Coiled metal strip edge abnormality detection device, method, and program

Abstract

To provide a coil-shaped metal band edge abnormality detection device, coil-shaped metal band edge abnormality detection method and coil-shaped metal band edge abnormality detection program which can reduce erroneous detection when inspecting an edge portion in a coil-shaped metal band.SOLUTION: A coil-shaped metal band edge abnormality detection device S according to the present invention comprises: an image acquisition unit 1 which acquires an image obtained by imaging an edge portion of a metal band wound in a coil shape from the axial direction; a smoothing processing unit 22 which generates a smoothed image by performing the smoothing processing on the image acquired by the image acquisition unit 1; and an abnormality determination unit 23 which determines the presence / absence of an abnormality in the edge portion on the basis of the smoothed image generated by the smoothing processing unit 22.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a coiled metal strip edge abnormality detection device, a coiled metal strip edge abnormality detection method, and a coiled metal strip edge abnormality detection program for detecting the presence or absence of abnormalities in the edge portion of a metal strip wound in a coil shape. [Background technology]

[0002] For example, metal strips such as steel strips are generally shipped in a coiled state after undergoing a quality inspection of the surface, etc. Techniques related to this quality inspection are disclosed in, for example, Patent Document 1 and Patent Document 2.

[0003] The metal strip edge imaging device disclosed in Patent Document 1 is an apparatus that includes an imaging means for imaging the cut surface of the edge of a metal strip, and performs quality control of the edge based on the image captured by the imaging means.

[0004] The side imaging method for coiled steel sheet disclosed in Patent Document 2 is a method for irradiating the side of a coiled steel strip with light from an illumination device, imaging the illuminated side of the steel strip with an imaging device, displaying the image on a display device, and inspecting for defects and / or shape defects that occur at the widthwise end of the steel strip based on the displayed image. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Laid-Open No. 2015-219181 [Patent Document 2] Japanese Patent Application Laid-Open No. 2010-210388 Summary of the Invention [Problem to be solved by the invention]

[0006] However, the metal strip edge imaging device disclosed in Patent Document 1 requires support rolls to suppress fluctuations in the thickness direction, measurement means to acquire position data in the width direction of the metal strip, and means to change the position as an imaging means based on that data, making it a large-scale device that cannot be installed unless there is ample space in the installation location.In addition, there is a problem that it is difficult to follow position fluctuations when the metal strip meanders, etc., when the metal strip is transported at a high speed.

[0007] In Patent Document 2, the presence or absence of defects and abnormalities is determined by an operator by observing an image displayed on a display device. The purpose of Patent Document 2 is to be able to capture a good image of the side surface of a coiled steel sheet. For these reasons, Patent Document 2 does not describe or suggest a method for detecting the edge portion of a coiled metal strip.

[0008] Furthermore, commonly known methods for automatically detecting defects in metal strips based on the brightness and darkness of an image can detect irregularities in the axial direction of the coiled metal strip or gaps between the metal strips due to irregular winding of the coiled metal strip, making it difficult to distinguish these irregularities and gaps from abnormalities such as cracks that have occurred in the edge portions.

[0009] The present invention has been made in consideration of the above-mentioned circumstances, and its object is to provide a coiled metal strip edge abnormality detection device, a coiled metal strip edge abnormality detection method, and a coiled metal strip edge abnormality detection program that can reduce false detections when inspecting the edge portions of coiled metal strips. [Means for solving the problem]

[0010] After extensive investigation, the inventors have found that the above object can be achieved by the present invention, which is described below. That is, a coiled metal strip edge abnormality detection device according to one aspect of the present invention comprises an image acquisition unit that acquires an image of an edge portion of a coiled metal strip taken from the axial direction, a smoothing processing unit that generates a smoothed image by smoothing the image acquired by the image acquisition unit, and an abnormality determination unit that determines whether or not there is an abnormality in the edge portion based on the smoothed image generated by the smoothing processing unit.

[0011] Such a coiled metal strip edge abnormality detection device generates a smoothed image, and therefore can generate a standard based on the image acquired by the image acquisition unit, thereby reducing false detections when inspecting the edge portion of a coiled metal strip.

[0012] In another aspect, in the above-mentioned coil-shaped metal band edge abnormality detection device, the smoothing processing unit generates a circumferentially smoothed image as the smoothed image by smoothing a metal band region image that includes at least the metal band in the image acquired by the image acquisition unit along the circumferential direction of the coil, and the abnormality determination unit includes a differential image generation unit that generates a difference image in brightness between the metal band region image and the circumferentially smoothed image generated by the smoothing processing unit, and a determination unit that determines whether or not there is an abnormality in the edge portion based on the difference image generated by the differential image generation unit.

[0013] Since this coiled metal strip edge anomaly detection device generates a circumferentially smoothed image by smoothing along the circumferential direction, it is possible to generate a reference that includes partial fluctuations in brightness values ​​caused by unevenness in the axial direction due to so-called winding irregularities.The coiled metal strip edge anomaly detection device generates a brightness difference image between the metal strip region image and the circumferentially smoothed image, so it is possible to emphasize only abnormal areas such as cracks using an appropriate reference, thereby reducing false detections when inspecting the edge of a coiled metal strip.

[0014] In another aspect, in the above-mentioned coil-shaped metal band edge abnormality detection device, the smoothing processing unit generates a radially smoothed image as one of the smoothed images by smoothing a metal band region image that includes at least the metal band in the image acquired by the image acquisition unit along the radial direction of the coil, and generates a circumferentially smoothed image as another of the smoothed images by smoothing the generated radially smoothed image along the circumferential direction of the coil, and the abnormality determination unit includes a differential image generation unit that generates a difference image of brightness between the radially smoothed image generated by the smoothing processing unit and the circumferentially smoothed image generated by the smoothing processing unit, and a determination unit that determines the presence or absence of an abnormality in the edge portion based on the difference image generated by the differential image generation unit.

[0015] Such a coiled metal strip edge abnormality detection device generates a radially smoothed image by performing a smoothing process along the radial direction, thereby reducing fluctuations in brightness values ​​caused by recesses or protrusions (voids) that appear on the edge and are recessed or protruded in the width direction of the metal strip and are smaller than its thickness, thereby reducing false positives and enabling detection.

[0016] In another aspect, in the above-mentioned coil-shaped metal band edge abnormality detection device, the smoothing processing unit generates a first angle range smoothed image as one of the smoothed images by smoothing a metal band region image that includes at least the metal band in the image acquired by the image acquisition unit along the circumferential direction of the coil within a predetermined first angle range, and generates a second angle range smoothed image as another of the smoothed images by smoothing the metal band region image along the circumferential direction of the coil within a predetermined second angle range different from the first angle range, and the abnormality determination unit includes a difference image generation unit that generates a difference image of brightness between the first angle smoothed image generated by the smoothing processing unit and the second angle smoothed image generated by the smoothing processing unit, and a determination unit that determines the presence or absence of an abnormality in the edge portion based on the difference image generated by the difference image generation unit.

[0017] Such a coiled metal strip edge anomaly detection device generates a first angle-smoothed image in a first angle range and a second angle-smoothed image in a second angle range, allowing the size of the edge anomaly in the circumferential direction for which the presence or absence of an anomaly is to be detected to be selected.

[0018] In another aspect, in the above-mentioned coil-shaped metal band edge abnormality detection device, the smoothing processing unit generates a first-time smoothed image as one of the smoothed images by repeatedly smoothing a metal band region image that includes at least the metal band in the image acquired by the image acquisition unit a predetermined first number of times along the circumferential direction of the coil, and generates a second-time smoothed image as another of the smoothed images by repeatedly smoothing the metal band region image along the circumferential direction of the coil a predetermined second number of times different from the first number of times, and the abnormality determination unit includes a difference image generation unit that generates a difference image of brightness between the first-time smoothed image generated by the smoothing processing unit and the second-time smoothed image generated by the smoothing processing unit, and a determination unit that determines the presence or absence of an abnormality in the edge portion based on the difference image generated by the difference image generation unit.

