しておくのが好ましい。
【００３８】
図４に示される原画像に対して、第１欠陥候補抽出部４２と第２欠陥抽出部４３において処理を施した後の画像（Ｓｔｅｐ２：第２変換画像）を図６に示す。この画像は、０レベルか１レベルかの２値画像である。画像のほぼ中央部にまとまった１レベルの領域がみられるが、この領域が表面欠陥が存在する領域と推定される。また、この第２変換画像においては、表面欠陥が存在する領域以外にも、微小な１レベルの領域が点在している。これは、照明装置２のシェーディングの影響が除去しきれていなかったり、あるいは、正常部のチェッカーパターンの輪郭部に生じる中間調領域が本実験で抽出した輝度勾配の高い領域より１〜２画素程度大きいことにより、正常領域を誤って欠陥候補領域として検出したなどの理由によるものであり、これらの微小領域はノイズ成分であって表面欠陥ではない。これら微小領域は適切なシェーディング補正と輪郭領域の除去が行われれば発生しないと考えられる。適切なシェーディング補正と輪郭領域の除去が行われず、これら微小領域が欠陥候補領域として抽出された場合には、これら微小領域を処理するため収縮処理部４５（微小領域処理部）にて周知の４近傍収縮処理を２回行い、微小な孤立点を除去することで誤検知を回避することができる。なお、収縮処理としては、８近傍収縮処理でも良い。収縮処理後の画像（Ｓｔｅｐ３：第３変換画像）を図７に示す。図７も、２値画像であるが、中央部分にまとまった領域の１レベルの画素が見られる。
【００３９】
欠陥判定部４６では、第３変換画像に１レベルの画素が存在するか否かで、表面欠陥の有無を判定し、本実施形態では、１レベルの画素が１つでもあれば表面欠陥が存在するものと判定する。表面欠陥が存在する場合は、その旨をＴＶモニター５の画面に表示したり、別に設けられたランプに表示したり、適宜の手法で警告する。なお、欠陥判定部４６における判定手法は上記に限定されず、１レベルの画素数が所定数以上のときに表面欠陥が存在するものと判定しても良い。
【００４０】
図７に示される第３変換画像において、被検体１に表面欠陥が存在しない場合は、すべて０レベルの画像（真っ黒な画像）に変換されることになる。
【００４１】
図９は、表面欠陥が存在しないと推定される被検体１の原画像を示す。図４と比較すると、微細チェッカーパターンが歪んでいるが中間調レベルの画素が存在しないことが理解される。つまり、３次元曲面のような、被検体１に許容され得る凹凸が存在していたとしても、微細チェッカーパターンは少し歪んだ画像となるが、中間調レベルの画素は現われることがないので、間違えて許容され得る凹凸を表面欠陥として誤検出してしまうと言うことがない。また、図５との比較のために、図９の原画像をＳｏｂｅｌ変換した画像を図１０に示す。
【００４２】
＜テスト結果＞
図１１は、本発明による表面検査装置によるテスト結果を表で示すものである。欠陥種類はピラミッド型の凹凸が存在するフィルム（サンプルＮｏ．１〜Ｎｏ．ａ）を用いた。欠陥の強度は、「強」「中」「弱」の３段階である。見え方Ａ，Ｂとあるのは、原画像をオペレータが観察した場合に、表面欠陥がはっきりと認識できるものがＡレベルであり、表面欠陥は存在するのだが見え方は図９に近いものがＢレベルである。
【００４３】
テスト１では視野１２０ｍｍ（撮影される画像の領域の底辺の長さが１２０ｍｍ）で行ない、テスト２では視野８０ｍｍで行なっている。表面欠陥が「強」「中」では、テスト１，２ともに、表面欠陥を確実に検出できている。また、オペレータが原画像の観察から表面欠陥であると判定できないような「弱」の表面欠陥であっても、テスト２においては高い確率で検出できており、本発明による表面検査方法の優れている点が実証できた。
【００４４】
【発明の効果】
以上のように、微細チェッカーパターンを用いて、明部から暗部又は暗部から明部への明るさ変化の度合いを解析することにより、フィルム、ガラス面、塗装面、樹脂製品等の光沢面、などの表面欠陥を検出することができる。
【００４５】
本発明の構成によれば、目視検査や公知技術１，２，３（従来技術の欄参照）では困難であった微小凹凸欠陥の発見が容易になった。
また、被検体に許容され得る曲面（凹凸分布）が存在する場合でも、これを表面欠陥として誤検出することなく確実に識別し、微小凹凸欠陥を容易に発見することができた。
また、公知技術２のように、フィードバック制御をするために表面欠陥のない被検体をわざわざ準備する必要もなく、低コストにて表面欠陥の検出をすることができた。
【図面の簡単な説明】
【図１】本実施形態にかかる表面検査装置の構成を示す図
【図２】本発明の原理を説明する図
【図３】画像解析装置の構成を説明するブロック図
【図４】表面欠陥が存在する原画像を示す図
【図５】Ｓｏｂｅｌ変換後の第１変換画像を示す図
【図６】欠陥候補抽出を行なった後の第２変換画像を示す図
【図７】収縮処理を行なった後の第３変換画像を示す図
【図８】Ｓｏｂｅｌ変換の変換式を示す図
【図９】表面欠陥のない原画像を示す図
