JPH0438453A - Apparatus for inspecting surface state - Google Patents

Abstract

PURPOSE:To enhance inspection accuracy by further comparing an adaptation coefficient corresponding to the difference between the density histogram of an object to be inspected and theoretical frequency with a threshold value when the standard deviation based on the density histogram of the pixel of an object to be inspected is judged to be abnormal. CONSTITUTION:A television camera 10 takes the image of the surface of a foamed sheet and transmits a multilevel image sampled in a pixel unit to an inspection apparatus 20. A density histogram is calculated with respect to pixel data and the standard deviation sigma is compared with a preset threshold value alphadev to inspect the surface state of an object to be inspected. In the case of sigma>alphadev, the average value mu of the density histogram and the theoretical fre quency of each density according to regular distribution having standard devia tion sigma are calculated. The adaptation coefficient F corresponding to the differ ence between the density histogram and the density frequency is calculated and compared with a preset threshold value alphafit and the surface state is judged to be normal at the time of F <= alphafit and abnormal at the time of F > alphafit to output a result.

Description

DETAILED DESCRIPTION OF THE INVENTION [Field of Industrial Application] The present invention relates to a surface condition inspection device for inspecting surface conditions such as scratches, dust, roughness, color unevenness (shading of color tone), etc. existing on a surface.

[Prior Art] Conventionally, as a surface flaw inspection device, image data is written into a binary image memory and a gray memory, a mask pattern serving as an inspection area is created from the binary image, and an average value in the clay memory corresponding to the pattern is written. There is a method for detecting by calculating the value and standard deviation (Japanese Patent Laid-Open No. 124783/1983).

[Problem to be solved by the invention] However, the above-mentioned conventional surface scratch inspection device has the following problems.
There are ~■ problems.

■Performance is insufficient when it is desired to detect subtle abnormalities on the surface of the object to be inspected.

■It is difficult to set accurate thresholds that will be used as judgment criteria during capital inspections.

■Unable to properly binarize or detect defects such as roughness.

An object of the present invention is to reliably inspect surface conditions including roughness and the like with high precision.

[Means for Solving the Problems] The present invention provides an imaging device that images the surface of an object to be inspected, a verification device that verifies the surface state of the object to be inspected based on the imaging result of the imaging device, and a verification device for the verification device. A surface condition inspection device configured to include an output device that outputs a result, the verification device determines a density histogram n (K) for image data captured by the imaging device, and calculates the density histogram n (K) for image data captured by the imaging device.
By calculating the average value μ and standard deviation σ based on (K), and comparing the standard deviation σ with the threshold value αdev set in advance corresponding to the surface condition to be tested this time,
When the surface condition of the object to be inspected is tested and the test result determines that the surface condition is abnormal, the above average value μ, standard deviation σ
Find the theoretical frequency g (K) of each concentration that follows a normal distribution with
Find the compatibility coefficient F corresponding to the difference between
The surface state of the object to be inspected is tested by comparing the value αfit with a threshold value αfit set in advance corresponding to the surface state to be tested this time.

[Effect] According to the present invention, there are the following effects (1) to (2).

■The surface condition can be detected using a versatile imaging device such as a television camera, and the device configuration can be made compact.

Since the surface condition is detected based on the concentration distribution state of the surface, surface conditions including roughness can be detected with a sense similar to that of humans.

■When abnormalities are tested by comparing the standard deviation σ based on the concentration histogram n (K) with the threshold value αdev, there is also a conformity coefficient corresponding to the difference between the concentration histogram n (K) and the theoretical frequency g (K). F is the threshold value α21. Since the test is performed in detail by comparing the test object with the test object, it is possible to reliably separate whether the test object is good or bad and improve the test accuracy.

[Example] Fig. 1 is a block diagram showing an example of the inspection device of the present invention, Fig. 2 is a schematic diagram showing image data, Fig. 3 is a flowchart showing the inspection procedure according to the present invention, and Fig. 4 shows standard deviation. FIG. 5 is a schematic diagram showing the test results using the compatibility coefficient.

The surface condition inspection device 1 includes a television camera 10 (imaging device)
, a verification device 20, and an output device 3o, and inspects the surface of an object to be inspected, such as a thermoplastic foam sheet, for abnormalities.

The basic operations of the surface condition inspection device 1 are as follows (1) to (4)
It is.

(1) The surface of the foam sheet is imaged by the television camera 10.

The television camera 10 transfers a multivalued image sampled pixel by pixel to the verification device 20.

(2) The verification device 20 quantizes the image data of the television camera 10 with an A/D converter 21, for example, at 8 bits (256 gradations), creates a digital image of MXN pixels, and inputs this into the image memory 22. do.

(3) Based on the image input to the image memory 22, the testing device 20 uses the CPU 23 to test whether there is any abnormality on the surface.

(4) The output device 30 displays the test results of the test device 2o and generates an alarm if necessary.

Note that as the imaging device (10), a line sensor having M spatial resolution may be used instead of the television camera. In this case, the line sensor and the object to be inspected are moved relative to each other, N groups of data are stored in the image memory.

The verification device 20 does not necessarily use the image memory 22, and the
The output data of the /D converter 21 may be input directly to the CPU 23.

However, the verification operation by the verification device 2o is as follows.
This is done as follows (see Figure 3).

(2) Obtain a density histogram n (k) for the image data of MXN pixels (k: density value, n: frequency).