[0019] Such a coiled metal strip edge abnormality detection device generates a first-time smoothed image for a first number of times and a second-time smoothed image for a second number of times, so that the size of the edge abnormality for which the presence or absence of an abnormality is to be detected can be selected in the circumferential direction according to the size per pixel of the image acquired by the image acquisition unit.

[0020] In another aspect, in the coiled metal strip edge abnormality detection device described above, the smoothing processing unit converts the image acquired by the image acquisition unit into polar coordinates and then performs smoothing processing.

[0021] Such a coiled metal strip edge abnormality detection device performs the smoothing process after polar coordinate conversion, which simplifies the smoothing process.

[0022] Another aspect of the present invention provides a method for detecting abnormalities in the edge of a coiled metal strip, comprising an image acquisition step for acquiring an image of the edge portion of a metal strip wound in a coil from an axial direction, a smoothing processing step for generating a smoothed image by smoothing the image acquired in the image acquisition step, and an abnormality determination step for determining whether or not there is an abnormality in the edge portion based on the smoothed image generated in the smoothing processing step.

[0023] Another aspect of the present invention provides a coiled metal strip edge abnormality detection program that causes a computer to execute an image acquisition process for acquiring an image of the edge portion of a coiled metal strip taken from the axial direction, a smoothing process process for generating a smoothed image by smoothing the image acquired in the image acquisition process, and an abnormality determination process for determining whether or not there is an abnormality in the edge portion based on the smoothed image generated in the smoothing process process.

[0024] Such a coiled metal strip edge abnormality detection method and coiled metal strip edge abnormality detection program can reduce false detections when inspecting the edge portion of a coiled metal strip.

[0025] In another aspect, in the above-mentioned coil-shaped metal band edge abnormality detection device, the smoothing processing unit generates a third angle range smoothed image as one of the smoothed images by smoothing a metal band region image that includes at least the metal band in the image acquired by the image acquisition unit along the circumferential direction of the coil within a predetermined third angle range, and generates a fourth angle range smoothed image as another of the smoothed images by smoothing the metal band region image along the circumferential direction of the coil within a predetermined fourth angle range that is different from the third angle range, and further includes an abnormality size processing unit that determines the size of the abnormality in the edge portion based on the third and fourth angle range smoothed images generated by the smoothing processing unit.

[0026] Such a coiled metal strip edge abnormality detection device can determine the size of the abnormality in the edge portion (magnitude of the abnormality).

[0027] In another aspect, in the above-mentioned coil-shaped metal strip edge abnormality detection device, there are a plurality of different combinations of the third angle range and the fourth angle range, the smoothing processing unit generates the third and fourth angle range smoothed images for each of the plurality of combinations, and the abnormality size processing unit calculates the size of the abnormality for each of the plurality of combinations.

[0028] By varying the combination of the third angle range and the fourth angle range, the target size to be detected can be changed. The coiled metal strip edge anomaly detection device has a plurality of different combinations of the third angle range and the fourth angle range, so it is possible to determine the size of an anomaly in a plurality of targets of the size to be detected.

[0029] In another aspect, in the above-mentioned coil-shaped metal band edge abnormality detection device, the smoothing processing unit generates a third-time smoothed image as one of the smoothed images by repeatedly smoothing a metal band region image that includes at least the metal band in the image acquired by the image acquisition unit a predetermined third number of times along the circumferential direction of the coil, and generates a fourth-time smoothed image as another of the smoothed images by repeatedly smoothing the metal band region image along the circumferential direction of the coil a predetermined fourth number of times different from the third number of times, and further includes an abnormality size processing unit that determines the size of the abnormality in the edge portion based on the third and fourth-time smoothed images generated by the smoothing processing unit.

[0030] Such a coiled metal strip edge abnormality detection device can determine the size of the abnormality in the edge portion (magnitude of the abnormality).

[0031] In another aspect, in the above-mentioned coil-shaped metal strip edge abnormality detection device, there are multiple combinations of the third and fourth counts that are different from each other, the smoothing processing unit generates the third and fourth count smoothed images for each of the multiple combinations, and the abnormality size processing unit calculates the size of the abnormality for each of the multiple combinations.

[0032] By varying the combination of the third and fourth counts, the target size to be detected can be changed. The coiled metal strip edge anomaly detection device has multiple different combinations of the third and fourth counts, so it is possible to determine the size of an anomaly in multiple targets of the desired size to be detected. [Effects of the Invention]

[0033] The coiled metal strip edge abnormality detection device, coiled metal strip edge abnormality detection method, and coiled metal strip edge abnormality detection program of the present invention can reduce false detections when inspecting the edge portion of a coiled metal strip. [Brief explanation of the drawings]

[0034] [Figure 1] 1 is a block diagram showing the configuration of a coiled metal strip edge abnormality detection device according to an embodiment; [Figure 2] 10A and 10B are diagrams for explaining how an image of a detection target is generated by capturing an image of an edge portion of a metal strip wound in a coil shape from an axial direction. [Figure 3] FIG. 10 is a diagram illustrating an image of a detection target, as an example. [Figure 4] 3B is a diagram schematically showing, as an example, an image of the detection target obtained by polar coordinate conversion of the image of the detection target schematically shown in FIG. 3A. FIG. [Figure 5] As an example, this is a diagram showing brightness values ​​in the circumferential direction in the image of the detection target shown typically in FIG. [Figure 6] FIG. 5 is a diagram showing, as an example, brightness values ​​in the circumferential direction in a circumferentially smoothed image based on the image of the detection target shown typically in FIG. [Figure 7] FIG. 5 is a diagram showing, as an example, brightness values ​​in the circumferential direction in a difference image based on the image of the detection target shown typically in FIG. [Figure 8] 4 is a flowchart showing the operation of the coiled metal strip edge abnormality detection device. [Figure 9]As an example, this figure shows the locations and sizes of cracks detected for the combinations n=1 and n=7. [Figure 10] As an example, this figure shows the locations and sizes of cracks detected in the combinations of n=7 and n=21, and the combinations of n=21 and n=37. DETAILED DESCRIPTION OF THE INVENTION

[0035] Hereinafter, one or more embodiments of the present invention will be described with reference to the drawings. However, the scope of the invention is not limited to the disclosed embodiments. In addition, components with the same reference numerals in each drawing indicate the same components, and their description will be omitted as appropriate. In this specification, when referring to a general term, a reference numeral without a subscript is used, and when referring to an individual component, a reference numeral with a subscript is used.

[0036] In one embodiment, a coiled metal strip edge anomaly detection device detects the presence or absence of anomalies in the edge of a coiled metal strip. The coiled metal strip edge anomaly detection device includes an image acquisition unit that acquires an axial image of the edge of the coiled metal strip, a smoothing processing unit that smooths the image acquired by the image acquisition unit to generate a smoothed image, and an anomaly determination unit that determines the presence or absence of anomalies in the edge based on the smoothed image generated by the smoothing processing unit. The coiled metal strip edge anomaly detection device will be described in more detail below.