【図１０】図９の原画像に対しＳｏｂｅｌ変換を行なった画像を示す図
【図１１】本発明による表面検査装置のテスト結果を示す表
【図１２】従来技術を示す図
【符号の説明】
１ 被検体
２ 照明装置
２ａ 明部
２ｂ 暗部
３ ＣＣＤエリアカメラ
４ 画像解析装置
５ ＴＶモニター
４２ 第１欠陥候補抽出部
４３ 第２欠陥抽出部
４４ 微分処理部
４５ 収縮処理部[0001]
BACKGROUND OF THE INVENTION
The present invention relates to a surface inspection apparatus and a surface inspection method, and more particularly to a surface inspection apparatus and a surface inspection method for inspecting defects generated on glossy surfaces such as a film surface, a glass surface, and a painted surface. .
[0002]
[Prior art]
Such surface inspection technology is used for inspecting minute uneven surface defects present on a flat surface or curved surface such as a film, a plate-shaped product, or a product body. Some specific examples of such techniques are given below.
[0003]
(1) Japanese Patent Application Laid-Open No. 5-18728 (hereinafter referred to as known art 1)
The known technique 1 includes an illuminating unit that projects a lattice pattern on the surface of a subject (belt-like object) and an imaging unit that images the projected pattern, and processes an image obtained by the imaging unit. A surface defect of the subject is detected by detecting a distorted pattern.
[0004]
(2) Japanese Patent Application Laid-Open No. 5-288533 (hereinafter referred to as known art 2)
In the known technique 2, a display pattern is generated by a pattern generator, and an object (glass, sheet material, metal sheet material, painted surface, etc.) is illuminated by a pattern display element (light source), Capture images from a camera. Then, the difference between the image captured from the camera and the output waveform of the pattern generator is calculated by an analog difference circuit to detect a surface defect.
[0005]
(3) Reflective scattering method (hereinafter referred to as known art 3)
As shown in FIG. 12A, the known technique 3 illuminates the subject 1 with the fluorescent lamp 10, takes a reflected image with the camera 3, and detects the distortion of the end of the fluorescent lamp 10. Detect that there is a defect.
[0006]
[Problems to be solved by the invention]
However, the above known technique has the following problems.
