JP3201217B2 - Surface defect inspection equipment - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an apparatus for inspecting a surface defect of an object to be inspected, for example, a surface defect such as unevenness of a painted surface of an automobile body.

[0002]

2. Description of the Related Art As a conventional surface defect inspection apparatus, for example, JP-A-2-73139 and JP-A-5-45142 are known.
For example, there is one disclosed in Japanese Patent No. 45144. These show a predetermined light and dark fringe (stripe) pattern on the surface to be inspected, and when there is a defect such as unevenness on the surface to be inspected, a received light image having a difference in brightness (brightness) and a change in brightness (brightness) due to the defect. Is differentiated to detect a defect on the surface of the surface to be inspected. Further, in the above conventional example, there is a defect that is not a defect such as a processed part on the surface to be inspected, and when it is present in a received image, the luminance level at the defect and the luminance level at a non-defect part such as the processed part are determined. There is described a technique for distinguishing a defect from other parts by utilizing the difference between the two.

[0003]

However, the conventional surface defect inspection apparatus as described above has the following problems. For example, in painting automotive bodies,
What is called a surface defect is a projection on the surface of the coating resulting from the application of dust and the like, for example, having a diameter of about 0.5 mm to 2 mm and a thickness of about several tens μm. . The height (thickness) of such a convex part is small even though its diameter is small.
Is relatively large, the light diffuse reflection angle becomes large, and it is easy to see. On the other hand, there are irregularities that do not become defects. That is, since the concentration of the paint is not strictly constant due to the eddy convection generated in the process of evaporating the paint solvent, extremely thin (low) irregularities are periodically generated in the thickness of the coating film. The unevenness is, for example, when the interval between the peaks is 1 to
It is about 10 mm, and the height of the unevenness is about several μm. Such extremely thin irregularities are of such a degree that they are usually not noticed and do not become defects. However, it may look like "yuzu" or "orange" depending on the degree of light, so it is called "yuzu skin" or "orange skin". In the conventional example as described above, since the luminance change is emphasized and detected, extremely thin irregularities that do not become a defect like the above-mentioned “Yuzu skin” are also detected, and this may be erroneously determined as a defect.

For example, in the above-mentioned Japanese Patent Application Laid-Open No. 2-73139, the distance between stripe patterns is small (1.5 mm).
Since the defect detection process is performed using illumination, if the above-mentioned “yuzu skin” is formed on the surface to be inspected, the stripe itself and its boundary (line) are greatly disturbed. The black part becomes an isolated point,
Erroneous detection is likely to occur when the stripe width on the image is not constant. For example, as shown in FIG. 22, a disturbance corresponding to the surface roughness occurs at the boundary between light and dark in the stripe image, and an isolated point as shown in FIG. appear. This part of the range α causes the erroneous detection. Since this increases in proportion to the surface roughness, erroneous detection is more likely to occur as the degree of roughness increases. As described above, in the related art, there is a problem that extremely thin irregularities that do not become defects such as so-called “yuzu skin” due to surface roughness may be erroneously detected as defects.

The present invention has been made to solve the problems of the prior art as described above, and is called "citrus skin".
It is an object of the present invention to provide a surface defect inspection apparatus capable of performing more precise defect detection without being affected by surface roughness as described above.

[0006]

In order to achieve the above object, the present invention is configured as described in the appended claims. FIG. 1 is a diagram corresponding to the claims of the present invention. In FIG. 1, reference numeral 100 denotes a surface to be inspected, for example, a painted surface. Reference numeral 101 denotes an illuminating unit, which reflects a light-dark pattern in which the light-dark change changes continuously or stepwise in at least two or more steps, and the change is periodically repeated at predetermined intervals, For example, it is a lighting device as shown in FIGS.
Reference numeral 102 denotes an imaging unit that images a surface to be inspected and converts the light-receiving image of the light and dark pattern into image data of an electric signal, for example, a video camera such as a CCD camera. Reference numeral 103 denotes a defect detection unit that detects a defect on the surface to be inspected from the received light image obtained by the imaging unit 102. The defect detecting means 103 is constituted by, for example, a computer.
The defect detected by the defect detection unit 103 is displayed on a display unit (not shown) (for example, the monitor 7 in FIG. 2) or input to a subsequent control device (for example, a coating control device) for use.