[0037] FIG. 1 is a block diagram showing the configuration of a coiled metal strip edge abnormality detection device according to an embodiment. FIG. 2 is a diagram illustrating how an image of a detection target is generated by capturing an image of an edge of a coiled metal strip from an axial direction. FIG. 3 is a diagram illustrating an example of the image of the detection target. FIG. 3A is a schematic diagram of the image of the detection target, and FIG. 3B is a cross-sectional view of the edge of the coiled metal strip taken along section line II shown in FIG. 3A. FIG. 4 is a schematic diagram illustrating an example of an image of the detection target obtained by polar coordinate transformation of the image of the detection target shown in FIG. 3A. The horizontal axis of FIG. 4 represents the circumferential direction of the coiled metal strip (coil), and the vertical axis represents the radial direction of the coil. FIG. 5 is a diagram illustrating, as an example, brightness values ​​relative to the circumferential direction in the image of the detection target shown in FIG. 4. FIG. 6 is a diagram illustrating, as an example, brightness values ​​relative to the circumferential direction in a circumferentially smoothed image based on the image of the detection target shown in FIG. 4. The horizontal axis of each of FIGS. 5 and 6 represents the circumferential direction of the coil, and the vertical axis represents brightness. Fig. 7 is a diagram showing, as an example, brightness values ​​in the circumferential direction in a difference image based on the image of the detection target shown typically in Fig. 4. The horizontal axis of Fig. 7 represents the circumferential direction of the coil, and the vertical axis represents brightness (difference in brightness) of the difference image.

[0038] The coil-shaped metal strip edge abnormality detection device S in the embodiment (hereinafter, abbreviated as "edge abnormality detection device" as appropriate) includes, for example, an image acquisition unit 1, a control processing unit 2, an input unit 3, an output unit 4, an interface unit (IF) 5, a memory unit 6, and an illumination unit 7, as shown in FIG.

[0039] The image acquisition unit 1 is connected to the control processing unit 2 and, under the control of the control processing unit 2, acquires an image (image of the detection target, edge image) of an edge portion of a detection target in a coiled metal strip (coiled metal strip, coil) captured from an axial direction. The metal strip is, for example, a metal (including an alloy) such as a steel plate formed into a long plate (sheet) extending in one direction. The image acquisition unit 1 is, for example, an imaging device that captures an image of the edge portion of the detection target in the coil from an axial direction to generate the edge image. The imaging device is, for example, a color digital camera or a monochrome digital camera. Alternatively, the image acquisition unit 1 is, for example, an interface circuit that inputs and outputs data to and from an external device. The external device is, for example, a storage medium, such as a USB (Universal Serial Bus) memory or an SD card (registered trademark), that stores the edge image. Alternatively, the external device may be a drive device that reads data from a recording medium, such as a CD-ROM (Compact Disc Read Only Memory), a CD-R (Compact Disc Recordable), a DVD-ROM (Digital Versatile Disc Read Only Memory), or a DVD-R (Digital Versatile Disc Recordable), on which the edge image is recorded. The interface circuit serving as the image acquisition unit 1 may be connected to the external device via a wired or wireless connection. Alternatively, the image acquisition unit 1 may be, for example, a communication interface circuit that transmits and receives communication signals to and from an external device, and the external device may be a server device that is connected to the communication interface circuit via a network (WAN (Wide Area Network, including a public communication network)) or LAN (Local Area Network) and manages the edge image. Note that when the image acquisition unit 1 is an interface circuit or a communication interface circuit, the image acquisition unit 1 may also function as the IF unit 5 (i.e., the IF unit 5 may be used as the image acquisition unit 1).

[0040] In this embodiment, the image acquisition unit 1 is, for example, a digital camera (hereinafter abbreviated as "camera") 1 that captures an image of the edge portion of the coil CO illuminated by the illumination unit 7 from the axial direction of the axis AX, as shown in FIG. 2 . When the coil CO is captured by the camera 1 in this manner, an edge image PCa shown in FIG. 3A is generated, for example. The coil CO shown in FIG. 3 is formed by winding a metal strip five times, and so-called cracks (fissures) DE1 and DE2 have occurred in the fourth and third metal strips from the inside. As shown in FIG. 3A , these cracks DE1 and DE2 are captured in the edge image PCa. In the coil CO shown in FIG. 3 , the third metal strip from the inside is shifted in the axial direction of the axis AX due to so-called winding irregularities, and is recessed from the edge surface formed by the edges of the first, second, fourth, and fifth metal strips. In the edge image PCa, the brightness of the image portion capturing the third metal strip from the inside is different from the brightness of the image portions capturing the first, second, fourth, and fifth metal strips. For example, the brightness of the image portion in which the third metal band from the inside is captured is darker than the brightness of each of the image portions in which the first, second, fourth, and fifth metal bands are captured.

[0041] The input unit 3 is connected to the control processing unit 2 and is a device that inputs various commands, such as a command to instruct the edge abnormality detection device S to start detection, and various data required for operating the edge abnormality detection device S, such as the date of detection, to the edge abnormality detection device S, and is, for example, a plurality of input switches assigned with predetermined functions, a keyboard, a mouse, etc. The output unit 4 is connected to the control processing unit 2 and is a device that outputs the commands and data input from the input unit 3 and abnormalities detected by the edge abnormality detection device S under the control of the control processing unit 2, and is, for example, a display device such as a CRT display, LCD (liquid crystal display), or organic EL display, or a printing device such as a printer.

[0042] The input unit 3 and the output unit 4 may be configured as a touch panel. In this touch panel configuration, the input unit 3 is a position input device, such as a resistive or capacitive type, that detects and inputs an operation position, and the output unit 4 is a display device. In this touch panel, a position input device is provided on the display surface of the display device, and one or more input content candidates that can be input are displayed on the display device. When a user touches the display position showing the input content they want to input, the position is detected by the position input device, and the display content displayed at the detected position is input to the edge anomaly detection device S as the user's operation input content. With such a touch panel, the user can easily intuitively understand the input operation, providing an edge anomaly detection device S that is easy for the user to use.

[0043] The IF unit 5 is connected to the control processing unit 2 and is a circuit that inputs and outputs data to and from, for example, an external device under the control of the control processing unit 2, and is, for example, an interface circuit for RS-232C, which is a serial communication method, an interface circuit using the Bluetooth (registered trademark) standard, an interface circuit using the USB standard, etc. The IF unit 5 may also be, for example, a communication interface circuit that transmits and receives communication signals to and from an external device, such as a data communication card or a communication interface circuit conforming to the IEEE802.11 standard, etc.

[0044] The illumination unit 7 is connected to the control processing unit 2 and is a light source that emits illumination light to illuminate the edge portion of the coil under the control of the control processing unit 2. For example, as shown in Fig. 2, the illumination unit 7 illuminates the edge portion of the coil CO with illumination light from diagonally above the coil CO. Note that the illumination direction of the illumination unit 7 is not limited to this and may be any direction as long as it can illuminate the edge portion of the coil CO.

[0045] The storage unit 6 is connected to the control processing unit 2 and is a circuit that stores various predetermined programs and various predetermined data under the control of the control processing unit 2. The various predetermined programs include, for example, a control program that controls the components 1, 3, 4, 5, 6, 7, and 8 of the edge abnormality detection device S, a smoothing processing program that generates a smoothed image by smoothing the image acquired by the image acquisition unit 1, and an abnormality determination program that determines whether an edge portion has an abnormality based on the smoothed image generated by the smoothing processing program. The various predetermined data include data necessary for executing these programs, such as a threshold value (determination threshold value) Th for determining whether an abnormality exists. The storage unit 6 includes, for example, a nonvolatile storage element such as a read-only memory (ROM) or a rewritable nonvolatile storage element such as an electrically erasable programmable read-only memory (EEPROM). The storage unit 6 also includes a random access memory (RAM) that serves as the working memory of the control processing unit 2 and stores data generated during execution of the predetermined programs. The storage unit 6 may also be configured with a hard disk drive with a relatively large storage capacity.

[0046] The control processing unit 2 is a circuit that controls each of the units 1, 3 to 7 of the edge abnormality detection device S according to the function of each unit and determines whether or not an abnormality exists in the edge portion of the coil. The control processing unit 2 is configured, for example, with a CPU (Central Processing Unit) and its peripheral circuits. By executing a control processing program, the control processing unit 2 functionally includes a control unit 21, a smoothing processing unit 22, and an abnormality determination unit 23.