That is, in the method of detecting the distortion of the lattice pattern of the known technique 1, although very minute unevenness on the surface may be detected with high sensitivity, the unevenness distribution in which the subject itself is indefinite and acceptable is acceptable. When it has, it will become difficult to distinguish such uneven distribution from surface defects. As such an object, for example, there is a film in which warpage is likely to occur. In this case, it is assumed that unevenness of the surface distributed over a wide range is normal, and only unevenness that occurs locally must be detected as a defect. As another example, the body of a production good (various types such as automobiles, housings of various products, front plates of display devices, etc.) is rarely a flat surface, and is formed by a curved surface such as a three-dimensional curved surface. There are many cases. Therefore, when a surface defect is to be detected from a subject, it is desired that only a locally generated unevenness is detected as a surface defect instead of erroneously detecting such a curved surface (unevenness) as a defect. In the known technique 1, since the lattice pattern is distorted even if it has a slight curved surface, there is a problem in terms of accuracy of detection accuracy. In order to solve such problems, not only two-dimensional measurement of still images, but also techniques such as tracking the temporal image change by moving the subject are not conceivable, but very advanced technology There was a problem that it would be expensive.
[0007]
In the known technique 2, since a surface defect is detected from the difference between the image captured from the camera and the output waveform of the pattern generator, there is a possibility that it can be detected with high sensitivity to extremely minute irregularities on the surface. However, when the subject itself has an acceptable curved surface (uneven distribution), this may be detected as a difference and erroneously detected as a surface defect.
[0008]
Further, in this publicly known technology 2, using a material having only a three-dimensional shape having no minute waviness defect, the deviation between the number of fringes of image data captured from the camera and the set fringe pattern is calculated, A technique is also disclosed in which this is fed back to the deviation calculation circuit and finally controlled so that the deviation becomes zero, so that the allowable uneven distribution of the subject itself is not erroneously detected.
[0009]
However, there is a problem that not only the configuration of feedback control for preventing erroneous detection is complicated, but also that feedback control is required for each subject to be examined, and the work becomes complicated. It was.
[0010]
The same applies to the known technique 3. As shown in FIGS. 12B and 12C, there is a problem that even if the subject 1 has a warp that should originally be allowed, this is detected.
[0011]
OBJECT OF THE INVENTION
The present invention has been made in view of the above circumstances, and an object of the present invention is to easily and reliably detect a surface defect without erroneously detecting a curved surface (unevenness distribution) acceptable for the subject as a surface defect. It is to provide a surface inspection apparatus and a surface inspection method capable of performing the above.
[0012]
[Means for Solving the Problems]
In order to achieve the above object, a surface inspection apparatus according to the present invention comprises:
Illumination means for illuminating the subject with a fine checker pattern composed of a repetitive pattern of bright and dark portions, an imaging means for imaging the subject illuminated with the checker pattern, and an image captured by the imaging means Image analysis means for analyzing the original image,
The image analysis means is configured to detect a surface defect of the subject by extracting and analyzing a halftone image portion between a distortion and a bright portion and a dark portion of the original image. It is a feature.
[0013]
The effect | action by this structure is as follows.
(A) The subject is illuminated with a predetermined fine checker pattern by the illumination means.
(B) The subject on which a predetermined fine checker pattern is illuminated (projected) is imaged by the imaging means.
(C) The image (original image) captured by the imaging unit is analyzed by the image analysis unit, and the degree of distortion of the original image and the brightness change of the bright part and the dark part are analyzed.
(D) The surface defect of the subject is detected based on the analysis result.
[0014]
The principle will be described with reference to FIG. When a predetermined fine checker pattern is imaged, there is a predetermined level difference between the brightness level (luminance value or photometric value) of the dark portion and the brightness level of the bright portion, and when there is no surface defect, FIG. As shown in FIG. 4, a sudden level change is observed at the boundary between the dark part and the bright part. On the other hand, if there is a surface defect, the level change becomes moderate at the boundary between the dark part and the bright part as shown in FIG. Therefore, a surface defect can be detected by extracting and analyzing the degree of brightness change from the bright part to the dark part or from the dark part to the bright part as a halftone image part .