When creating this density histogram n (k), depending on the size of the abnormal part predicted in advance in the test object or the sampling density by the television camera 10, it may not be necessary to use all MXN pixels input to the verification device 20. , mXn (nsM, n≦N) pixels (see Figure 2 (A)), or, for example, N or even pixels (Figure 2 (B)).
) may also be used as part of the MXN pixels.

■ Average each density value next to each other to smooth the histogram. For example, replace the frequency n' (K) in the density value with 0.

(2) Based on the averaged density histogram n' (K), find its average value μ and standard deviation σ.

Σ-n' (K) Σn' (K) ...(2)
Σn' (K) ”・(
3) ■Verify the surface condition of the object to be inspected by comparing the standard deviation σ of the above ■ with the threshold value αdev set in advance corresponding to the surface condition to be tested this time (
(See Figure 4).

At this time, if σ≦adav, it is determined that there is no abnormality and the inspection is terminated.

On the other hand, if σ>Qdev, the following process is performed for more detailed verification.

■Mean value μ, standard deviation σ of concentration histogram n'
The theoretical frequency g (K) of each concentration is calculated according to the normal distribution N (μ, σ).

■Concentration histogram n' (K) and theoretical frequency g (K
) is calculated using the following formula (4) or (5).
) is calculated using the formula. However, this compatibility coefficient F is g (K
)≠0, and in equations (4) and (5), if β-bit quantization is performed, L=2β-1.

g (K) ... (4) g (K) ... (5)■
Compare the fitness coefficient F with the threshold value αfit set in advance corresponding to the surface condition to be tested this time, and find that F≦αfit No abnormality F>αfit Abnormality exists...(6)
The result is output (see Figure 5).

At this time, one threshold data α (α6°9 or αfit) may exist depending on the grade classification (normal/abnormal) of the same type of surface condition; /impossible), there may be two.

In addition, there are the same number of threshold values α (Qdev or αtst) as there are types of abnormalities (P), so if α, ≦α, ≦...≦α, ... (7), then the currently detected abnormality It is sufficient to set α = α + (i = 1..., P) ... (8) according to It is sufficient to adopt the smallest α5 among the various αes.

In addition, FIG. 4 shows the test results using the standard deviation σ of the above (■), and the sample number 6.
8.9 and all defective products are subjected to the next stage of processing (O indicates non-defective products, . indicates defective products).

Moreover, FIG. 5 shows the test results using the above-mentioned conformity coefficient F, and the threshold value αfit reliably separates non-defective products from defective products (◯ indicates non-defective products, . indicates defective products).

Next, the operation of the above embodiment will be explained.

- Surface conditions can be detected using a versatile imaging device such as the television camera 10, and the device configuration can be made compact.

Further, the processing contents are simple, the surface condition can be verified in a short time, and the inspection can be completed even on the conveyance line of the object to be inspected.

■Since the surface condition is detected based on the surface concentration distribution, it is possible to detect surface conditions, including roughness, with a sense similar to that of humans.

■Standard deviation σ based on concentration histogram n' (K)
is compared with the threshold value αdsv to test for abnormality, and then the concentration histogram n' (K) and the theoretical frequency g (K
) is compared with the threshold value αfit for detailed verification, so that it is possible to reliably separate whether the object to be inspected is good or bad and improve the verification accuracy.

■In addition to evaluating the surface condition in two grades (normal/abnormal, etc.) using one threshold value α (α.9 or αf1t),
For example, two threshold values α (α, □ or αfit) can be used to evaluate the surface condition in three grades (good/fair/poor, etc.).

(2) Various surface conditions can be detected by preparing the same number of threshold values α (αday or αfit) as the number of types of surface conditions (the number CP) of surface conditions (surface roughness, color unevenness, etc.).

Note that the present invention can be widely used not only for inspecting surface abnormalities but also for classifying surface conditions.

[Effects of the Invention] As described above, according to the present invention, surface conditions including roughness can be reliably inspected with high precision.

[Brief explanation of drawings]

Fig. 1 is a block diagram showing an example of the inspection device of the present invention, Fig. 2 is a schematic diagram showing image data, Fig. 3 is a flowchart showing the inspection procedure according to the invention, and Fig. 4 shows the test results based on standard deviation. The schematic diagram shown in FIG. 5 is a schematic diagram showing the test results based on the conformity coefficient. 10... Imaging device, 20... Verification device, 30... Output device. Patent applicant Sekisui Chemical Co., Ltd. Representative Hiroshi 1) Kaoru Figure 3 Figure 4 Figure 5 Sample number

Claims (1)

[Claims] (1) It has an imaging device that images the surface of the object to be inspected, a test device that tests the surface condition of the test object based on the imaging results of the imaging device, and an output device that outputs the test results of the test device. The verification device calculates a density histogram n(K) for the image data captured by the imaging device, and calculates the average value μ and standard deviation σ based on the density histogram n(K). The surface condition of the object to be inspected is verified by comparing the standard deviation σ with the threshold value α_d_e_v set in advance corresponding to the surface state to be tested this time. When determining an abnormality, further determine the theoretical frequency g (K) of each concentration that follows a normal distribution with the above average value μ and standard deviation σ,
The above density histogram n(K) and the above theoretical frequency g(K)
The surface condition of the object to be inspected can be determined by determining the conformity coefficient F corresponding to the difference between A surface condition inspection device that performs inspection.