[0047] The control unit 21 controls each of the units 1, 3 to 7 of the edge abnormality detection device S in accordance with the function of each unit, and controls the entire edge abnormality detection device S.

[0048] The smoothing processing unit 22 generates a smoothed image by smoothing the image acquired by the image acquisition unit 1. More specifically, the smoothing processing unit 22 generates a circumferentially smoothed image as the smoothed image by smoothing a metal band region image, which includes at least the metal band (coil) in the image acquired by the image acquisition unit 1, along the circumferential direction of the coil. Here, the smoothing processing unit 22 performs polar coordinate conversion on the image acquired by the image acquisition unit 1 before smoothing. For example, as shown in FIG. 4, the polar coordinate space is formed by a horizontal axis (angle axis) φ representing the circumferential direction of the coil and a vertical axis (distance axis) r that is perpendicular to the horizontal axis φ and represents the radial direction of the coil. The edge image PCa shown in FIG. 3A is converted into an image PCb in polar coordinates shown in FIG. 4 by polar coordinate conversion. Let the center point of the coil be (a, b), the brightness at point (x, y) be L, the thickness of the metal band (coil plate thickness) be d [mm], the size of the anomaly to be detected be w [mm], the size of one pixel in the image be p [mm / pixel], the size of one pixel in the radial direction after polar coordinate conversion be s [mm / pixel], and the size of one pixel in the circumferential direction after polar coordinate conversion be t [deg / pixel]. After polar coordinate conversion, smoothing processing unit 22 smoothes the metal band region image along the circumferential direction of the coil by using the following equation 1, which gives the brightness I(φ, r) after smoothing at point (φ, r), to generate a circumferentially smoothed image. Note that after polar coordinate conversion, φ becomes a coordinate of the image and is therefore an integer.

[0049]

number

[0050] Here, n represents the ordinal number of the summation symbol Σ, and preferably n is an odd number that satisfies the following formula 2, which is a modification of the infinitesimal-angle arc n×trs≧4w. This allows the smoothing range to be made sufficiently larger than the size of the abnormality to be detected, and the abnormality to be detected is more emphasized in the difference image generated by the difference image generation unit described below.

[0051]

number

[0052] When smoothing is performed along the circumferential direction of the coil without polar coordinate conversion, the following equation 3 is used to give the smoothed brightness I(x, y) at point (x, y) when y≦b, and the following equation 4 is used when y>b.

[0053]

number

[0054]

number

[0055] Preferably, n is an odd number that satisfies the following formula 5.

[0056]

number

[0057] The abnormality determination unit 23 determines the presence or absence of an abnormality in the edge portion based on the smoothed image generated by the smoothing processing unit 22. An abnormality in the edge portion is an uneven portion of a certain size or larger that occurs on the side surface of the edge portion of the metal band, such as a crack (fissure). More specifically, the abnormality determination unit 23 includes a differential image generation unit 231 and a determination unit 232 that are functionally formed. The differential image generation unit 231 generates a difference image of the brightness between the metal band region image and the circumferentially smoothed image generated by the smoothing processing unit 22. The determination unit 232 determines the presence or absence of an abnormality in the edge portion based on the difference image generated by the differential image generation unit 231. More specifically, the determination unit 232 compares the brightness of the difference image with a determination threshold Th and determines that a portion with a brightness equal to or smaller than the determination threshold Th (or a portion with a brightness smaller (darker) than the determination threshold Th) is abnormal.

[0058] For example, in the polar coordinate image PCb shown in FIG. 4, the brightness graph BRα of the image portion α-α capturing the fourth metal strip from the inside shows that the sides of the metal strip glow almost uniformly except for the crack DE1, which is darker than the crack DE1. As a result, as shown in FIG. 5, the brightness graph BRα has a profile in which the brightness value BR1 is obtained at every circumferential position except for the crack DE1, but is darker than the brightness value BR1 at the crack DE1. Similarly, the brightness graph BRβ of the image portion β-β capturing the third metal strip from the inside shows that the sides of the metal strip glow almost uniformly except for the crack DE2, which is darker than the brightness value BR2 at the crack DE2, as shown in FIG. 5. Furthermore, as described above, due to so-called winding irregularities, the brightness of the third image portion β-β differs from the brightness of the fourth image portion α-α, for example, it is darker. Therefore, the brightness value BR1 in the brightness graph BRα of the fourth image portion α-α is greater (brighter, higher) than the brightness value BR2 in the brightness graph BRβ of the third image portion β-β. Therefore, if an abnormality is determined for each of the brightness graphs BRα and BRβ of the third and fourth image portions α-α and β-β using a single judgment threshold Th, an erroneous determination may occur, such as the entire brightness graph BRβ being below the judgment threshold Th and the crack DE2 not being determined to be abnormal, or the brightness value of the crack DE1 in the brightness graph BRα being greater than the judgment threshold and the crack DE1 not being determined to be abnormal.

[0059] For this reason, in the edge abnormality detection device S of this embodiment, as described above, first, the smoothing processing unit 22 performs smoothing processing along the circumferential direction of the coil. For example, the brightness graph BRα of the fourth image portion α-α shown in FIG. 5 is smoothed to become the brightness graph SMα having a profile with approximately the same brightness value BR3 at each position in the circumferential direction, as shown in FIG. 6. This blurs the brightness of the crack DE1, and a reference for the fourth image portion α-α is generated. Similarly, the brightness graph BRβ of the third image portion β-β shown in FIG. 5 is smoothed to become the brightness graph SMβ having a profile with approximately the same brightness value BR4 at each position in the circumferential direction, as shown in FIG. 6. This blurs the brightness of the crack DE2, and a reference for the third image portion β-β is generated.

[0060] Then, a difference image is generated by the difference image generation unit 231. In this difference image, the brightness graph SBα of the fourth image portion α-α has a profile in which, at each circumferential position, it becomes approximately the brightness value BR5 at each circumferential position except for the position of the crack DE1, but the brightness value is darker than the brightness value BR5 at the position of the crack DE1, as shown in FIG. 7, by subtracting the brightness graph SMβ shown in FIG. 6 from the brightness graph BRα shown in FIG. 5 at each circumferential position. Similarly, the brightness graph SBβ of the third image portion β-β in the difference image has a profile in which, at each circumferential position, it becomes approximately the brightness value BR5 at each circumferential position except for the position of the crack DE2, but the brightness value is darker than the brightness value BR5 at the position of the crack DE2, as shown in FIG. 7, by subtracting the brightness graph SMβ shown in FIG. 6 from the brightness graph BRβ shown in FIG. Since the brightness graphs SBα and SBβ of the third and fourth image portions α-α and β-β are subtracted using the respective standards (brightness graphs SMα and SMβ) of the third and fourth image portions α-α and β-β, the brightness values ​​are all approximately the same at BR5 at each circumferential position except for the positions of the cracks DE1 and DE2, and therefore the determination unit 232 can determine whether there is an abnormality using a single determination threshold Th for the brightness graphs SBα and SBβ of the third and fourth image portions α-α and β-β. The determination threshold Th is set in advance as appropriate from a plurality of samples.

[0061] The control processing unit 2, input unit 3, output unit 4, IF unit 5, and storage unit 6 can be configured by, for example, a desktop or notebook computer. The computer configuring each of these units 2 to 6 may be located, for example, in an operation room that operates a crane for moving the coil, and may be incorporated into a console (or may serve as the console), or may be separate from the console.

[0062] Next, the operation of this embodiment will be described with reference to the flowchart of FIG.

[0063] When the coiled metal strip edge abnormality detection device S having such a configuration is powered on, it initializes the necessary parts and starts operation. By executing the control processing program, the control processing unit 2 is functionally configured with a control unit 21, a smoothing processing unit 22, and an abnormality determination unit 23, and the abnormality determination unit 23 is functionally configured with a difference image generation unit 231 and a determination unit 232.