[0015]
In addition, when there is a curved surface (unevenness distribution) that can be tolerated by the subject, the shape of the predetermined fine checker pattern to be imaged is imaged in a distorted state according to the shape of the curved surface. The possibility of being detected as the degree of change in brightness from the dark part to the bright part is extremely low or not at all, and it is not erroneously detected as a surface defect, and the certainty is increased. In addition, unlike the known technique 2, it is not necessary to prepare a subject having no surface defect in order to perform feedback control, which is simple. As a result, it has been possible to provide a surface inspection apparatus that can easily and reliably detect a surface defect without erroneously detecting a curved surface (unevenness distribution) acceptable for the subject as a surface defect.
[0016]
As a preferred embodiment of the present invention, the fine checker pattern is a checker pattern in which a bright part and a dark part are arranged at a dimensional ratio of 1: 1. An example of this checker pattern is shown in FIG. According to the principle of the present invention, the surface defect can be detected using such a checker pattern at the boundary part between the bright part and the dark part. Therefore, a checker pattern in which as many repeating patterns as possible are provided. Is good. For this purpose, it is preferable to arrange the bright part and the dark part at a dimensional ratio of 1: 1.
[0017]
As another preferred embodiment of the present invention, the imaging means is focused on the checker pattern of the illumination means.
Even when the subject is imaged by the imaging means, the imaging means usually images the subject from an oblique direction as shown in FIG. In this case, even if an attempt is made to focus on the subject, the entire subject cannot be focused because the distance between the upper end and the lower end of the subject is different from the imaging means. That is, an accurate surface inspection cannot be performed. Therefore, by focusing on the light / dark pattern of the illumination means, it is possible to capture an image in which the entire screen is in focus, and to perform an accurate surface inspection. Incidentally, as can be understood from FIG. 1A, the imaging means and the illumination means can be equivalently faced.
[0018]
As still another preferred embodiment of the present invention, the image analyzing means obtains a halftone image in which the brightness of each of a bright portion having a predetermined luminance or higher and a dark portion having a predetermined luminance or lower is 0 from the original image. A defect candidate extraction unit;
A differential processing unit for performing a differential process on the original image to obtain a first converted image;
A pixel having a large gradient in the first converted image is extracted, or both the extracted pixel and a pixel in the vicinity thereof are extracted, and the luminance of the pixel corresponding to the extracted pixel in the obtained halftone image is determined. 0 includes a second defect extraction unit that removes a normal region.
[0019]
The operation of the image analysis means with this configuration is as follows.
(E) A halftone level pixel is extracted by removing a bright portion above a predetermined level and a dark portion below a predetermined level from the original image. Removal means converting a corresponding pixel into a signal of 0 level, and extracting a halftone level pixel means converting the corresponding pixel to 1 level, for example.
(F) Differentiating the original image to obtain a first converted image. This differentiation process is performed in order to obtain the gradient in the image. For example, Sobel differentiation is used.
(G) A portion having a large gradient is extracted from the first converted image, and pixels having a large gradient are removed from the original image. This removal means, for example, converting a corresponding pixel in a portion with a large gradient to 0 level. This is because, as described above with reference to FIG. 2B, the normal part without surface defects has a sharp gradient at the boundary between the dark part and the bright part, so that the gradient increases.
[0020]
The presence of surface defects can be detected by the procedure as described above. In addition, it is not necessary to process the said effect | action in the order of (e) (f) (g), For example, you may process in the order of (f) (g) (e).
[0021]
As still another preferred embodiment of the present invention, the image analysis means further removes a micro region from the second converted image that has been processed by the first defect candidate extraction unit and the second defect extraction unit. And a micro-region processing unit that performs the processing.
[0022]
When surface defects have been extracted by the processing performed by the first defect candidate extraction unit and the second defect extraction unit that have already been described, but in addition to regions where surface defects exist, minute one-level regions are scattered There is. This is because the influence of shading of the lighting device has not been completely removed, so that the halftone luminance range was not properly selected, and the halftone area generated in the outline of the normal checker pattern is A normal area is erroneously detected as a defect candidate area by being about 1 to 2 pixels larger than the extracted area having a high luminance gradient. These regions are considered not to occur if appropriate shading correction and contour region deletion are performed, but can be removed by using, for example, a well-known contraction process by further providing the minute region processing unit. And a highly reliable surface inspection apparatus.