[0007]

In the present invention, a light-dark pattern in which the light-dark change changes continuously or at least in two or more steps and the change is periodically repeated at predetermined intervals is used as the light-dark pattern projected by the illumination means 101. Used. More specifically, as described in claim 3 , the brightness changes in a sinusoidal manner (for example, the embodiment of FIG. 5a described later), or the brightness changes in a two-step manner as shown in FIG. It is. As described above, when the boundary between light and dark in the light-dark pattern is changed continuously or in a plurality of steps (at least two steps instead of one step from light to dark immediately), as in the related art. The degree of change at the boundary between light and dark becomes gentler compared to a pattern that clearly changes only between light and dark. As described above, the erroneous detection of “Yuzu skin” as a defect is caused by the presence of “Yuzu skin” near the boundary between light and dark, so that the boundary is greatly disturbed. Erroneous detection is likely to occur when the width of the upper stripe is not constant. Therefore, as described above, the boundary between light and dark is changed continuously or stepwise, and the degree of change in light and dark at the boundary is made gentle, so that "yuzu skin" does not cause an isolated point, thereby causing erroneous detection. Can be prevented.

Further, as described in claim 1, with a surface roughness detecting means for detecting the surface roughness of the inspected surface, depending on the surface roughness, so that the higher the brightness gradient is larger roughness smaller If the illuminating means is used, the defect can be detected more precisely. Claims 4 and 2
Shows an example of the configuration of the defect detection means, claim 4 is to extract only a component of a high frequency region of the frequency components in the image data and the level is a predetermined value or more,
That is configured to determine that part is defective,
Claim 2 comprises driving means for driving the received light image obtained by the imaging means to sequentially move on the surface to be inspected in a predetermined direction at a predetermined speed, and a plurality of temporally different images obtained by the imaging means, At least the moving amount of the moving object in the image is detected from the moving image, and based on the detected amount, the moving object under the predetermined moving condition is determined to be a defect.

[0009]

DESCRIPTION OF THE PREFERRED EMBODIMENTS The present invention will be described below with reference to the drawings.
FIG. 2 is a diagram showing one embodiment of the present invention. In FIG. 2, reference numeral 1 denotes an illuminating device, which is arranged so as to irradiate the surface 3 to be inspected with light having a predetermined light and dark pattern (to be described in detail later). Reference numeral 2 denotes a video camera (for example, a CCD camera or the like), which is arranged to capture an image of the surface 3 to be inspected on which a light-dark pattern is projected. Reference numeral 4 denotes a camera control unit, which generates an image signal of a received image picked up by the video camera 2 and outputs the image signal to the image processing device 5. Reference numeral 6 denotes a host computer, which has a function of controlling the image processing apparatus 5 and displaying or outputting processing results to the outside. Reference numeral 7 denotes a monitor which displays a screen captured by the video camera 2 and the like.

FIG. 3 is a block diagram showing the configuration of the image processing apparatus 5. In FIG. 3, an image signal from the camera control unit 4 is converted into a digital value by an A / D converter 9 via a buffer amplifier 8. Further, an MPU (microprocessor) 10 performs a predetermined operation, processing, and the like on the image data. Reference numeral 11 denotes a memory for storing image data and processing results.
It can be output to the monitor 7 via the / A converter 12 and displayed.

Next, FIG. 4 is an exploded perspective view showing the details of the lighting device 1. In the present embodiment, description will be made assuming that illumination of a light and dark stripe pattern is used. In FIG. 4, reference numeral 1a denotes a light source, the light of which is diffused by a diffusion plate 1b and radiated to a surface to be inspected through a stripe plate 1c. The diffusion plate 1b is, for example, a piece of frosted glass, and uniformly irradiates the inspection surface 3 with light. Stripe plate 1
c is a transparent or diffuser plate with black stripes applied at predetermined intervals, but the brightness of the boundary portion changes continuously or in two or more steps as shown in the figure. It does not change in one step from light to dark.