[0064] In FIG. 8, first, the edge abnormality detection device S acquires an image of the detection target by the image acquisition unit 1, and stores the acquired image in the storage unit 6 (S1).

[0065] Next, the edge abnormality detection device S generates a smoothed image by smoothing the image acquired by the image acquisition unit 1 in process S1 using the smoothing processing unit 22 of the control processing unit 2 (S2). In this embodiment, the smoothing processing unit 22 generates a circumferentially smoothed image as the smoothed image by smoothing, along the circumferential direction of the coil, the metal band region image that captures at least the metal band in the image acquired by the image acquisition unit 1 in process S1.

[0066] Next, the edge abnormality detection device S causes the difference image generation unit 231 of the abnormality determination unit 23 in the control processing unit 2 to generate a difference image of brightness between the metal band region image and the circumferentially smoothed image generated by the smoothing processing unit 22 in process S2 (S3). In this embodiment, the difference image generation unit 231 generates the difference image by subtracting the brightness value of the circumferentially smoothed image from the brightness value of the metal band region image at each of the same positions.

[0067] Next, the edge abnormality detection device S determines the presence or absence of an abnormality in the edge portion by the determination unit 232 of the abnormality determination unit 23 in the control processing unit 2 (S4). In this embodiment, the determination unit 232 determines the presence or absence of an abnormality in the edge portion based on the difference image generated by the difference image generation unit 231 in process S3. More specifically, the determination unit 232 compares the luminance value of the difference image with the determination threshold Th at each position, and if the result of the comparison shows that the luminance value of the difference image is equal to or less than the determination threshold Th (or if the luminance value of the difference image is smaller (darker) than the determination threshold Th), it determines that an abnormality exists at that position, and if the result of the comparison shows that the luminance value of the difference image exceeds the determination threshold Th (or if the luminance value of the difference image is equal to or greater than the determination threshold Th), it determines that no abnormality exists at that position.

[0068] Then, the edge abnormality detection device S outputs the determination result obtained in the process S4 to the output unit 4 by the control processing unit 2 (S6), and ends this process. Note that, if necessary, the determination result may be output from the IF unit 5 to an external device.

[0069] As described above, the coiled metal strip edge abnormality detection device D in the embodiment and the coiled metal strip abnormality detection method and coiled metal strip abnormality detection program implemented therein generate smoothed images, and therefore can generate standards according to the images acquired by the image acquisition unit 1, thereby reducing false detections when inspecting the edge portions of coiled metal strips.

[0070] The coiled metal strip edge anomaly detection device D, coiled metal strip edge anomaly detection method, and coiled metal strip edge anomaly detection program generate a circumferentially smoothed image by smoothing along the circumferential direction, making it possible to generate a reference that includes partial fluctuations in brightness values ​​caused by unevenness in the axial direction due to so-called winding irregularities.The coiled metal strip edge anomaly detection device D, coiled metal strip edge anomaly detection method, and coiled metal strip edge anomaly detection program generate a brightness difference image between the metal strip region image and the circumferentially smoothed image, making it possible to emphasize only abnormal areas such as cracks using an appropriate reference, thereby reducing false detections when inspecting the edge of a coiled metal strip.

[0071] The coiled metal strip edge abnormality detection device D, the coiled metal strip edge abnormality detection method, and the coiled metal strip edge abnormality detection program perform the smoothing process after polar coordinate conversion, which simplifies the smoothing process.

[0072] The coil-shaped metal strip edge abnormality detection device S described above generates a circumferentially smoothed image and generates a difference image to determine whether or not an abnormality exists. However, the difference image may be generated in the following first to third modified embodiments to determine whether or not an abnormality exists.

[0073] In the coil-shaped metal band edge abnormality detection device S in the first modified form, the smoothing processing unit 22 generates a radially smoothed image as one of the smoothed images by smoothing a metal band region image that captures at least the metal band in the image acquired by the image acquisition unit 1 along the radial direction of the coil, and generates a circumferentially smoothed image as another of the smoothed images by smoothing the generated radially smoothed image along the circumferential direction of the coil.

[0074] More specifically, after the polar coordinate transformation, the smoothing processing unit 22 smoothes the metal band region image along the radial direction of the coil by using the following equation 6, which gives the smoothed brightness I(φ, r) at the point (φ, r), to generate a radially smoothed image.

[0075]

number

[0076] Preferably, n is the smallest odd number that satisfies the following formula 7. This reduces the change in brightness value due to irregularities equal to or smaller than the plate thickness, making it possible to reduce false detections caused by irregularities equal to or smaller than the plate thickness.

[0077]

number

[0078] The difference image generating unit 231 of the abnormality determining unit 23 generates a difference image in brightness between the radially smoothed image generated by the smoothing processing unit 22 and the circumferentially smoothed image generated by the smoothing processing unit 22. The difference image generating unit 231 generates the difference image by subtracting the brightness value of the circumferentially smoothed image from the brightness value of the radially smoothed image at each same position.

[0079] Determination section 232 of abnormality determination section 23 determines the presence or absence of an abnormality in the edge portion based on the difference image generated by difference image generation section 231. Determination section 232 compares the luminance value of the difference image with a determination threshold Th at each position, and if the result of the comparison shows that the luminance value of the difference image is equal to or less than the determination threshold Th (or if the luminance value of the difference image is smaller (darker) than the determination threshold Th), it determines that an abnormality exists at that position, and if the result of the comparison shows that the luminance value of the difference image exceeds the determination threshold Th (or if the luminance value of the difference image is equal to or greater than the determination threshold Th), it determines that no abnormality exists at that position.

[0080] The coiled metal strip edge abnormality detection device S in this first variant, and the coiled metal strip abnormality detection method and coiled metal strip abnormality detection program implemented therein, generate a radially smoothed image by performing a smoothing process along the radial direction, thereby reducing fluctuations in brightness values ​​caused by voids that occur in the edge portion and enabling detection with reduced false positives.

[0081] In the coil-shaped metal band edge abnormality detection device S in the second modified form, the smoothing processing unit 22 generates a first angle range smoothed image as one of the smoothed images by smoothing a metal band area image that includes at least the metal band in the image acquired by the image acquisition unit 1 along the circumferential direction of the coil in a predetermined first angle range φ1≦φ≦φ2, and generates a second angle range smoothed image as another of the smoothed images by smoothing the metal band area image along the circumferential direction of the coil in a predetermined second angle range φ3≦φ≦φ4 that is different from the first angle range φ1≦φ≦φ2.

[0082] When generating the first angle range smoothed image, n is preferably an odd number that satisfies the above formula 2, and when generating the second angle range smoothed image, n is preferably an odd number that satisfies the following formula 8. This reduces brightness fluctuations caused by irregularities that are smaller than the abnormal size to be detected and emphasizes brightness fluctuations caused by irregularities that are the abnormal size to be detected, thereby reducing overdetection.

[0083]

number

[0084] When smoothing is performed along the circumferential direction of the coil without polar coordinate conversion by using the above equations 3 and 4, when generating the first angle range smoothed image, n is preferably an odd number that satisfies the above equation 5, and when generating the second angle range smoothed image, n is preferably an odd number that satisfies the following equation 9.

[0085]

number

[0086] The difference image generating unit 231 of the abnormality determining unit 23 generates a difference image in luminance between the first angle-smoothed image generated by the smoothing processing unit 22 and the second angle-smoothed image generated by the smoothing processing unit 22. The difference image generating unit 231 generates the difference image by subtracting the luminance value of the second angle-smoothed image from the luminance value of the first angle-smoothed image at each identical position when the first angle-smoothed image and the second angle-smoothed image are overlaid, for example, corner by corner (for example, upper left corner).

[0087] Determination section 232 of abnormality determination section 23 determines the presence or absence of an abnormality in the edge portion based on the difference image generated by difference image generation section 231. Determination section 232 compares the luminance value of the difference image with a determination threshold Th at each position, and if the result of the comparison shows that the luminance value of the difference image is equal to or less than the determination threshold Th (or if the luminance value of the difference image is smaller (darker) than the determination threshold Th), it determines that an abnormality exists at that position, and if the result of the comparison shows that the luminance value of the difference image exceeds the determination threshold Th (or if the luminance value of the difference image is equal to or greater than the determination threshold Th), it determines that no abnormality exists at that position.

[0088] The coiled metal strip edge abnormality detection device S and the coiled metal strip abnormality detection method and coiled metal strip abnormality detection program implemented therein in this second modified form generate a first angle-smoothed image in a first angle range and a second angle-smoothed image in a second angle range, so that the size of the edge abnormality for which the presence or absence of an abnormality is to be detected can be selected in the circumferential direction.

[0089] In the coil-shaped metal band edge abnormality detection device S in the third modified form, the smoothing processing unit 22 generates a first-time smoothed image as one of the smoothed images by repeatedly smoothing a metal band area image that includes at least the metal band in the image acquired by the image acquisition unit 1 a predetermined first number M1 along the circumferential direction of the coil, and generates a second-time smoothed image as another of the smoothed images by repeatedly smoothing the metal band area image a predetermined second number M2 that is different from the first number M1 along the circumferential direction of the coil.

[0090] More specifically, after the polar coordinate transformation, the smoothing processing unit 22 repeatedly smoothes the metal band region image along the radial direction of the coil a first number of times M1 by using the following equation 10, which gives the smoothed brightness I(φ, r) at the point (φ, r), to generate a first-time smoothed image, and repeatedly smooths the metal band region image along the radial direction of the coil a second number of times M2 by using the following equation 10, to generate a second-time smoothed image.

[0091]

number

[0092] Preferably, n is the smallest odd number that satisfies the following formula 11. This makes it possible to emphasize only the fluctuations in brightness values ​​due to irregularities that are close in size to the abnormality to be detected, thereby reducing false detections.

[0093]

number

[0094] Alternatively, preferably, n satisfies the following formula 12, the first number of times M1 is 1, and the second number of times M2 is a plurality of times, for example, 4 (M1=1, M2=4). In one experimental example, when detecting cracks, performing smoothing processing over an area four times the size of the crack enabled more accurate detection of the crack. For this reason, M1=1 and M2=4 are preferable. More specifically, if the Mth smoothed image is PC(M), the first smoothed image PC(1) is generated by smoothing the metal band region image using the above formula 10 with n satisfying the following formula 12. The second smoothed image PC(4) is generated by smoothing the first smoothed image PC(1) using the above equation 10 with n satisfying the following equation 12 to generate a second smoothed image PC(2), smoothing the second smoothed image PC(2) using the above equation 10 with n satisfying the following equation 12 to generate a third smoothed image PC(3), and smoothing the third smoothed image PC(3) using the above equation 10 with n satisfying the following equation 12 (second smoothed image = fourth smoothed image PC(4)).

[0095]

number

[0096] In addition, when smoothing is performed along the circumferential direction of the coil without polar coordinate conversion by using the above equations 3 and 4, preferably, n satisfies the following equation 13, the first number of times M1 is 1, and the second number of times M2 is 4 (M1=1, M2=4).

[0097]

number

[0098] The coiled metal strip edge abnormality detection device S and the coiled metal strip abnormality detection method and coiled metal strip abnormality detection program implemented therein in this third modified form generate a first-count smoothed image of the first count M1 and a second-count smoothed image of the second count M2, so that the size of the edge abnormality for which the presence or absence of an abnormality is to be detected can be selected in the circumferential direction according to the size per pixel of the image acquired by the image acquisition unit 1.

[0099] In the above-described embodiments (including the first to third variants), the coiled metal strip edge abnormality detection device S may further include an abnormal size processing unit 24 in the control processing unit 2 as a fourth variant, as shown by the dashed line in Figure 1, and this abnormal size processing unit 24 may have, for example, two first and second modes.

[0100] In this first embodiment, the smoothing processing unit 22 generates a third angle range smoothed image as one of the smoothed images by smoothing a metal band region image, which includes at least the metal band, in the image acquired by the image acquisition unit 1 along the circumferential direction of the coil within a predetermined third angle range, and generates a fourth angle range smoothed image as another of the smoothed images by smoothing the metal band region image along the circumferential direction of the coil within a predetermined fourth angle range different from the third angle range. In this case, the smoothing processing unit 22 may be the same as the smoothing processing unit 22 in the second modified embodiment, with the first and second angle ranges φ1≦φ≦φ2 and φ3≦φ≦φ4 being the third and fourth angle ranges. The anomaly size processing unit 24 calculates the size of an anomaly in an edge portion based on the third and fourth angle range smoothed images generated by the smoothing processing unit 22, and is functionally configured in the control processing unit 2.

[0101] More specifically, the abnormality size processing unit 24 obtains a difference image in brightness between the third angle range smoothed image (=first angle range smoothed image) and the fourth angle range smoothed image (=second angle range smoothed image) generated by the smoothing processing unit 22, binarizes this difference image using, for example, 0 and 1, and determines the size of an image area where the value is 1 as abnormal. In generating the difference image, the difference image generating unit 231 of the abnormality determining unit 23 in the second modified embodiment described above may be used for both purposes. The threshold value for the binarization is set to an appropriate value from a plurality of samples in advance, for example. Alternatively, for the binarization, a so-called Otsu binarization process may be used. Otsu's binarization process calculates a histogram of the image to be binarized (input image, in this case the second smoothed image), divides the histogram in half, and defines the ratio of the variance of one side to the variance of the other side as the degree of separation. The class of the histogram at the position where the degree of separation is greatest is used as the threshold for binarization.

[0102] FIG. 9 shows three cracks DE31, DE32, and DE33, identified as examples of the anomaly, along with their sizes. FIG. 9 illustrates, as an example, the locations and sizes of cracks detected for combinations of n = 1 and n = 7. In the case shown in FIG. 9, the third angle range φ5≦φ≦φ6 (=first angle range φ1≦φ≦φ2) is 1, i.e., n = 1 in the above-mentioned Equation 10, and the fourth angle range φ7≦φ≦φ8 (=second angle range φ3≦φ≦φ4) is 7, i.e., n = 7 in the above-mentioned Equation 10. Note that, as shown in Equations 2 and 9, n is related to the angle range, and the angle range can be changed by changing n. Cracks DE31 and DE32 were detected in the ninth metal strip from the inside (the ninth metal strip from the bottom in FIG. 9), and crack DE33 was detected in the seventh metal strip from the inside (the seventh metal strip from the bottom in FIG. 9). Although Equation 10 is used here, Equation 1 may also be used.

[0103] The position LGm of the crack DE found as an example of this abnormality is expressed from the tip of the metal strip (the tip on the inner circumference, the start of the coil) by the following equation 14 when the metal strip is wound into a coil in the same direction as the +φ direction of the polar coordinates, and from the tip of the metal strip when the metal strip is wound into a coil in the opposite direction to the +φ direction of the polar coordinates, by the following equation 15. The position LGm of the crack DE is, in other words, the length of the metal strip from the tip of the metal strip to the position of the crack DE.

[0104]

number

[0105]

number

[0106] Here, d is the thickness of the metal strip (coil thickness) [mm], Rin is the inner diameter of the coil, and m is the number of turns of the metal strip in the coil where the crack DE occurs, counted from the inside. For example, for cracks DE31 and 32, m = 9, and for crack DE33, m = 7.