[0023]
In order to achieve the object of the present invention, the surface inspection method according to the present invention comprises:
Illuminating the subject with a fine checker pattern composed of a repetitive pattern of bright and dark portions , imaging the subject with the fine checker pattern illuminated, and the original imaged by the imaging means Analyzing the image,
The step of analyzing the image includes a step of extracting and analyzing a halftone image portion between a distortion and a bright portion and a dark portion of the original image, and a step of detecting a surface defect of the subject from the degree of the change. It is characterized by having.
[0024]
The operation by this configuration is the same as (a) (b) (c) (d) as described above, and the effect by this operation is also as described above.
[0025]
As a preferred embodiment of the present invention, the step of analyzing the image includes a first defect candidate extraction step for obtaining a halftone image in which the brightness of a bright portion of a predetermined level or higher and a dark portion of a predetermined level or lower is 0 from the original image. A differential processing step of performing a differential process on the original image to obtain a first converted image, and extracting a pixel having a large gradient of the first converted image, or both the extracted pixel and a pixel in the vicinity thereof The extracted halftone image includes a second defect extraction step in which the luminance of the pixel corresponding to the extracted pixel is set to 0 and a normal area is removed. Further, there may be mentioned one having a step of removing a minute region after performing the first defect candidate extraction step and the second defect extraction step.
[0026]
The operation by this configuration is the same as (e) (f) (g) as described, and the effect by this operation is also as described.
[0027]
DETAILED DESCRIPTION OF THE INVENTION
Preferred embodiments of the present invention will be described with reference to the drawings.
FIG. 1 is a diagram illustrating a configuration of a surface inspection apparatus according to the present embodiment.
In FIG. 1A, this surface inspection apparatus includes an illuminating device 2 (corresponding to an illuminating means) that illuminates a subject 1 with a fine checker pattern, and a subject 1 illuminated with the fine checker pattern. A CCD area camera 3 for imaging (corresponding to imaging means, hereinafter simply referred to as a camera), an image analysis device 4 for analyzing an image captured by the camera 3 (corresponding to image analysis means), and an image. And a TV monitor 5 for projecting.
[0028]
Details of the fine checker pattern of the illumination device 2 are shown in FIG. In this fine checker pattern, bright portions 2a and dark portions 2b are alternately repeated,
The dimension ratio (duty ratio) in the x direction is xB: xW = 1: 1.
The dimensional ratio in the y direction is yB: yW = 1: 1,
Furthermore, xB = yB and xW = yW. That is, the bright part 2a and the dark part 2b are squares of the same size.
[0029]
Although the principle of the present invention has already been described with reference to FIG. 2, if a surface defect exists, it is detected that the brightness level becomes moderate at the boundary between the bright part 2 a and the dark part 2 b, and therefore, the bright part 2 a. And dark portions 2b are preferably present as many as possible. For this purpose, it is reasonable to set the dimensional ratio of the dark part 2b and the bright part 2a to 1: 1. Further, from the relationship with the number of pixels of the CCD of the camera 3, if the bright portion 2 a and the dark portion 2 b are made too fine, the resolution is lowered. Therefore, in this embodiment, one side is about 10 pixels of the CCD. It is set as follows.
[0030]
Further, the focus (focus) of the camera 3 is adjusted so as to focus on the fine checker pattern of the illumination device 2, not the subject 1. As shown in FIG. 1A, since the camera 3 captures the subject 1 from an oblique direction, the camera 3 is positioned between the lower end and the upper end of the subject 1 even when focusing on the subject 1. Since the distance from is different, the entire subject 1 cannot be focused. Further, in the present invention, it is desirable that the change in brightness in the normal portion is large, but for that purpose, it is natural to focus on the brightness pattern of the illumination means. When the subject is focused, the light / dark pattern of the illumination means is observed as a blurred image, so that sufficient detection accuracy cannot be obtained. The light / dark pattern of the illuminating means and the imaging means are equivalently facing each other through the subject, and the entire screen can be focused, and an image can be acquired without impairing the steepness of the light / dark change. it can.