FIGS. 5A and 5B are diagrams showing examples of the above-mentioned stripes, wherein FIG. 5A shows a light-dark change in a sine wave shape, and FIG.
Indicates that the brightness changes in two stages. However, (a)
In the figure shown in FIG. 7, the change in brightness is actually discontinuous, but this is because it is difficult to display a continuous change for the sake of illustration. One that changes in a wave shape is used. In addition, it may be one that changes discontinuously in multiple stages. In order to form a stripe as shown in FIG. 5A, a stripe pattern is sprayed on the stripe plate 1c with a black spray or the like, or an original of a continuously changing stripe pattern is created by a personal computer or the like and printed. Can be created in the following way. Also, as shown in FIG. 5B, a filter which changes in two stages can be created by attaching a filter having a transmittance of, for example, 50% to the boundary of the black stripe. In the case of further changing the number of stages, a plurality of filters having different transmittances may be sequentially arranged and bonded.

FIG. 6 is a sectional view showing an embodiment of an illuminating device which does not use the stripe plate 1c. In this embodiment, a plurality of rotators 1d having a non-circular cross section are provided in front of an intermediate portion of the plurality of light sources 1a, and by rotating the rotator 1d, a boundary portion between light and dark stripes is blurred. . The rotation speed of the rotating body 1d is
The speed is sufficiently higher than the shutter speed of the video camera 2. The installation position of the diffusion plate 1b may be an intermediate position between the light source 1a and the rotating body 1d. By using the illuminating device as shown in FIGS. 5 and 6, the boundary between light and dark does not change from light to dark at once, but stripes having a blurred boundary with a gradual change in brightness are inspected. Can be made on the surface.

Next, the principle of using a stripe illumination as described above to project a defect on the surface of an object to be inspected in a received light image will be described with reference to FIGS. In addition, FIG.
In FIG. 9, for convenience of explanation, the stripes are shown in which the brightness changes in one step. In FIG. 7, the inspection surface 3
It is assumed that there is a convex defect 13 at the top. Point P1 is any point on the normal surface without defects, and point P2 is
3 is an arbitrary point. When the defect 13 is convex as shown in the figure, each of the arbitrary points on the defect 13 has an angle between a tangent line of the point and a normal surface to be inspected. I do. In this case, it is assumed that the vicinity of the defect 13 and the point P1 is within a bright stripe (a portion irradiated with light) of the stripe illumination.

Assuming that the angle between the video camera 2 and the surface 3 to be inspected is an incident angle θi, light is specularly reflected (θi) at a normal plane point P 1, and in that direction, a bright portion of the stripe illumination is provided. Point P1 is bright (white) with video camera 2.
Projected as part. However, the point P2 on the defect 13
, The incident light from the video camera 2 is not specularly reflected but irregularly reflected due to the inclination angle θ, and there is a black stripe in that direction, so that the point P2 is projected black by the video camera 2. Assuming that the irregular reflection angle at this time is θh, the inclination angle θ at the defect point P2 and the angle (incident angle) in a certain direction of the video camera 2 are θi, so that θh = θi + 2θ (Equation 1) (FIG. 8) reference). That is, in the direction of the angle θh, a bright (white) portion of the stripe is projected white, and a black portion of the stripe is projected black (dark). Also,
As described above, since the light is specularly reflected on the normal plane, it can be expressed as θh = θi (Equation 2). Therefore, from the formulas (1) and (2), the irregular reflection due to the defect can be expressed as 2θ based on the regular reflection direction θi. In the above description, the video camera 2 is used as a starting point, but the same applies to the case where the lighting device 1 is used as a starting point. FIG. 8 is an example of the case where the point P2 is on the right slope of the defect. However, the concept is the same even when P2 is on the left slope, and a description thereof will be omitted.

Next, FIG. 9 shows a case where the surface to be inspected is a curved surface.
This will be described with reference to FIG. As shown in FIG. 9, the surface 3 to be inspected is a curved surface having a radius of curvature R, and its center is C. An arbitrary point on this curved surface is defined as P1 ', and the central angle of the point is θc
And Here, the reflection angle θh at the point P1 ′ on the curved surface is represented by θh = θi + 2θc (Equation 3), and this is the regular reflection angle on the curved surface. Further, when there is a defect at the point P1 ', the reflection angle θh is given by θh = θi + 2θc + 2θ (Equation 4), as in the case of the plane. Therefore, the irregular reflection direction at the defect is 2θ regardless of the plane or the curved surface, based on the regular reflection direction at that point.
Becomes