[0107] The coiled metal strip edge anomaly detection device S in the first aspect of the fourth modified embodiment can determine the size of the anomaly in the edge portion. For example, the output unit 4 may display an image showing the determined position and size of a crack DE as an example of the anomaly, as shown in Fig. 9. The position LGn of the anomaly expressed by the above-mentioned formula 14 or 15 may then be displayed on the output unit 4.

[0108] In the coiled metal strip edge anomaly detection device S according to the first aspect of this fourth modified embodiment, there may be a plurality of different combinations of the third angle range (= first angle range) and the fourth angle range (= second angle range), the smoothing processor 2 generates the third and fourth angle range smoothed images for each of the plurality of combinations, and the anomaly size processor 24 may calculate the size of the anomaly for each of the plurality of combinations. The larger n, the wider the angle range, and therefore the larger the size of an anomaly that can be detected. Therefore, by varying the combination of the third angle range and the fourth angle range, the target size to be detected can be changed.

[0109] An example is shown in Figure 10. Figure 10 is a diagram showing, as an example, the location and size of cracks detected for the combinations of n=7 and n=21 and n=21 and n=37. Figure 10A is a diagram showing the location and size of cracks detected for the combinations of n=7 and n=21, and Figure 10B is a diagram showing the location and size of cracks detected for the combinations of n=21 and n=37.

[0110] In the case shown in Fig. 10A, the third angle range (=first angle range) is 7, i.e., n = 7 in the above-mentioned formula 10, and the fourth angle range (=second angle range) is 21, i.e., n = 21 in the above-mentioned formula 10. Crack DE41 is detected in the ninth metal strip from the inside (the ninth metal strip from the bottom shown in Fig. 10A, m = 9), and defect DE42 is detected in the eighth metal strip from the inside (the eighth metal strip from the bottom shown in Fig. 10A, m = 8).

[0111] In the case shown in Figure 10B, the third angle range (=first angle range) is 21, i.e., n = 21 in the above equation 10, and the fourth angle range (=second angle range) is 37, i.e., n = 37 in the above equation 10. Crack DE51 is detected in the 11th metal strip from the inside (the 11th metal strip from the bottom shown in Figure 10B, m = 11).

[0112] As shown in Figures 9 and 10, the size of the crack DE detected with the combination of n = 7 and n = 21 shown in Figure 10A is larger than the size of the crack DE detected with the combination of n = 1 and n = 7 shown in Figure 9, and the size of the crack DE detected with the combination of n = 21 and n = 37 shown in Figure 10B is larger than the size of the crack DE detected with the combination of n = 7 and n = 21 shown in Figure 10A. In the examples shown in Figures 9 and 10, the thickness d of the metal strip (coil thickness) is approximately constant, so the difference in size of the crack DE can be rephrased as a difference in length of the crack DE. Note that the three cracks DE31, DE32, and DE33 shown in Figure 9 are approximately equal in size and are detected as the desired target size, and the two cracks DE41 and DE42 shown in Figure 10A are approximately equal in size and are detected as the desired other target size.

[0113] Such a coiled metal strip edge anomaly detection device S has multiple combinations of different third and fourth angle ranges, making it possible to determine the size of an anomaly in multiple targets of the desired size. For example, the output unit 4 displays three images showing the position and size of a crack DE determined as an example of the anomaly, as shown in Figures 9, 10A, and 10B. The multiple combinations of different third and fourth angle ranges are set to appropriate values ​​based on multiple samples in advance. Note that the multiple combinations of different third and fourth angle ranges may be changed by inputting them from the input unit 3.

[0114] On the other hand, in the second aspect, the smoothing processing unit 22 generates a third-time smoothed image as one of the smoothed images by repeatedly smoothing a metal band region image, which includes at least the metal band, in the image acquired by the image acquisition unit 1 along the circumferential direction of the coil a predetermined third number of times, and generates a fourth-time smoothed image as another of the smoothed images by repeatedly smoothing the metal band region image along the circumferential direction of the coil a predetermined fourth number of times different from the third number of times. In this case, the smoothing processing unit 22 may be the same as the smoothing processing unit 22 in the third modified aspect, with the first and second times M1 and M2 being the third and fourth times. The abnormality size processing unit 24 calculates the size of an abnormality in an edge portion based on the third and fourth-time smoothed images generated by the smoothing processing unit 22, and is functionally configured in the control processing unit 2.

[0115] More specifically, the abnormality size processing unit 24 obtains a difference image in brightness between the third smoothed image (=first smoothed image) and the fourth smoothed image (=second smoothed image) generated by the smoothing processing unit 22, binarizes this difference image using, for example, 0 and 1, and determines the size of an image area where the value is 1 as abnormal. In generating the difference image, the difference image generating unit 231 of the abnormality determining unit 23 in the third modified embodiment described above may be used for both purposes. The threshold value for the binarization is set to an appropriate value from a plurality of samples in advance, for example. Alternatively, for the binarization, a so-called Otsu binarization process may be used.

[0116] The coiled metal strip edge anomaly detection device S in the second aspect of the fourth modified embodiment can determine the size of the anomaly in the edge portion. For example, the output unit 4 may display an image showing the position and size of the crack DE determined as an example of the anomaly. The position LGn of the anomaly expressed by the above-mentioned formula 14 or formula 15 may then be displayed on the output unit 4.

[0117] In the coiled metal strip edge anomaly detection device S in the second aspect of this fourth modified embodiment, there may be a plurality of different combinations of the third count (=first count M1) and the fourth count (=second count M2), the smoothing processing unit 2 generates the third and fourth count smoothed images for each of the plurality of combinations, and the anomaly size processing unit 24 may calculate the size of the anomaly for each of the plurality of combinations. The more times the smoothing processing is performed, the greater the degree of blurring, and therefore the larger the size of the anomaly that can be detected. Therefore, by varying the combination of the third and fourth counts, the target size to be detected can be changed.

[0118] Such a coiled metal strip edge anomaly detection device S has multiple different combinations of the third and fourth counts, making it possible to determine the size of an anomaly in multiple targets of the desired size. For example, the output unit 4 displays images for each combination showing the position and size of the crack DE determined as an example of the anomaly. The multiple different combinations of the third and fourth counts are, for example, set to appropriate values ​​from multiple samples in advance. Note that the multiple different combinations of the third and fourth counts may be changed by inputting them from the input unit 3.

[0119] In order to express the present invention, the present invention has been properly and sufficiently described above through the embodiments with reference to the drawings, but it should be recognized that those skilled in the art can easily change and / or improve the above-mentioned embodiments. Therefore, unless the changes or improvements made by those skilled in the art are at a level that causes departure from the scope of the claims described in the claims, such changes or improvements are interpreted as being included in the scope of the claims. [Explanation of symbols]

[0120] S Coiled metal strip edge abnormality detection device 1 Image acquisition unit 2. Control processing section 6 Memory section 7. Lighting Section 21 Control section 22 Smoothing processing section 23 Abnormality determination section 24 Abnormal size processing unit 231 Differential Image Generation Unit 232 Judgment section

Claims

1. an image acquisition unit that acquires an image of an edge portion of the coiled metal strip from an axial direction; a smoothing processing unit that generates a smoothed image by smoothing the image acquired by the image acquisition unit; an abnormality determination unit that determines whether or not there is an abnormality in an edge portion based on the smoothed image generated by the smoothing processing unit, the smoothing processing unit generates a circumferentially smoothed image as the smoothed image by smoothing a metal band region image, in which at least the metal band is captured, in the image acquired by the image acquisition unit along a circumferential direction of the coil; The abnormality determination unit includes a differential image generation unit that generates a differential image of brightness between the metal band region image and the circumferentially smoothed image generated by the smoothing processing unit, and a determination unit that determines whether or not there is an abnormality in an edge portion based on the differential image generated by the differential image generation unit. Coiled metal strip edge abnormality detection device.