[0031]
<Image analysis procedure>
Next, a procedure for image analysis (image processing) will be described. FIG. 3 is a block diagram illustrating functions of the image analysis device 4.
First, as shown in FIG. 1A, the subject 1, the illumination device 2, and the camera 3 are set. Here, what is selected as the subject 1 to be surface-inspected is, for example, a film-like product, but is not limited to this. A transparent plate-like body such as glass, a metal sheet material, a surface Examples of the specimen 1 include those having a smooth inspection surface, such as painting, production goods (car bodies, various product casings, front plates of display devices, etc.), and photoreceptors.
[0032]
Next, the camera 3 captures an image of the subject 1. The captured image is converted into digital data by the A / D converter 40 of the image analysis device 4 and stored in the frame memory 41. Here, the image data is represented as 8-bit grayscale data, and can be obtained as 256-level density data (luminance data) from 0 (darkest) to 255 (brightest). FIG. 4 shows that the digitalized image data is displayed on the TV monitor 5. This is referred to as an original image for convenience of description (Step 0). In FIG. 4, it can be seen that the fine checker pattern is gently bent because the subject 1 itself has a three-dimensional curved surface, which does not correspond to a surface defect.
[0033]
Further, in FIG. 4, the portion corresponding to the dark portion 2b of the fine checker pattern is black and the portion corresponding to the bright portion 2a of the fine checker pattern is white. Depending on the type, the portion corresponding to the bright portion 2a may be projected in gray. In the central part of FIG. 4, a halftone gray part is seen, but this part is presumed to be a surface defect.
[0034]
FIG. 5 is a process for obtaining a two-dimensional gradient vector called Sobel transform (Sobel transform) for the original application of FIG. 4, and this is performed by the differentiation processing unit 44 of FIG. 3. The principle of Sobel transformation will be briefly described with reference to FIG. When the pixel of interest is f (i, j), the differential Δx in the x direction is

It is represented by The conversion coefficient is as shown in FIG.
[0035]
FIG. 5 shows an image after the Sobel conversion (Step 1: first converted image). This image represents the gradient value of each pixel in the original image, and is shown as 8-bit grayscale image data as in the original image. In other words, the brighter the portion, the greater the gradient. If a value exceeding 255 is calculated in the Sobel transformation, the value is set to 255.
[0036]
Next, the processing content in the 1st defect candidate extraction part 42 of FIG. 3 is demonstrated. In the first defect candidate extraction unit 42, as shown in FIG. 2 (b), the first threshold value and the second threshold value are set, and a portion darker than the first threshold value and the second threshold value are set. A portion brighter than the threshold value is removed, that is, the corresponding pixel is converted to 0 level. This is because if there is a surface defect, the boundary between the dark portion 2b and the light portion 2a becomes gentle and becomes halftone gray, and this halftone pixel is converted to one level and extracted. It is preferable that the first and second threshold values can be set and changed according to the type of the subject 1. In order to perform image conversion, an image conversion table (LUT) is prepared in advance, whereby efficient processing can be performed.
[0037]
Next, in the second defect extraction unit 43 (see FIG. 3), a portion having a large gradient is removed from the first converted image described above, that is, converted to 0 level. The fact that the gradient is large means that the change from the bright part 2a to the dark part 2b or the change from the dark part 2b to the bright part 2a is abrupt, that is, there is no surface defect. Means. That is, as shown in FIG. 2B, since the value of the gradient is reduced when the surface defect exists, the surface defect portion can be extracted by removing the portion having the large gradient. It is preferable to prepare an image conversion table (LUT) in advance for the processing in the second defect extraction unit 43 as well.
[0038]
FIG. 6 shows an image (Step 2: second converted image) after the first defect candidate extraction unit 42 and the second defect extraction unit 43 perform processing on the original image shown in FIG. This image is a binary image of 0 level or 1 level. A one-level region is observed in the center of the image, and this region is estimated to be a region where surface defects exist. Further, in the second converted image, in addition to the region where the surface defect exists, minute one-level regions are scattered. This is because the influence of the shading of the lighting device 2 is not completely removed, or the halftone region generated in the contour portion of the normal checker pattern is about 1 to 2 pixels from the region having a high luminance gradient extracted in this experiment. This is because the normal area is erroneously detected as a defect candidate area due to its large size, and these minute areas are noise components and not surface defects. These minute regions are considered not to occur if appropriate shading correction and contour region removal are performed. In the case where appropriate shading correction and outline region removal are not performed and these minute regions are extracted as defect candidate regions, the shrinkage processing unit 45 (minute region processing unit) 4 known to process these minute regions. Misdetection can be avoided by performing neighborhood contraction processing twice and removing minute isolated points. As the contraction process, an 8-neighbor contraction process may be used. FIG. 7 shows an image after the contraction process (Step 3: third converted image). FIG. 7 is also a binary image, but one-level pixels in a region gathered at the center can be seen.
[0039]
The defect determination unit 46 determines whether or not there is a surface defect depending on whether or not one level pixel exists in the third converted image. In this embodiment, if there is one pixel at one level, a surface defect exists. It is determined to be. If there is a surface defect, the fact is displayed on the screen of the TV monitor 5, displayed on a lamp provided separately, or warned by an appropriate method. Note that the determination method in the defect determination unit 46 is not limited to the above, and it may be determined that a surface defect exists when the number of pixels at one level is a predetermined number or more.
[0040]
In the third converted image shown in FIG. 7, when there is no surface defect in the subject 1, all the images are converted to a 0 level image (a black image).
[0041]
FIG. 9 shows an original image of the subject 1 estimated to have no surface defect. Compared to FIG. 4, it can be seen that the fine checker pattern is distorted but there are no halftone pixels. In other words, even if there are irregularities that can be allowed on the subject 1, such as a three-dimensional curved surface, the fine checker pattern becomes a slightly distorted image, but the halftone level pixels do not appear, so it is wrong. Therefore, it cannot be said that irregularities that can be tolerated are erroneously detected as surface defects. For comparison with FIG. 5, FIG. 10 shows an image obtained by Sobel transforming the original image of FIG.
[0042]
<Test results>
FIG. 11 is a table showing test results obtained by the surface inspection apparatus according to the present invention. The defect type used was a film (samples No. 1 to No. a) having pyramidal irregularities. There are three levels of defect strength: “strong”, “medium”, and “weak”. The appearances A and B are those where the surface defect can be clearly recognized when the operator observes the original image at the A level. Although the surface defect exists, the appearance is similar to FIG. B level.
[0043]
Test 1 is performed with a field of view of 120 mm (the length of the base of the imaged area is 120 mm), and Test 2 is performed with a field of view of 80 mm. When the surface defect is “strong” and “medium”, both the tests 1 and 2 can reliably detect the surface defect. Even a “weak” surface defect that cannot be determined as a surface defect from the observation of the original image can be detected with high probability in Test 2, and the surface inspection method according to the present invention is excellent. I was able to prove that.
[0044]
【The invention's effect】
As described above, by using a fine checker pattern, by analyzing the degree of change in brightness from a bright part to a dark part or from a dark part to a bright part, a glossy surface such as a film, a glass surface, a painted surface, a resin product, etc. It is possible to detect surface defects.
[0045]
According to the configuration of the present invention, it has become easy to find minute irregularities that have been difficult by visual inspection and publicly known techniques 1, 2 and 3 (see the column of the prior art).
Moreover, even when a curved surface (unevenness distribution) that can be accepted by the subject exists, it was possible to identify the surface without being erroneously detected as a surface defect and to easily find a minute unevenness defect.