As described above, since the reflection angle on the surface to be inspected is obtained from the inclination angle θ at the defect, the distribution of the inclination angle θ at the defect is measured in advance, and it is desired to detect the distribution.
In other words, if the area to be projected in black in the image (to match the minimum rank of the size to be determined as a defect according to the type of the inspection object) is determined, the dark (black) of the stripe in the reflection direction in that area is determined. ), The distance between the stripes (T in FIG. 4) and the distance between the illumination device 1 and the inspection surface 3 (D in FIG. 7; including the angle between the illumination device and the inspection surface) can be determined. At this time, a representative defect is sampled according to the type of the object to be inspected, and is set so as to match the sample.

Next, a first embodiment of the defect detection processing will be described. If a light and dark stripe as shown in FIG. 5A is projected on the surface 3 to be inspected and a defect 13 is present in the stripe, the received light image is as shown in FIG.
In FIG. 10, the defect 13 is black in the bright stripe,
White appears in the dark stripe, half at the border, and black at the rest. In the received light image of FIG. 10A, when the coordinate axes x and y are set with the origin at the upper left of the screen, the luminance level of the defect 13 in the x direction is as shown in FIG. In FIG. 10B, the large irregularities change in a sine wave shape corresponding to the bright and dark portions of the stripe. Fine irregularities are noise. The defect appears as a narrow concave portion or a fine convex portion larger than the noise. The image processing device 5 takes in a received light image as shown in FIG. 10 as an original image signal S0. For example, the image signal from the video camera 2 is A / D converted,
When the luminance level is converted into an 8-bit digital value, as shown in FIG. 10B, 255 is the maximum value on the vertical axis, bright and 0 is black, and one screen is, for example, 512 × 4.
When the image is captured at a resolution of 80 pixels, the horizontal axis is 0 to 5
The number of pixels is 12.

It should be noted that the signal S0 shown is a signal for a certain value in the y-axis direction (the value indicated by the broken line), and such a signal exists for each value on the y-axis.

FIG. 11 is a signal waveform diagram showing each component included in the original image signal S0. The original image signal S0 shown in FIG. 11A includes shading components (low frequency components) due to uneven illuminance shown in FIG. 11B, luminance change components (intermediate frequency components) of stripe illumination shown in FIG. ), And a luminance change component (high-frequency component) due to the defect 13. Therefore, only the signal corresponding to the defect 13 can be detected by detecting the high-frequency component and removing the low-level noise component.

Hereinafter, the above-described defect detection processing will be described in detail with reference to FIG. First, only high frequency components are extracted from the original image signal S0. Specifically, for example, the original image signal S
It can be extracted by performing differential processing on 0. That is, since the differentiation processing is a kind of a bypass filter, if the signal S0 is differentiated and its absolute value is obtained, a signal in which only the high-frequency components are emphasized as shown in S1 of FIG. 12 is obtained. The same applies to the case where the signal S0 is passed through an appropriate bypass filter.

Next, by binarizing the high frequency component signal S1 with an appropriate threshold value Th, a signal corresponding to a defect such as S2 is obtained. Based on the defect image signal S2 thus obtained, labeling (labeling) of the defect portion, calculation of the area of the defect, and calculation of the center of gravity of the defect portion are performed. Then, the host computer 6 specifies the defect, positions and ranks the defect based on the above result, and outputs it to a display device or a subsequent device (for example, a coating control device). As described above, in this embodiment,
Since the change in brightness of the illumination light has a gentle gradient and there is no clear boundary (edge), even if the surface to be inspected has a large surface roughness such as "yuzu skin", an isolated point exists at the boundary. Therefore, there is no possibility of erroneously detecting “yuzu skin” or the like as a defect.

Next, a second embodiment of the defect detection processing will be described. In this embodiment, a moving device (for example, an inspection line for sequentially moving an object to be inspected by a belt conveyor or the like) for moving a relative position between the illumination device 1 and the video camera 2 and the surface to be inspected 3 at a predetermined speed in a predetermined direction is used. Provided to detect a defect from a moving image.