2. An image acquisition unit that acquires an image of an edge portion of a coiled metal strip from an axial direction; a smoothing processing unit that generates a smoothed image by smoothing the image acquired by the image acquisition unit; an abnormality determination unit that determines whether or not there is an abnormality in an edge portion based on the smoothed image generated by the smoothing processing unit, the smoothing processing unit generates a radially smoothed image as one of the smoothed images by smoothing a metal band region image, which includes at least the metal band, in the image acquired by the image acquisition unit along the radial direction of the coil, and generates a circumferentially smoothed image as one of the smoothed images by smoothing the generated radially smoothed image along the circumferential direction of the coil; The abnormality determination unit includes a differential image generation unit that generates a differential image of brightness between the radially smoothed image generated by the smoothing processing unit and the circumferentially smoothed image generated by the smoothing processing unit, and a determination unit that determines whether or not there is an abnormality in an edge portion based on the differential image generated by the differential image generation unit. Coiled metal strip edge abnormality detection device.

3. An image acquisition unit that acquires an image of an edge portion of a coiled metal strip from an axial direction; a smoothing processing unit that generates a smoothed image by smoothing the image acquired by the image acquisition unit; an abnormality determination unit that determines whether or not there is an abnormality in an edge portion based on the smoothed image generated by the smoothing processing unit, the smoothing processing unit generates a first angle range smoothed image as one of the smoothed images by smoothing a metal band region image, which includes at least the metal band in the image acquired by the image acquisition unit, along the circumferential direction of the coil within a predetermined first angle range, and generates a second angle range smoothed image as another of the smoothed images by smoothing the metal band region image along the circumferential direction of the coil within a predetermined second angle range different from the first angle range; the abnormality determination unit includes a difference image generation unit that generates a difference image of brightness between the first angle-smoothed image generated by the smoothing processing unit and the second angle-smoothed image generated by the smoothing processing unit, and a determination unit that determines the presence or absence of an abnormality in an edge portion based on the difference image generated by the difference image generation unit. Coiled metal strip edge abnormality detection device.

4. An image acquisition unit that acquires an image of an edge portion of a coiled metal strip from an axial direction; a smoothing processing unit that generates a smoothed image by smoothing the image acquired by the image acquisition unit; an abnormality determination unit that determines whether or not there is an abnormality in an edge portion based on the smoothed image generated by the smoothing processing unit, the smoothing processing unit generates a first-time smoothed image as one of the smoothed images by repeatedly smoothing a metal band region image, which includes at least the metal band in the image acquired by the image acquisition unit, a predetermined first number of times along the circumferential direction of the coil, and generates a second-time smoothed image as another of the smoothed images by repeatedly smoothing the metal band region image, which is a predetermined second number of times different from the first number of times, along the circumferential direction of the coil; The abnormality determination unit includes a difference image generation unit that generates a difference image of brightness between the first-time smoothed image generated by the smoothing processing unit and the second-time smoothed image generated by the smoothing processing unit, and a determination unit that determines whether or not there is an abnormality in an edge portion based on the difference image generated by the difference image generation unit. Coiled metal strip edge abnormality detection device.

5. the smoothing processing unit performs polar coordinate conversion on the image acquired by the image acquisition unit and then performs smoothing processing on the image.

5. The coiled metal strip edge abnormality detection device according to claim 1.

6. an image acquisition step of acquiring an image of an edge portion of the coiled metal strip from an axial direction; a smoothing processing step of generating a smoothed image by smoothing the image acquired in the image acquisition step; an abnormality determination step of determining whether or not there is an abnormality in an edge portion based on the smoothed image generated in the smoothing processing step, the smoothing process includes smoothing a metal band region image, which includes at least the metal band, in the image acquired in the image acquisition process along the circumferential direction of the coil to generate a circumferentially smoothed image as the smoothed image; The abnormality determination step includes a difference image generation step of generating a difference image of brightness between the metal band region image and the circumferentially smoothed image generated in the smoothing processing step, and a determination step of determining whether or not there is an abnormality in an edge portion based on the difference image generated in the difference image generation step. A method for detecting anomalies on the edge of a coiled metal strip.

7. An image acquisition step of acquiring an image of an edge portion of a coiled metal strip taken from an axial direction; a smoothing processing step of generating a smoothed image by smoothing the image acquired in the image acquisition step; an abnormality determination step of determining whether or not there is an abnormality in an edge portion based on the smoothed image generated in the smoothing processing step, The smoothing process includes smoothing a metal band region image, which includes at least the metal band, in the image acquired in the image acquisition process along the radial direction of the coil to generate a radially smoothed image as one of the smoothed images, and smoothing the generated radially smoothed image along the circumferential direction of the coil to generate a circumferentially smoothed image as one of the smoothed images, The abnormality determination step includes a difference image generation step of generating a difference image of brightness between the radially smoothed image generated in the smoothing processing step and the circumferentially smoothed image generated in the smoothing processing step, and a determination step of determining whether or not there is an abnormality in an edge portion based on the difference image generated in the difference image generation step. A method for detecting anomalies on the edge of a coiled metal strip.

8. On the computer, an image acquisition step of acquiring an image of an edge portion of the coiled metal strip from an axial direction; a smoothing processing step of generating a smoothed image by smoothing the image acquired in the image acquisition step; and an abnormality determination step of determining whether or not there is an abnormality in an edge portion based on the smoothed image generated in the smoothing processing step, the smoothing process includes smoothing a metal band region image, which includes at least the metal band, in the image acquired in the image acquisition process along the circumferential direction of the coil to generate a circumferentially smoothed image as the smoothed image; The abnormality determination step includes a difference image generation step of generating a difference image of brightness between the metal band region image and the circumferentially smoothed image generated in the smoothing processing step, and a determination step of determining whether or not there is an abnormality in an edge portion based on the difference image generated in the difference image generation step. Coiled metal strip edge anomaly detection program.

9. the smoothing processing unit generates a third angle range smoothed image as one of the smoothed images by smoothing a metal band region image, which includes at least the metal band in the image acquired by the image acquisition unit, along the circumferential direction of the coil within a predetermined third angle range, and generates a fourth angle range smoothed image as another of the smoothed images by smoothing the metal band region image along the circumferential direction of the coil within a predetermined fourth angle range different from the third angle range; further comprising an abnormality size processing unit that calculates the size of an abnormality in an edge portion based on the third and fourth angle range smoothed images generated by the smoothing processing unit.

5. The coiled metal strip edge abnormality detection device according to claim 1.

10. There are a plurality of combinations of the third angle range and the fourth angle range that are different from each other, the smoothing processing unit generates the third and fourth angle range smoothed images for each of the plurality of combinations; the anomaly size processing unit calculates the size of the anomaly for each of the plurality of combinations; 10. The coiled metal strip edge abnormality detection device according to claim 9.

11. the smoothing processing unit generates a third-time smoothed image as one of the smoothed images by repeatedly smoothing a metal band region image, which includes at least the metal band in the image acquired by the image acquisition unit, a predetermined third number of times along the circumferential direction of the coil, and generates a fourth-time smoothed image as another of the smoothed images by repeatedly smoothing the metal band region image a fourth predetermined number of times different from the third number of times along the circumferential direction of the coil; further comprising an abnormality size processing unit that calculates the size of an abnormality in an edge portion based on the third and fourth smoothed images generated by the smoothing processing unit.

5. The coiled metal strip edge abnormality detection device according to claim 1.

12. There are a plurality of different combinations of the third number of times and the fourth number of times, the smoothing processing unit generates the third and fourth smoothed images for each of the plurality of combinations, the anomaly size processing unit calculates the size of the anomaly for each of the plurality of combinations; 12. The coiled metal strip edge abnormality detection device according to claim 11.

Images