Further, unlike the prior art 2, it is not necessary to prepare a specimen having no surface defect for feedback control, and the surface defect can be detected at a low cost.
[Brief description of the drawings]
FIG. 1 is a diagram illustrating the configuration of a surface inspection apparatus according to the present embodiment. FIG. 2 is a diagram illustrating the principle of the present invention. FIG. 3 is a block diagram illustrating the configuration of an image analysis apparatus. FIG. 5 is a diagram showing a first transformed image after Sobel transformation. FIG. 6 is a diagram showing a second transformed image after defect candidate extraction is performed. FIG. FIG. 8 is a diagram illustrating a conversion image of the Sobel transform; FIG. 9 is a diagram illustrating an original image having no surface defect. FIG. 10 is a Sobel transform performed on the original image in FIG. FIG. 11 is a table showing test results of the surface inspection apparatus according to the present invention. FIG. 12 is a diagram showing the prior art.
DESCRIPTION OF SYMBOLS 1 Subject 2 Illumination device 2a Bright part 2b Dark part 3 CCD area camera 4 Image analysis apparatus 5 TV monitor 42 First defect candidate extraction part 43 Second defect extraction part 44 Differentiation processing part 45 Contraction processing part

Claims (8)

Illuminating means for illuminating the subject with a fine checker pattern composed of a repeating pattern of bright and dark parts ,
Imaging means for imaging the subject illuminated with the fine checker pattern;
In the surface inspection apparatus provided with the image analysis means for analyzing the original image imaged by the imaging means,
The image analysis means is configured to detect a surface defect of the subject by extracting and analyzing a distortion of a fine checker pattern in the original image and a halftone image portion between a bright portion and a dark portion. Surface inspection apparatus characterized by being made.   The fine checker pattern is a checker pattern in which a bright part and a dark part are arranged at a dimensional ratio of 1: 1, and the size of each checker pattern depends on the shape and size of a surface defect to be detected. The surface inspection apparatus according to claim 1.   The surface inspection apparatus according to claim 1, wherein the imaging unit is focused on the fine checker pattern of the illumination unit. The image analysis means includes a first defect candidate extraction unit for obtaining a halftone image in which the brightness of each of a bright part having a predetermined luminance or higher and a dark part having a predetermined luminance or lower is 0 from the original image;
A differential processing unit that performs a differential process on the original image to obtain a first converted image, and extracts a pixel having a large gradient in the first converted image, or both the extracted pixel and a pixel in the vicinity thereof A second defect extraction unit that extracts and removes a normal area by setting the luminance of a pixel corresponding to the extracted pixel to 0 in the obtained halftone image is provided. The surface inspection apparatus of any one of Claims.   The image analysis means further includes a micro region processing unit that removes a micro region from the second converted image that has been processed by the first defect candidate extraction unit and the second defect extraction unit. The surface inspection apparatus according to claim 4. Illuminating a subject with a fine checker pattern composed of a repetitive pattern of bright and dark portions, and imaging the subject illuminated with the fine checker pattern;
A surface inspection method including a step of analyzing an original image imaged by the imaging means,
The step of analyzing the image includes a step of extracting and analyzing a checker pattern distortion of the original image and a halftone image part between a bright part and a dark part, and a surface of the subject based on the degree of the distortion and the change. And a step of detecting a defect. The step of analyzing the image includes a first defect candidate extraction step for obtaining a halftone image in which the brightness of each of a bright part having a predetermined luminance or higher and a dark part having a predetermined luminance or lower is 0 from the original image;
A differential processing step for obtaining a first converted image by performing differential processing on the original image, and extracting a pixel having a large gradient of the first converted image, or extracting both the extracted pixel and a pixel in the vicinity thereof The surface inspection according to claim 6, further comprising: a second defect extracting step of removing a normal area by setting a luminance of a pixel corresponding to the extracted pixel to 0 in the obtained halftone image. Method.   The surface inspection method according to claim 7, further comprising a step of removing a minute region after performing the first defect candidate extraction step and the second defect extraction step.

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