Assuming that a light and dark stripe is projected on the surface 3 to be inspected and that a convex or concave defect 13 exists in the bright portion of the stripe, the received light image is as shown in FIG. Due to the principle, the defect 13 appears black in the bright stripe. Further, as shown in FIG. 14A, when the object to be measured (the surface 3 to be inspected) moves in the direction of the arrow (the x-axis direction in the received light image) with the illumination device 1 and the video camera 2 fixed, The defect 13 moves to the position 13 '. Therefore, as shown in FIG. 14B, the received light image is an image in which only the defect 13 moves in the x-axis direction while the bright and dark stripes remain stationary. Therefore, a defect can be accurately detected by processing a plurality of continuous light-receiving images and detecting a moving object therein.
The object to be measured (the surface to be inspected 3) is fixed, and the illumination device 1 is fixed.
The same applies when the video camera 2 is moved, but it is generally easier to move the measured object in terms of structure. However, when the object to be measured is difficult to move with a large-sized device, the lighting device 1 and the video camera 2 may be configured to be moved as a set.

Next, a process for detecting a defect from a moving object on an image will be described. Note that the example described here is an example in which the measured object moves at a constant speed in the x-axis direction. FIG. 15 shows two consecutive frames, ie, the time point f _t.
Moving object from the difference image between the image in the image and the time point f _{t + 1} in, that is a diagram illustrating a method for detecting defects, (a) shows the image at time f _t, (b) the time f _{t + 1}
, And (c) shows the difference image between them. In the above _ft image and _{ft + 1} image, the defect 13 hardly moves in the y-axis direction, but moves only in the x-axis direction. The moving amount and the moving direction thereof, is known from the moving direction and the moving amount of the object to be measured, and a constant sampling intervals of Δt between f _t image and f _{t + 1} images. Therefore, the theoretical movement amount ΔD of the defect 13 in the received image can be easily obtained. Therefore, the moving amount Δd of the moving object obtained from the difference image of FIG. 15C is compared with the theoretical moving amount ΔD. You can judge. The above-mentioned predetermined range (error range)
Is an error range due to a change in moving speed, a change in image processing, and the like, and may be appropriately set according to the moving accuracy of the inspection line. In addition to the comparison above,
The inspection accuracy can be further improved by taking the moving direction into consideration.

FIG. 16 is a flowchart showing the sequence of the above-described defect detection method. FIG. 17 is a diagram showing a change in the luminance signal at a certain y value (corresponding to a portion where a defect exists) in the received light image. However, in this embodiment,
This is an example in which the lighting device shown in FIG. 5B in which the brightness changes in two stages is used. Hereinafter, the flow of FIG. 16 will be described with reference to FIG. In FIG.
In step S1, reads the image at time f _t.
As shown in FIG. 17A, the luminance signal in this case has a high level in a bright portion of the stripe and a low level in a dark portion, and
Defective parts are also indicated by recesses in the high level. In step S2, the image at the time point _{ft + 1} is read. In this case, as shown in FIG. 17B, the light and dark positions of the stripe do not change, and only the defect position moves. Next, in step S3,
The difference image | f _{t + 1} −f _t | between the two images is calculated. As described above, since the bright and dark positions of the stripe do not change, only the defective portion remains in the difference image, as shown in FIG. 17C. If a slight shift occurs between the light and dark positions of the stripes of both images at the signal processing stage, a thin noise remains at that part as shown in the figure. It can be easily removed by the process of S9.

Next, in step S4, the difference image is binarized at a predetermined threshold value Th. The binarized waveform is as shown in FIG. 17D, and the x and y coordinates of the position expected to be a defect are obtained. Next, in step S5, a movement amount Δy (= y _t −y _{t + 1} ) of the center of gravity position in the y-axis direction is obtained. Next, in step S7, it is determined whether or not the difference between the movement amount Δy and the theoretical movement amount ΔDy in the _y- axis direction is equal to or smaller than a predetermined value. In this case, since the measured object moves only in the x-axis direction, the theoretical movement amount ΔD _y ≒ 0. If “NO” in the step S7, that is, the theoretical movement amount Δ
A large difference signal between D _y is not a defect, to determine the data one after another, go to only step S8 for those of "YES". In step S8, the amount of movement Δx (= x _t −x _{t + 1} ) of the position of the center of gravity in the x-axis direction is obtained.

Next, in step S9, the movement amount Δx
It is determined whether or not the difference between the distance and the theoretical movement amount ΔD _{x in} the x-axis direction is equal to or smaller than a predetermined value. In this case, the amount of movement of the object to be measured becomes the stoichiometric amount of movement [Delta] D _x. “NO” in step S9
In other words, a signal having a large difference from the theoretical movement amount ΔD _x is not a defect, and data is determined one after another.
The process goes to step S10 only for those of "ES". Note that the thin noise at the boundary between light and dark in FIG.
Since the movement amount Δx ≒ 0, the difference from the theoretical movement amount ΔD _x is large, and therefore, the difference is removed by the processing in this step. Next, in step S10, the portion where the difference between the movement amount and the theoretical movement amount is equal to or smaller than a predetermined value in both the y-axis direction and the x-axis direction is stored as defect portion data. As described above,
In the present embodiment, since a defect is detected by processing a moving image and detecting a moving object, erroneous detection due to noise and omission of defect detection occur due to the position and movement of the defect. There is no fear.

Next, another embodiment of the processing for detecting a defect will be described. In this embodiment, a motion vector (optical flow) of each frame is obtained from a moving image, and thereby a defect is detected. A certain time t in the video
Is the brightness at frame coordinates (x, y) at f
(x, y, t). Then, assuming that the brightness at the point (x, y) after the time δt for moving by δ _x and δ _{y in the} x direction and the y direction does not change, the following equation (5) holds. f (x, y, t) = f (x + δ x, y + δ y, t + δt) ... ( 5) points to the equation (5) (x, y, t) when Taylor expansion for the following equation (6) Is obtained.

[0030]

(Equation 6)

Here, ignoring the second-order and higher-order terms, the following equation (7) is obtained from the equations (5) and (6).

[0032]

(Equation 7)

When the object to be measured is moved in the x-axis direction, it hardly moves in the y-axis direction. Therefore, if the y term is ignored, the following equation (8) is obtained.

[0034]

(Equation 8)

The following equation (9) is obtained from the above equation (8).

[0036]

(Equation 9)

In the equation (9), ∂f / ∂x: change in brightness in the x direction at the point (x, y) = differentiation of the image f in the x direction ∂f / ∂t: at the point (x, y) Temporal change of brightness = difference between both frames dx / dt: x component of motion vector at point (x, y) Therefore, a motion vector at each point is obtained from the differential image and the difference image. The moving direction of the defect is known (determined by the moving direction of the object to be measured).
Since the moving speed of the (measured object) is also substantially constant and known, a defect can be detected from the obtained moving vector of the received light image.

FIG. 18 is a flowchart showing the processing sequence when detecting a defect by the above method. FIG.
In 8, first, in step S20, it fetches the original image f _t at time t, in step S21, the time t + 1
The original image _{ft + 1 in} is acquired. Next, step S2
In 2, x-direction differential value of the original image f _t a (∂f / ∂x) determined, in step S23, obtains a difference (∂f / ∂t) of the original image f _{t + 1} and the original image f _t . Next, step S24
Then, from the results of S22 and S23, the motion vector (dx
/ Dt) is determined by the above (Equation 9). Next, in step S25, of the motion vectors obtained in step S24, only points having a movement amount (corresponding to movement of the measured object) within a predetermined range in a predetermined movement direction are detected as defects. Next, in step S26, labeling (numbering), area calculation, centroid calculation, and the like of the detected defect are performed.

If there are processing holes or irregularities provided as a design on the inspection surface of the object to be measured, they also move together in the received light image, so that they are erroneously detected as defects. There is a risk. However, while the size of a defect is generally about several mm or less in diameter,
Since the area of the processing hole or the unevenness provided as a design is considerably large, and the shape is predetermined and constant, it can be distinguished from a true defect by utilizing its properties. For example, in the flowchart of FIG. 18, a step may be provided in which only those having a defect area equal to or smaller than a predetermined value determined in step S26 are determined to be true defects.
This process is the same in the flowchart of FIG. As described above, the three types of the defect detection processing have been described. However, the present invention basically has a feature in the illumination device, and the defect detection method uses another method other than the above. You may.

Next, another embodiment of the present invention will be described. In this embodiment, the surface roughness of the inspection surface 3 is detected, and the brightness gradient of the illumination device is changed accordingly. That is, the larger the surface roughness of the surface to be inspected, the greater the influence on the defect detection, and the more likely the erroneous detection will occur. Therefore, the higher the surface roughness, the smaller the brightness gradient of the illumination device, and the more precise defect detection can be performed by blurring the boundary between light and dark.

FIGS. 19 and 20 are cross-sectional views of an embodiment of a lighting device capable of changing the brightness gradient. First, in FIG. 19, a pair of filters 15 and 15 'having different sizes are provided between the light source 1a and the stripe plate 1c in which only the black stripe is provided on the diffusion plate. These filters 15, 15 'can be moved in the direction shown by the arrow in the figure (the direction perpendicular to the stripe plate 1c) by an illumination control device (not shown).
As the filters 15 and 15 'are farther from the diffusion plate 1c, the contrast at the end of the black stripe decreases and the boundary between light and dark is blurred. In the above description, the stripe plate 1c is a diffusion plate. However, if a diffusion plate (not shown: for example, the diffusion plate 1b in FIG. 4) is provided outside the stripe plate 1c, the boundary between light and dark can be further blurred. Can be done.

In FIG. 20, filters 16 and 16 'are provided between the light source 1a and the stripe plate 1c in which only the black stripe is provided on the diffusion plate. These filters 16, 16 'are each divided into two,
It can be moved in the direction of the arrow in the figure (the direction parallel to the stripe plate 1c). These filters 16, 1
The farther 6 ′ is from the center of the black stripe (indicated by the dashed line in the figure), the lower the contrast of the end of the black stripe is, and the brighter and darker the boundary becomes. In this case, if a diffusion plate is provided outside the stripe plate 1c, the boundary between light and dark can be further blurred. As described above, in the illuminating devices shown in FIGS. 19 and 20, the contrast at the stripe end can be reduced by changing the position of the filter. Can be changed.

Next, a method for detecting the surface roughness will be described. FIGS. 21A and 21B are diagrams showing the relationship between the surface roughness and the power spectrum of the received light image. FIG. 21A shows the case where the surface roughness is small, and FIG. 21B shows the case where the surface roughness is large. As shown in the figure, the boundary of the stripe is disturbed in FIG. In a light-receiving image on which a stripe pattern is projected, the larger the roughness of the surface to be inspected, the more the stripe pattern is disturbed. Therefore, the surface roughness can also be detected using the light reception image of the video camera 2 for detecting a surface defect. The amount of disturbance of the stripe pattern can be detected, for example, by obtaining a power spectrum by performing a fast Fourier transform (FFT) on a received image of the video camera 2 and obtaining an area of a second peak in the power spectrum. That is, in the power spectrum characteristics shown in FIG. 21, the large peak at the left end corresponds to the bright and dark stripe pattern, and the area of the next peak (long wavelength region) corresponds to the amount of disturbance of the stripe, that is, the surface roughness. are doing. Therefore, the surface roughness can be detected by obtaining the area of the hatched portion in the power spectrum characteristics of FIG. According to the result, the filters 15, 1 in FIG.
By moving the filter 5 'or the filter 16, 16' in FIG. 20, a light-dark gradient adapted to the surface roughness can be realized.

[0044]

As described above, according to the present invention, there is provided an illuminating means for a light-dark pattern in which the brightness changes continuously or at least in two or more steps and the change is periodically repeated at predetermined intervals. By using this, the degree of change at the boundary between light and dark becomes more gradual compared to a pattern that clearly changes only to light and dark as in the past. This leads to an effect that erroneous detection can be prevented. Further, in the case of using an illumination unit that includes a surface roughness detection unit that detects a surface roughness of a surface to be inspected, and that controls the brightness to be smaller as the roughness is larger according to the surface roughness, The effect that more precise defect detection can be performed is obtained.

[Brief description of the drawings]

FIG. 1 is a functional block diagram of the present invention.

FIG. 2 is a diagram showing a first embodiment of the present invention.

FIG. 3 is a block diagram of an example of an image processing apparatus 5 in the embodiment of FIG.

FIG. 4 is an exploded perspective view of an example of the illumination device 1 in the embodiment of FIG.

FIG. 5 is a diagram showing an example of a light-dark pattern and a light-dark level in the lighting device 1.

FIG. 6 is a sectional view of another embodiment of the illumination device 1.

FIG. 7 is a view showing the principle of displaying a defect on the surface of an inspection object in a received light image.

FIG. 8 is a view showing the principle of displaying a defect on the surface of an inspection object in a received light image.

FIG. 9 is a diagram for explaining a case where the surface to be inspected is a curved surface.

FIG. 10 is a diagram showing light and dark stripes and defects in a received light image.

FIG. 11 is a characteristic diagram showing each frequency component in a received light image.

FIG. 12 is a diagram showing signal waveforms in the first example of the defect detection processing.

FIG. 13 is a view showing light and dark stripes and defects in a received light image.

FIG. 14 is a view showing the movement of a defect when the surface to be inspected moves.

[15] moving object from the difference image between the image in the image and the time point f _{t + 1} at time f _t, ie illustrates a method of detecting defects.

FIG. 16 is a flowchart showing a calculation process in the first embodiment of the defect detection process.

FIG. 17 is a diagram showing a luminance signal waveform in the processing of FIG. 16;

FIG. 18 is a flowchart illustrating a calculation process in a second embodiment of the defect detection process.

FIG. 19 is a sectional view of a second embodiment of the lighting device.

FIG. 20 is a sectional view of a third embodiment of the lighting device.

FIG. 21 is a diagram showing a relationship between surface roughness and a power spectrum of a received light image.

FIG. 22 is a view showing surface roughness and disturbance of a stripe image.

[Explanation of symbols]

DESCRIPTION OF SYMBOLS 1 ... Illumination device 7 ... Monitor 1a ... Light source 8 ... Buffer amplifier 1b ... Diffusion plate 9 ... A / D converter 1c ... Stripe plate 10 ... MPU 1d ... Background 11 ... Memory 2 ... Video camera 12 ... D / A converter 3 … Inspection surface 13… defect 4… camera control unit 15, 15 ′…
Filter 5 ... Image processing device 16, 16 '...
Filter 6: Host computer

──────────────────────────────────────────────────続 き Continuation of the front page (56) References JP-A-4-231853 (JP, A) JP-A-63-108249 (JP, A) (58) Fields investigated (Int. Cl. ⁷ , DB name) G01B 11/00-11/30 102 G01N 21/84-21/958

Claims (4)

(57) [Claims] 1. A method of irradiating light to a surface to be inspected, and from the surface to be inspected.
A received light image is created based on the reflected light of
Defect Inspection System for Detecting Defects on Inspection Surface Based on
In which, the light-dark change is continuous or at least two steps or more
And the change is repeated periodically at predetermined intervals.
An illumination means for projecting a light and dark pattern on the surface to be inspected; and a light receiving image obtained by imaging the surface to be inspected as an electric signal.
Imaging means for converting the image data into image data; and
Defect detection means for detecting defects on the inspection surface;Surface roughness detection means for detecting the surface roughness of the surface to be inspected, And the illumination means responds to the detected surface roughness.
In contrast, the greater the roughness, the smaller the light / dark gradient.
The method according to claim 1, wherein
 Surface defect inspection equipment. 2. The image forming apparatus according to claim 1, further comprising a driving unit that drives the received light image obtained by the imaging unit to sequentially move on the surface to be inspected in a predetermined direction at a predetermined speed. The defect detection unit is obtained by the imaging unit. A plurality of temporally different images, that is, at least the moving amount of the moving object in the image is detected from the moving image, and based on the detected moving object, the moving object with the predetermined moving condition is determined to be a defect, The surface defect inspection apparatus according to claim 1, wherein Wherein said illuminating means is to project a stripe pattern brightness varies sinusoidally in the inspected surface, that the surface defect inspection apparatus according to claim 1 or claim 2, characterized in. 4. The defect detecting means extracts only a component having a level higher than a predetermined value in a high frequency region among frequency components in the image data, and judges the portion as a defect. The surface defect inspection apparatus according to claim 1 or claim 3, wherein: