CN112819745B - Nut kernel center worm-eating defect detection method and device - Google Patents
Nut kernel center worm-eating defect detection method and device Download PDFInfo
- Publication number
- CN112819745B CN112819745B CN201911054137.4A CN201911054137A CN112819745B CN 112819745 B CN112819745 B CN 112819745B CN 201911054137 A CN201911054137 A CN 201911054137A CN 112819745 B CN112819745 B CN 112819745B
- Authority
- CN
- China
- Prior art keywords
- detected
- image
- area
- central
- detection
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000001514 detection method Methods 0.000 title claims abstract description 110
- 230000007547 defect Effects 0.000 title claims abstract description 88
- 239000000463 material Substances 0.000 claims abstract description 231
- 230000007797 corrosion Effects 0.000 claims abstract description 85
- 238000005260 corrosion Methods 0.000 claims abstract description 85
- 238000000034 method Methods 0.000 claims abstract description 52
- 230000003628 erosive effect Effects 0.000 claims description 25
- 230000000994 depressogenic effect Effects 0.000 claims description 12
- 230000008569 process Effects 0.000 claims description 7
- 230000011218 segmentation Effects 0.000 claims description 7
- 230000009467 reduction Effects 0.000 claims description 3
- 235000014571 nuts Nutrition 0.000 description 38
- 238000010586 diagram Methods 0.000 description 8
- 244000144725 Amygdalus communis Species 0.000 description 3
- 235000020224 almond Nutrition 0.000 description 3
- 239000003086 colorant Substances 0.000 description 2
- 238000004891 communication Methods 0.000 description 2
- 238000003384 imaging method Methods 0.000 description 2
- 238000007689 inspection Methods 0.000 description 2
- 235000011437 Amygdalus communis Nutrition 0.000 description 1
- 235000017060 Arachis glabrata Nutrition 0.000 description 1
- 244000105624 Arachis hypogaea Species 0.000 description 1
- 235000010777 Arachis hypogaea Nutrition 0.000 description 1
- 235000018262 Arachis monticola Nutrition 0.000 description 1
- 241000238631 Hexapoda Species 0.000 description 1
- 241000208467 Macadamia Species 0.000 description 1
- 240000006711 Pistacia vera Species 0.000 description 1
- 235000003447 Pistacia vera Nutrition 0.000 description 1
- 241000353135 Psenopsis anomala Species 0.000 description 1
- 238000010521 absorption reaction Methods 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 239000007769 metal material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 235000020232 peanut Nutrition 0.000 description 1
- 235000020233 pistachio Nutrition 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/73—Deblurring; Sharpening
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10116—X-ray image
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Quality & Reliability (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Analysing Materials By The Use Of Radiation (AREA)
Abstract
The invention discloses a nut kernel center worm corrosion defect detection method and device, wherein the method comprises the following steps: acquiring an image to be detected, wherein the image to be detected is an external rectangular X-ray image of a material to be detected; carrying out central worm corrosion detection on the material to be detected according to the image to be detected, wherein the central worm corrosion detection comprises the following steps: and carrying out sharpening, corrosion and binarization on the image to be detected to obtain a first binarized image, and judging whether the material to be detected has a central wormhole defect or not according to the area of the inner contour in the first binarized image. According to the method, an external rectangular X-ray image of a material to be detected is used as an image to be detected, sharpening, corrosion and binarization processing are carried out on the image to be detected to obtain an inner contour in the image to be detected, and whether a central wormhole defect exists in the material to be detected is judged according to the area of the inner contour, so that the internal wormhole defect can be detected. The method has the advantages of high detection efficiency, high accuracy, automatic detection and the like.
Description
Technical Field
The invention relates to the technical field of X-ray detection, in particular to a method and a device for detecting the central worm erosion defect of nut kernels.
Background
In recent years, nuts have become popular and their sales have increased year by year. However, nut kernels (such as almond kernels, almonds and the like) are extremely easy to grow insects, and if worm-eaten nuts of nuts cannot be effectively sorted before being sold, extremely bad experience can be brought to consumers, and reputation and market of sellers are affected.
The X-ray inspection is an inspection method which can penetrate materials by utilizing X-rays, and can obtain X-ray images according to different absorption and scattering effects of the materials on the X-rays so as to judge the internal defect conditions of the materials. At present, X-ray flaw detection is often applied to the fields of metal material flaw detection, rubber material flaw detection such as tires, timber flaw detection and the like. However, there is no accurate and reliable detection method for the defect of worm erosion in the nut kernels.
Disclosure of Invention
The present invention is directed to solving, at least in part, one of the technical problems in the related art. Therefore, the invention aims to provide a method and a device for detecting the wormhole corrosion defect of the nut kernel center. The method can be used for efficiently detecting the internal wormhole defect of the nut kernels, is high in detection accuracy and can realize automatic detection.
In one aspect of the invention, the invention provides a method for detecting the wormhole corrosion defect of a nut kernel, which is characterized by comprising the following steps:
acquiring an image to be detected, wherein the image to be detected is an external rectangular X-ray image of a material to be detected;
carrying out central worm corrosion detection on the material to be detected according to the image to be detected, wherein the central worm corrosion detection comprises the following steps: and carrying out sharpening, corrosion and binarization on the image to be detected to obtain a first binarized image, and judging whether the material to be detected has a central wormhole defect or not according to the area of the inner contour in the first binarized image.
According to the method, an external rectangular X-ray image of a material to be detected is used as an image to be detected, sharpening, corrosion and binarization processing are carried out on the image to be detected to obtain an inner contour in the image to be detected, and whether a central wormhole defect exists in the material to be detected is judged according to the area of the inner contour, so that the internal wormhole defect can be detected. The method has the advantages of high detection efficiency, high accuracy, automatic detection and the like.
In addition, the method for detecting the wormhole defect in the nut kernel according to the embodiment of the invention can also have the following additional technical characteristics:
in some embodiments of the present invention, the determining whether the material to be detected has a central wormhole defect according to the area of the inner contour in the first binarized image includes:
and if the area of the inner contour is larger than a preset area threshold value, determining that the material to be detected has a central worm damage defect.
In some embodiments of the present invention, before the step of performing the central worm erosion detection on the material to be detected according to the image to be detected, the method further includes:
judging whether the materials to be detected in the image to be detected are adhered or not;
the step of detecting the central worm corrosion of the material to be detected according to the image to be detected comprises the following steps:
and under the condition that adhesion does not exist, performing central worm corrosion detection on the material to be detected according to the image to be detected.
In another aspect of the present invention, an embodiment of the present invention provides a method for detecting a wormhole defect in a nut kernel, which is characterized by including:
acquiring a to-be-detected image of a to-be-detected material, wherein the to-be-detected image is an external rectangular X-ray image of the to-be-detected material;
judging whether the materials to be detected in the image to be detected are adhered or not;
if adhesion exists, segmenting the material to be detected in the image to be detected, and intercepting the circumscribed rectangular X-ray image of the material to be detected again to obtain the image to be detected;
judging whether the material to be detected is cut or not according to the image to be detected;
if the material to be detected is cut, carrying out central worm corrosion detection on the material to be detected according to the image to be detected, wherein the central worm corrosion detection comprises the following steps: and carrying out sharpening, corrosion and binarization on the image to be detected to obtain a first binarized image, and judging whether the material to be detected has a central wormhole defect or not according to the area of the inner contour in the first binarized image.
According to the method, an external rectangular X-ray image of a material to be detected is used as an image to be detected, whether the condition that nuts and kernels are adhered to each other exists in the material to be detected is judged at first, when the material to be detected is adhered, the image is divided, the divided image is used as the image to be detected, the image to be detected is sharpened, corroded and binarized, so that an inner contour in the image to be detected is obtained, and whether a central wormhole defect exists in the material to be detected is judged according to the area of the inner contour, and therefore the internal wormhole defect can be detected. The method has the advantages of high detection efficiency, high accuracy and the like.
In addition, the method for detecting the wormhole defect in the nut kernel according to the embodiment of the invention can also have the following additional technical characteristics:
in some embodiments of the present invention, the determining whether the material to be detected has a central wormhole defect according to the area of the inner contour in the first binarized image includes:
and if the area of the inner contour is larger than a preset area threshold value, determining that the material to be detected has a central worm damage defect.
In some embodiments of the present invention, before performing the central worm erosion detection on the material to be detected according to the image to be detected, the method further includes:
sequentially carrying out binarization processing and convex hull processing on the image to be detected to respectively obtain a second binarization image and a convex hull image, and subtracting the convex hull image from the second binarization image to obtain a concave region;
the step of performing central worm erosion detection on the material to be detected according to the image to be detected comprises the following steps of:
if the area of the depressed area is smaller than a first preset threshold value and larger than a second preset threshold value, the number of the depressed areas is more than a preset number, after line drawing processing is carried out on the image to be detected, central worm corrosion detection is carried out on the material to be detected according to the image to be detected, and otherwise, central worm corrosion detection is directly carried out on the material to be detected according to the image to be detected.
In some embodiments of the invention, the method further comprises:
and if the central wormhole is not separated, performing the central wormhole detection on the material to be detected according to the image to be detected.
In some embodiments of the present invention, the step of determining whether the materials to be detected in the image to be detected are adhered includes:
judging whether the size of the image to be detected exceeds a preset size threshold value or not, and if so, judging that the material to be detected is adhered; otherwise, judging that the materials to be detected are not adhered.
In some embodiments of the invention, the segmenting comprises: and sequentially carrying out binarization treatment, corrosion treatment and expansion treatment on the image to be detected so as to segment the material to be detected in the image to be detected.
In some embodiments of the invention, the line drawing process comprises: and drawing lines on the minimum distance between the concave region with the largest area and the concave region with the second largest area.
In some embodiments of the invention, a second recessed area is obtained after drawing a line;
and judging whether the line is drawn successfully or not according to the recessed regions and the second recessed regions, if not, determining the distance between the vertexes of the second recessed regions with the area larger than a second preset threshold value, and drawing the line between the two vertexes corresponding to the minimum distance.
In another aspect of the present invention, an apparatus for detecting a worm damage defect in a nut kernel is provided in an embodiment of the present invention, including:
the acquisition module is used for acquiring an image to be detected, wherein the image to be detected is an external rectangular X-ray image of a material to be detected;
the detection module is used for carrying out central worm corrosion detection on the material to be detected according to the image to be detected, and the central worm corrosion detection comprises the following steps: and carrying out sharpening, corrosion and binarization on the to-be-detected image to obtain a first binarized image, and judging whether the to-be-detected material has a central wormhole defect or not according to the area of the inner contour in the first binarized image.
In another aspect of the present invention, an apparatus for detecting wormhole defects in nut kernels is provided in the embodiments of the present invention, including:
the device comprises an acquisition module, a detection module and a processing module, wherein the acquisition module is used for acquiring a to-be-detected image of a to-be-detected material, and the to-be-detected image is an external rectangular X-ray image of the to-be-detected material;
the first judgment module is used for judging whether the materials to be detected in the image to be detected are adhered or not;
the segmentation module is used for segmenting the material to be detected in the image to be detected when the judgment result of the first judgment module is that adhesion exists, and intercepting the circumscribed rectangle X-ray image of the material to be detected again to obtain the image to be detected;
the second judgment module is used for judging whether the material to be detected is cut according to the image to be detected;
the detection module is used for performing central worm erosion detection on the material to be detected according to the image to be detected when the judgment result of the second judgment module is that the material to be detected is divided, and the central worm erosion detection comprises the following steps: and carrying out sharpening, corrosion and binarization on the image to be detected to obtain a first binarized image, and judging whether the material to be detected has a central wormhole defect or not according to the area of the inner contour in the first binarized image.
Additional aspects and advantages of the invention will be set forth in part in the description which follows and, in part, will be obvious from the description, or may be learned by practice of the invention.
Drawings
The above and/or additional aspects and advantages of the present invention will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings of which:
FIG. 1 is a schematic flow diagram of a nut kernel core worm damage defect detection method according to an embodiment of the present invention;
FIG. 2 is an illustration of various images involved in a central worm erosion detection process in accordance with one embodiment of the present invention;
FIG. 3 is a schematic flow diagram of a nut kernel core worm damage defect detection method according to an embodiment of the present invention;
FIG. 4 is a schematic flow diagram of a nut kernel core worm damage defect detection method according to an embodiment of the present invention;
FIG. 5 is a schematic flow diagram of a nut kernel core worm damage defect detection method according to an embodiment of the present invention;
FIG. 6 is a diagram of various images involved in convex hull processing, according to one embodiment of the invention;
FIG. 7 is a representation of the various images involved in the convex hull processing according to one embodiment of the present invention;
FIG. 8 is a schematic flow chart diagram of a method for detecting a kernel core worm damage defect in accordance with one embodiment of the present invention; a
FIG. 9 is a representation of various images involved in a segmentation process according to one embodiment of the present invention;
FIG. 10 is an illustration of various images involved in drawing lines in accordance with one embodiment of the present invention;
FIG. 11 is a schematic diagram of a nut-kernel wormhole defect detection apparatus according to one embodiment of the present invention;
FIG. 12 is a schematic diagram of a nut-kernel wormhole defect detection apparatus according to one embodiment of the present invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference to the drawings are illustrative and intended to be illustrative of the invention and are not to be construed as limiting the invention.
Furthermore, the terms "first," "second," "third," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or to implicitly indicate the number of technical features indicated. Thus, a feature defined as "first," "second," "third," etc. may explicitly or implicitly include at least one of the feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In addition, the specific type of image processing software used in the detection method proposed by the present invention is not particularly limited, and MATLAB, openCV, or the like may be used, for example. In addition, in the present invention, the term "binarization processing" refers to setting, using image processing software, a pixel gradation value larger than a preset threshold value in an image to be processed as a gradation maximum value (i.e., black), and a pixel gradation value in the image to be processed, which gradation value is smaller than or equal to the preset threshold value, as a gradation minimum value (i.e., white).
In embodiments of the present invention, the nut kernels may include almonds, peanut kernels, melon seeds, macadamia nuts, pistachio nuts, and the like, and are not particularly limited herein. The present invention will be described in detail below with reference to nut kernels as an example.
In one aspect of the invention, the invention provides a method for detecting the wormhole defect in the center of nut kernels, which is used for detecting whether the wormhole defect exists in the nut kernels. According to an embodiment of the invention, with reference to fig. 1, the method comprises:
s11: and acquiring an image to be detected of the material to be detected, wherein the image to be detected is an external rectangular X-ray image of the material to be detected.
In the step, an X-ray image of a material to be detected is obtained through an imaging device of the X-ray detection equipment, and the material to be detected is intercepted in the X-ray image by utilizing an external rectangle, so that the image to be detected is obtained. According to some embodiments of the invention, the method for intercepting the material to be detected by using the circumscribed rectangle comprises the following steps: firstly, carrying out binarization processing on an obtained X-ray image, then extracting the outline of a material to be detected, obtaining a rectangle along the edge of the outline to obtain the external rectangle, and intercepting the material to be detected from the X-ray image by using the external rectangle. By adopting the method, the image to be detected of the material to be detected is obtained by intercepting the image to be detected by the external rectangle, so that the interference of other materials can be effectively eliminated, and the misjudgment caused by material adhesion can be reduced.
S12: carrying out central worm corrosion detection on the material to be detected according to the image to be detected, wherein the central worm corrosion detection comprises the following steps: and carrying out sharpening, corrosion and binarization on the image to be detected to obtain a first binarized image, and judging whether the material to be detected has a central wormhole defect or not according to the area of the inner contour in the first binarized image.
It should be noted that the detected central worm damage defect may be an external worm damage defect of the nut kernel, or may be an internal worm damage defect of the nut kernel.
In the step, the central wormhole corrosion detection is carried out on the material to be detected according to the image to be detected, if the central wormhole corrosion defect is found in the material to be detected, the wormhole corrosion defect is determined to exist in the material to be detected, and the detection on the image to be detected can be finished.
According to some embodiments of the present invention, the erosion radius used in the erosion image may be 0 to 30. According to an implementation manner of the embodiment of the invention, after the material contour, namely the contour corresponding to the maximum connected domain in the sharpened image is obtained, the interior of the contour can be filled with black, and the black part can be corroded. In addition, after the material contour is obtained from the sharpened image, the inside of the contour is filled with white, and the white part can be corroded. And (5) the outline of the corroded material becomes small, and a central area image corresponding to the corroded outline of the material is taken from the sharpened image to be detected. And (4) carrying out binarization processing on the central region image, extracting the inner contour of the central region, and judging the central worm damage according to the area of the inner contour. Specifically, referring to fig. 2, fig. 2a is an image to be detected, fig. 2b is a sharpened image obtained by sharpening the image to be detected, fig. 2c is a corroded image obtained by corroding a black part after filling black in the inside of the outline in the sharpened image, and fig. 2d is a binarized image obtained by binarizing the sharpened image according to the corroded material outline.
Therefore, by adopting corrosion treatment in the central wormhole corrosion detection, the interference of external wormhole corrosion defects possibly existing in the material to be detected can be effectively avoided. Further, a sharpened image of a central area is taken out according to the sharpened image and the contour of the corroded material, binarization processing is carried out on the sharpened image, all images outside the central area are filled into colors different from the inner contour in the central area by setting a threshold value of the binarization processing, if the area in the inner contour is white and the rest is black, or the area in the inner contour is black and the rest is white, the area of an internal communication area is obtained for the binarized central area image, namely the area closest to the central worm erosion part, namely the area of the inner contour, and if the area is within a preset area threshold value range of the central worm erosion defect, the area is determined to be the central worm erosion defect.
According to the method, an external rectangular X-ray image of a material to be detected is used as an image to be detected, sharpening, corrosion and binarization processing are carried out on the image to be detected to obtain an inner contour in the image to be detected, and whether a central wormhole defect exists in the material to be detected is judged according to the area of the inner contour, so that the internal wormhole defect can be detected. The method has the advantages of high detection efficiency, high accuracy, automatic detection and the like.
According to an embodiment of the invention, referring to fig. 3, the method for detecting the wormhole corrosion defect of the nut kernel provided by the invention comprises the following steps:
s31: and acquiring an image to be detected of the material to be detected, wherein the image to be detected is an external rectangular X-ray image of the material to be detected.
S32: judging whether the materials to be detected in the image to be detected have adhesion, if not, executing S33
Specifically, the step of judging whether the material to be detected in the image to be detected has adhesion comprises the following steps: if the size of the circumscribed rectangular X-ray image of the material to be detected exceeds a preset size threshold, judging that the material to be detected is adhered; otherwise, judging that the materials to be detected are not adhered. The above-mentioned size refers to at least one of the length, width and area of the circumscribed rectangular X-ray image, i.e., the image to be detected. For example, the size for judging adhesion includes length, width and area, if the length, width and area of the circumscribed rectangle X-ray image exceed the corresponding length threshold, width threshold and area threshold, it is determined that the material to be detected has adhesion, otherwise, it is determined that the material to be detected does not have adhesion.
According to some embodiments of the present invention, the threshold value for the length of the circumscribed rectangular X-ray image may be 60-70 pixels, and the threshold value for the width of the circumscribed rectangular X-ray image may be 60-70 pixels.
S33: carrying out central worm corrosion detection on the material to be detected according to the image to be detected, wherein the central worm corrosion detection comprises the following steps: and carrying out sharpening, corrosion and binarization on the image to be detected to obtain a binarized image, and judging whether the material to be detected has a central wormhole defect or not according to the area of the inner contour in the binarized image.
S31 is the same as S11, S33 is the same as S12, and the explanation of S31 and S33 can refer to the corresponding parts, which are not described herein again.
According to the method, through judgment of adhesion, when adhesion does not exist, central wormhole corrosion detection is carried out, misjudgment caused by adhesion can be eliminated, specifically, an external rectangular X-ray image of a material to be detected is used as an image to be detected, sharpening, corrosion and binaryzation are carried out on the image to be detected, an inner contour in the image to be detected is obtained, and whether a central wormhole corrosion defect exists in the material to be detected is judged according to the area of the inner contour, so that the internal wormhole corrosion defect can be detected. The method has the advantages of high detection efficiency, high accuracy, automatic detection and the like.
In another aspect of the invention, the invention also provides a method for detecting the worm corrosion defect of the nut kernel center. According to an embodiment of the invention, referring to fig. 4, the method comprises:
s41: and acquiring an image to be detected of the material to be detected, wherein the image to be detected is an external rectangular X-ray image of the material to be detected.
In the step, an X-ray image of the material to be detected is obtained through an imaging device of the X-ray detection equipment, the material to be detected is targeted in the X-ray image, and the material to be detected is intercepted by utilizing an external rectangle, so that the image to be detected is obtained. According to some embodiments of the invention, a method for intercepting a material to be detected by using a circumscribed rectangle comprises: firstly, carrying out binarization processing on an obtained X-ray image, then extracting the outline of a material to be detected, obtaining a rectangle along the edge of the outline to obtain the external rectangle, and intercepting the material to be detected from the X-ray image by using the external rectangle. By adopting the method, the image to be detected of the material to be detected is obtained by intercepting the image to be detected by the external rectangle, so that the interference of other materials can be effectively eliminated, and the misjudgment caused by adhesion of the materials is reduced.
S42: judging whether the materials to be detected in the image to be detected have adhesion, if so, executing S43
Specifically, the step of judging whether the material to be detected in the image to be detected has adhesion comprises the following steps: if the size of the external rectangular X-ray image of the material to be detected exceeds a preset size threshold value, judging that the material to be detected is adhered; otherwise, judging that the materials to be detected are not adhered. The above-mentioned size refers to at least one of the length, width and area of the circumscribed rectangular X-ray image, i.e., the image to be detected. For example, the size for judging adhesion includes length, width and area, if the length, width and area of the circumscribed rectangle X-ray image exceed the corresponding length threshold, width threshold and area threshold, it is determined that the material to be detected has adhesion, otherwise, it is determined that the material to be detected does not have adhesion.
S43: and segmenting the material to be detected in the image to be detected, and intercepting the external rectangular X-ray image of the material to be detected again to obtain the image to be detected.
Segmenting the materials to be detected in the images to be detected, comprising: and carrying out binarization processing, corrosion processing and expansion processing on the image to be detected. Specifically, binarization processing is carried out on a pre-detected image to obtain a binarization image; acquiring the outline of the maximum connected domain in the binary image, filling white or black in the outline area, carrying out corrosion treatment on the black area or the white area, acquiring the corroded outline, carrying out expansion treatment on the corroded outline, acquiring the expanded outline, acquiring the external rectangle of the expanded outline, and intercepting the area corresponding to the expanded outline in the pre-detected image by using the external rectangle, namely the re-intercepted image to be detected. Referring to fig. 9, fig. 9a is a predicted image to be detected with material adhesion, fig. 9b is an image obtained by performing corrosion expansion processing on the predicted image to be detected, and a small frame in fig. 9c is a rectangle obtained by cutting again, wherein the frame is selected as the image to be detected.
According to an embodiment of the invention, the parameters used in the above expansion include the expansion radius, i.e. how many turns the expansion takes, the operation is aimed at obtaining a rectangular frame after segmentation, and the value may be in the range of 35 to 42.
S44: and judging whether the material to be detected is cut or not according to the image to be detected, and executing S45 if the material to be detected is cut.
The step of judging whether the material to be detected is cut comprises the following steps: if the size of the circumscribed rectangular X-ray image of the material to be detected exceeds a preset size threshold, judging that the material to be detected is not cut, namely, the material to be detected in the image to be detected is still adhered; otherwise, judging that the material to be detected is cut, namely the material to be detected in the image to be detected does not adhere. The above-mentioned size refers to at least one of the length, width and area of the circumscribed rectangular X-ray image, i.e., the image to be detected. For example, the size for judging adhesion comprises length, width and area, if the length, width and area of the external rectangular X-ray image exceed the corresponding length threshold, width threshold and area threshold, the material to be detected is judged not to be cut, otherwise, the material to be detected in the image to be detected is judged to be cut.
S45: carrying out central worm corrosion detection on the material to be detected according to the image to be detected, wherein the central worm corrosion detection comprises the following steps: and carrying out sharpening, corrosion and binarization on the image to be detected to obtain a first binarized image, and judging whether the material to be detected has a central wormhole defect or not according to the area of the inner contour in the first binarized image.
In the step, the central wormhole corrosion detection is carried out on the material to be detected according to the image to be detected, if the central wormhole corrosion defect exists in the material to be detected, the wormhole corrosion defect exists in the material to be detected, and the detection aiming at the image to be detected can be finished.
According to an implementation manner of the embodiment of the invention, after the material contour, namely the contour corresponding to the maximum connected domain in the sharpened image is obtained, the interior of the contour can be filled with black, and the black part can be corroded. In addition, after the material contour is obtained from the sharpened image, the inside of the contour can be filled with white, and the white part can be corroded. And (5) the outline of the corroded material becomes small, and a central area image corresponding to the corroded outline of the material is taken from the sharpened image to be detected. And (4) carrying out binarization processing on the central region image, extracting the inner contour of the central region, and judging the central worm damage according to the area of the inner contour.
Therefore, by adopting corrosion treatment in the central wormhole corrosion detection, the interference of external wormhole corrosion defects possibly existing in the material to be detected can be effectively avoided. Further, a sharpened image of a central area is taken out according to the sharpened image and the contour of the corroded material, binarization processing is carried out on the sharpened image, all images outside the central area are filled into colors different from the inner contour in the central area by setting a threshold value of the binarization processing, if the area in the inner contour is white and the rest is black or the area in the inner contour is black and the rest is white, the area of an internal communication area is obtained, the area which is the area closest to central worm erosion, namely the area of the inner contour, is the area of the binarized central area image, and if the area is within a preset area threshold value range of the central worm erosion defect, the area is determined to be the central worm erosion defect.
When the materials to be detected are judged to be adhered, firstly, the materials to be detected are subjected to segmentation treatment, and the central worm erosion detection is carried out on the segmented materials to be detected, so that misjudgment caused by adhesion of the materials can be eliminated.
In another aspect of the invention, the invention also provides a method for detecting the wormhole corrosion defect of the nut kernel. According to an embodiment of the invention, referring to fig. 5, the method comprises:
s51: and acquiring an image to be detected of the material to be detected, wherein the image to be detected is an external rectangular X-ray image of the material to be detected. S51 is the same as S41, and details are not repeated herein, and reference may be made to the corresponding contents above.
S52: and judging whether the materials to be detected in the image to be detected are adhered or not. Judging whether the materials to be detected in the image to be detected are adhered or not; if there is sticking, the subsequent step S53 is performed. S52 is the same as S42, and is not described herein again, and reference may be made to the above corresponding contents.
S53: and segmenting the material to be detected in the image to be detected, and intercepting the external rectangular X-ray image of the material to be detected again to obtain the image to be detected. S53 is the same as S43, and is not described herein, and reference may be made to the above contents.
S54: and judging whether the material to be detected is cut or not according to the image to be detected, and executing S55 if the material to be detected is cut. S54 is the same as S44, and will not be described herein, and reference may be made to the above corresponding contents.
S55: sequentially carrying out binarization processing and convex hull processing on an image to be detected, respectively obtaining a second binarization image and a convex hull image, and subtracting the obtained convex hull image from the second binarization image to obtain a concave region; and if the number of the depressed regions with the areas larger than the second preset threshold is more than the preset number, performing line drawing on the image to be detected, and then performing S56. Therefore, the central worm damage defect detection and the external worm damage defect detection of the image to be detected are carried out on the maximum connected domain in the image to be detected, the plurality of sunken areas in the image are further divided into the plurality of areas through the drawing, the subsequent detection result can be influenced through the drawing processing, and the accuracy of the detection result can be further improved. As shown in fig. 10, fig. 10a is a cut image to be detected, and fig. 10b is a material image after line changing, and when central worm damage detection and/or external worm damage detection are/is subsequently performed, the largest connected domain in the material image after line drawing is processed.
According to an embodiment of the present invention, referring to fig. 6 and 7, fig. 6a and 7a are the images to be detected acquired according to S53, respectively. And (3) sequentially carrying out binarization processing and convex hull processing on the material in fig. 6a to obtain fig. 6b and fig. 6c, and subtracting fig. 6b from fig. 6c to obtain fig. 6d, namely the concave area outside the material in fig. 6. And (3) sequentially carrying out binarization processing and convex hull processing on the material in the graph 7a to obtain a graph 7b and a graph 7c respectively, and subtracting the graph 7b from the graph 7c to obtain a graph 7d, namely a concave region outside the material in the graph 7.
According to an embodiment of the present invention, the scribe line processing includes: drawing a line on the minimum distance between the concave region with the largest area and the concave region with the next largest area, and obtaining a plurality of second concave regions; and if the area of the second depressed region is obviously changed relative to the depressed regions, the line drawing is considered to be successful, and the line drawing is finished, for example, relative to the depressed regions, the area reduction amplitude of the second depressed regions exceeds a preset area ratio, or the number of the second depressed regions exceeds a preset number ratio, the line drawing is considered to be successful. And if the line drawing is unsuccessful, determining the distances between the vertexes of the plurality of second sunken areas with the areas larger than a second preset threshold value, and drawing the line between the two vertexes corresponding to the minimum distance.
S56: and performing central worm corrosion detection on the material to be detected according to the image to be detected, wherein the manner of performing central worm corrosion detection on the image to be detected in this step is the same as that in S45, and reference may be made to the above-mentioned corresponding contents, which is not described herein again.
According to the embodiment of the present invention, when the number of the depressed regions having the area smaller than the first preset threshold and larger than the second preset threshold is a preset number, the central worm corrosion of the material to be detected is detected according to the image to be detected, and the manner of detecting the central worm corrosion in this step is the same as that in S45 described above, which may specifically refer to the above corresponding contents, and is not repeated herein.
Further, if the number of the depressed areas with the areas smaller than a first preset threshold and larger than a second preset threshold is a preset number, the central worm corrosion detection is directly carried out on the material to be detected according to the image to be detected.
Furthermore, if the adhesion of the materials is not divided, the central worm erosion detection is directly carried out on the materials to be detected according to the images to be detected.
For ease of understanding, a nut kernel core worm damage defect detection method according to one specific example of the invention is described below with reference to fig. 8:
s81: acquiring a to-be-detected image of a to-be-detected material, wherein the to-be-detected image is an external rectangular X-ray image of the to-be-detected material;
s82: judging whether the materials to be detected in the image to be detected are adhered or not, if not, executing S83 to detect the central worm erosion defect of the image to be detected; if sticking exists, S84 is executed.
S83: taking the image to be detected as the image to be detected, and carrying out central worm corrosion detection on the material to be detected according to the image to be detected, wherein the central worm corrosion detection comprises the following steps: and carrying out sharpening, corrosion and binarization on the image to be detected to obtain a first binarized image, and judging whether the material to be detected has a central wormhole defect or not according to the area of the inner contour in the first binarized image.
S84: and segmenting the material to be detected in the image to be detected, and intercepting the external rectangular X-ray image of the material to be detected again to obtain the image to be detected.
S85: and judging whether the material to be detected is divided or not according to the image to be detected, if not, executing S86, and if so, executing S87.
S86: and carrying out central worm corrosion detection on the material to be detected according to the image to be detected.
S87: sequentially carrying out binarization processing and convex hull processing on an image to be detected to respectively obtain a second binarization image and a convex hull image, and subtracting the convex hull image from the second binarization image to obtain a concave region;
if the number of the recessed regions with areas larger than the second predetermined threshold is larger than the predetermined number, S88 is executed, and if the number of the recessed regions with areas larger than the second predetermined threshold is the predetermined number, S89 is executed.
S88: the line drawing process is performed on the image to be detected, and then S89 is performed.
S89: carrying out central worm corrosion detection on the material to be detected according to the image to be detected, wherein the central worm corrosion detection comprises the following steps: and carrying out sharpening, corrosion and binaryzation on the image to be detected to obtain a binaryzation image, and judging whether the material to be detected has a central wormhole defect or not according to the area of the inner contour in the binaryzation image.
According to the method, the adhesion is judged, the adhesion and the adhesion are separately processed, comprehensive central worm corrosion detection can be performed, misjudgment caused by adhesion can be eliminated, specifically, an external rectangular X-ray image of a material to be detected is used as an image to be detected, the image to be detected is subjected to sharpening, corrosion and binarization, an inner contour in the image to be detected is obtained, and whether the material to be detected has a central worm corrosion defect or not is judged according to the area of the inner contour, so that the internal worm corrosion defect can be detected. The method has the advantages of high detection efficiency, high accuracy, automatic detection and the like.
It should be noted that the threshold range adopted in the binarization processing in any of the above embodiments may be 180 to 200, the preset area threshold of the central worm damage defect may be 15 to 20, the preset distance threshold may be 1 to 2 pixels, the preset number may be 1, the number that is greater than the preset number may be more than 1, that is, not less than 2, and the remaining thresholds and preset values may be set according to actual expectations for results or according to feedback results.
In addition, it should be noted that each detection method, determination method, or step described in the foregoing specific examples is as described above, and is not described herein again.
In one aspect of the invention, the invention provides a nut kernel center worm damage defect detection device which is used for detecting whether worm damage defects exist in nut kernels. Referring to fig. 11, the nut kernel center wormhole defect detecting apparatus includes:
the acquisition module 111 is used for acquiring an image to be detected, wherein the image to be detected is an external rectangular X-ray image of a material to be detected;
a detection module 112, configured to perform central worm corrosion detection on the material to be detected according to the image to be detected, where the central worm corrosion detection includes: and carrying out sharpening, corrosion and binarization on the image to be detected to obtain a first binarized image, and judging whether the material to be detected has a central wormhole defect or not according to the area of the inner contour in the first binarized image.
According to an embodiment of the present invention, the detection module 112 is configured to determine whether the material to be detected has a central wormhole defect according to an area of an inner contour in the first binarized image, and specifically, determine that the material to be detected has the central wormhole defect if the area of the inner contour is greater than a preset area threshold.
According to one embodiment of the invention, the device further comprises a judging module for judging whether the materials to be detected in the image to be detected are adhered or not; the detection module 112 is specifically configured to perform central worm erosion detection on the material to be detected according to the image to be detected under the condition that adhesion does not exist.
In one aspect of the invention, the invention provides a nut kernel center worm damage defect detection device which is used for detecting whether a nut kernel has worm damage defects or not. Referring to fig. 12, the nut kernel center worm damage defect detecting device comprises:
the acquisition module 121 is configured to acquire a to-be-detected image of a to-be-detected material, where the to-be-detected image is an external rectangular X-ray image of the to-be-detected material;
the first judging module 122 is configured to judge whether the materials to be detected in the image to be detected are adhered;
the segmentation module 123 is configured to segment the material to be detected in the image to be detected when the first determination module determines that adhesion exists, and re-intercept an external rectangular X-ray image of the material to be detected to obtain an image to be detected;
the second judging module 124 is configured to judge whether the material to be detected is cut according to the image to be detected;
a detecting module 125, configured to perform, when the determination result of the second determining module is that the material to be detected is not divided, central worm corrosion detection on the material to be detected according to the image to be detected, where the central worm corrosion detection includes: and carrying out sharpening, corrosion and binarization on the to-be-detected image to obtain a first binarized image, and judging whether the to-be-detected material has a central wormhole defect or not according to the area of the inner contour in the first binarized image.
According to an embodiment of the present invention, the detection module 125 is configured to determine whether the material to be detected has a central wormhole defect according to an area of an inner contour in the first binarized image, and specifically, determine that the material to be detected has the central wormhole defect if the area of the inner contour is greater than a preset area threshold.
According to an embodiment of the present invention, the apparatus further includes a processing module, configured to, if the second determining module 124 determines that the image to be detected is divided, sequentially perform binarization processing and convex hull processing on the image to be detected, to obtain a second binarized image and a convex hull image, respectively, and subtract the second binarized image from the convex hull image, to obtain a concave region; if the area of the sunken area is smaller than a first preset threshold value and larger than a second preset threshold value and is more than a preset number, performing line drawing processing on the image to be detected;
the detection module 125 is further configured to perform the central worm erosion detection on the material to be detected according to the image to be detected after the line drawing process.
According to an embodiment of the present invention, the processing module is further configured to perform the central worm corrosion detection on the material to be detected according to the image to be detected if the number of the recessed regions having the areas smaller than a first preset threshold and larger than a second preset threshold is a preset number.
According to an embodiment of the present invention, the first determining module 122 is specifically configured to determine whether the size of the image to be detected exceeds a preset size threshold, and if so, determine that the material to be detected is adhered; otherwise, judging that the materials to be detected are not adhered.
According to an embodiment of the present invention, the segmentation module 123 is specifically configured to sequentially perform binarization processing, erosion processing, and expansion processing on the image to be detected, so as to segment the material to be detected in the image to be detected.
According to an embodiment of the invention, the processing module is used for drawing a line, and is specifically used for drawing a minimum distance between the recessed region with the largest area and the recessed region with the next largest area.
Further, the processing module is further configured to: acquiring a second sunken area after drawing a line;
and judging whether the line is drawn successfully or not according to the recessed regions and the second recessed regions, if not, determining the distance between the vertexes of the second recessed regions with the areas larger than a second preset threshold value, and drawing the line between two vertexes corresponding to the minimum distance.
In the embodiment of the present invention, the apparatus portion corresponds to the method portion, and for the related technical explanation of this portion, reference may be made to the method embodiment portion, which is not described herein again.
In the description of the specification, reference to the description of "one embodiment," "some embodiments," "an example," "a specific example," or "some examples" or the like means that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Moreover, various embodiments or examples and features of various embodiments or examples described in this specification can be combined and combined by one skilled in the art without being mutually inconsistent.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.
Claims (6)
1. A method for detecting wormhole corrosion defects in nut kernels is characterized by comprising the following steps:
acquiring a to-be-detected image of a to-be-detected material, wherein the to-be-detected image is an external rectangular X-ray image of the to-be-detected material;
judging whether the materials to be detected in the image to be detected are adhered or not;
if the adhesion exists, the material to be detected in the image to be detected is segmented, and the circumscribed rectangle X-ray image of the material to be detected is intercepted again to obtain the image to be detected;
judging whether the material to be detected is cut or not according to the image to be detected;
if the material to be detected is cut, performing central wormhole detection on the material to be detected according to the image to be detected,
if the image to be detected is not divided, carrying out binarization processing and convex hull processing on the image to be detected in sequence to respectively obtain a second binarization image and a convex hull image, and subtracting the obtained convex hull image from the second binarization image to obtain a concave region; if the number of the depressed areas with the areas larger than a second preset threshold is more than a preset number, performing line drawing treatment on the image to be detected,
the line drawing process comprises the following steps: drawing a line on the minimum distance between the concave region with the largest area and the concave region with the second largest area, and obtaining a plurality of second concave regions; if the area reduction amplitude of the second recessed areas exceeds a preset area or the number of the second recessed areas exceeds a preset number relative to the recessed areas, the line drawing is considered to be successful; if the line drawing is unsuccessful, determining the distances between the vertexes of the second sunken regions with the areas larger than the second preset threshold value, and drawing the line between the two vertexes corresponding to the minimum distance;
and carrying out central worm erosion detection on the material to be detected according to the image to be detected after line drawing processing, wherein the central worm erosion detection comprises the following steps: sharpening the image to be detected to obtain a sharpened image, corroding the material outline part of the sharpened image, taking a central area image corresponding to the corroded material outline, carrying out binarization processing on the central area image to obtain a first binarized image, extracting an inner outline of the central area subjected to binarization processing, and judging whether the material to be detected has a central wormhole defect or not according to the area of the inner outline in the first binarized image.
2. The method according to claim 1, wherein the determining whether the material to be detected has the central wormhole defect according to the area of the inner contour in the first binarized image comprises:
and if the area of the inner contour is larger than a preset area threshold value, determining that the material to be detected has a central worm damage defect.
3. The method according to claim 1, characterized in that before the step of performing the central worm erosion detection on the material to be detected according to the pre-detection image, the method further comprises:
judging whether the materials to be detected in the image to be detected are adhered or not,
and under the condition that adhesion does not exist, carrying out central worm corrosion detection on the material to be detected according to the image to be detected.
4. The method according to claim 1, wherein the step of determining whether the materials to be detected in the image to be detected are adhered comprises:
judging whether the size of the image to be detected exceeds a preset size threshold value or not, and if so, judging that the material to be detected is adhered; otherwise, judging that the materials to be detected are not adhered.
5. The method of claim 1, wherein the segmenting comprises: and sequentially carrying out binarization treatment, corrosion treatment and expansion treatment on the image to be detected so as to segment the material to be detected in the image to be detected.
6. The utility model provides a nut seed benevolence center worm loses defect detecting device which characterized in that includes:
the device comprises an acquisition module, a detection module and a processing module, wherein the acquisition module is used for acquiring a to-be-detected image of a to-be-detected material, and the to-be-detected image is an external rectangular X-ray image of the to-be-detected material;
the first judgment module is used for judging whether the materials to be detected in the image to be detected are adhered or not;
the segmentation module is used for segmenting the material to be detected in the image to be detected when the judgment result of the first judgment module is that adhesion exists, and intercepting the circumscribed rectangle X-ray image of the material to be detected again to obtain the image to be detected;
the second judgment module is used for judging whether the material to be detected is cut according to the image to be detected; when the judgment result of the second judgment module is that the material to be detected is separated, performing central worm corrosion detection on the material to be detected according to the image to be detected;
when the judgment result of the second judgment module is that the image to be detected is not divided, carrying out binarization processing and convex hull processing on the image to be detected in sequence to respectively obtain a second binarization image and a convex hull image, and subtracting the obtained convex hull image from the second binarization image to obtain a concave area; if the number of the depressed areas with the areas larger than a second preset threshold is more than a preset number, performing line drawing treatment on the image to be detected,
the line drawing process comprises the following steps: drawing a line on the minimum distance between the concave region with the largest area and the concave region with the second largest area, and obtaining a plurality of second concave regions; if the area reduction amplitude of the second recessed area exceeds a preset area or the number of the second recessed areas exceeds a preset number relative to the recessed areas, the line drawing is considered to be successful; if the line drawing is unsuccessful, determining the distances between the vertexes of the second sunken regions with the areas larger than the second preset threshold value, and drawing the line between the two vertexes corresponding to the minimum distance;
performing the central worm corrosion detection on the material to be detected according to the image to be detected after the line drawing processing;
the detection module is used for carrying out the central worm corrosion detection on the material to be detected according to the image to be detected, and the central worm corrosion detection comprises the following steps: sharpening the to-be-detected image to obtain a sharpened image, corroding the material outline part of the sharpened image, taking a central area image corresponding to the corroded material outline, performing binarization processing on the central area image to obtain a first binarized image, extracting the inner outline of the central area subjected to binarization processing, and judging whether the to-be-detected material has a central worm damage defect or not according to the area of the inner outline in the first binarized image.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911054137.4A CN112819745B (en) | 2019-10-31 | 2019-10-31 | Nut kernel center worm-eating defect detection method and device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201911054137.4A CN112819745B (en) | 2019-10-31 | 2019-10-31 | Nut kernel center worm-eating defect detection method and device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112819745A CN112819745A (en) | 2021-05-18 |
| CN112819745B true CN112819745B (en) | 2023-02-28 |
Family
ID=75851825
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201911054137.4A Active CN112819745B (en) | 2019-10-31 | 2019-10-31 | Nut kernel center worm-eating defect detection method and device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112819745B (en) |
Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2235093A1 (en) * | 1998-04-17 | 1999-10-17 | Newnes Machine Ltd. | Method and apparatus for improved identification of probable defects in a workpiece |
| CN105930833A (en) * | 2016-05-19 | 2016-09-07 | 重庆邮电大学 | Vehicle tracking and segmenting method based on video monitoring |
| CN106442525A (en) * | 2016-08-30 | 2017-02-22 | 石河子大学 | Online detecting method for wizened defects of insides of walnuts |
| CN106971393A (en) * | 2017-02-23 | 2017-07-21 | 北京农业信息技术研究中心 | The phenotype measuring method and system of a kind of corn kernel |
| CN107341490A (en) * | 2017-07-17 | 2017-11-10 | 华中科技大学 | A kind of shielding automobile detection method and system based on convex closure analysis |
| CN108230321A (en) * | 2018-01-19 | 2018-06-29 | 深圳市亿图视觉自动化技术有限公司 | Defect inspection method and device |
| CN108613989A (en) * | 2018-04-24 | 2018-10-02 | 南京财经大学 | A kind of detection method of paddy unsound grain |
| CN109598715A (en) * | 2018-12-05 | 2019-04-09 | 山西镭谱光电科技有限公司 | Material size online test method based on machine vision |
| CN109685760A (en) * | 2018-11-05 | 2019-04-26 | 华中科技大学 | A kind of SLM powder bed powder laying image convex closure depression defect detection method based on MATLAB |
| CN109961071A (en) * | 2019-03-27 | 2019-07-02 | 赵杨 | A kind of determination method of forward direction watermelon quirk pollination state |
| CN110033516A (en) * | 2019-04-16 | 2019-07-19 | 重庆交通大学 | Flat-elongated particles detection method of content based on the identification of binocular camera Image Acquisition |
| CN110261410A (en) * | 2019-05-24 | 2019-09-20 | 西安交通大学 | A kind of detection device and method of glass lens defect |
Family Cites Families (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP5169626B2 (en) * | 2008-08-27 | 2013-03-27 | Ｊｆｅスチール株式会社 | Internal defect measurement method |
| CN104424639B (en) * | 2013-09-04 | 2017-07-28 | 中国环境科学研究院 | Petiole and blade dividing method based on morphological erosion |
| CN104730092B (en) * | 2015-03-10 | 2018-09-07 | 盐城市圣泰阀门有限公司 | Defect inspection method |
| CN106023137B (en) * | 2016-05-03 | 2018-10-16 | 华中科技大学 | A kind of timber method of counting based on contour optimization |
| CN107437256B (en) * | 2016-05-27 | 2020-06-02 | 合肥美亚光电技术股份有限公司 | Automatic dividing method and system applied to tire X-ray image channel |
| CN107545557A (en) * | 2016-06-23 | 2018-01-05 | 爱威科技股份有限公司 | Egg detecting method and device in excrement image |
| CN106846316B (en) * | 2017-02-10 | 2020-03-27 | 云南电网有限责任公司电力科学研究院 | Automatic identification method for typical defect images in GIS |
| CN106980828B (en) * | 2017-03-17 | 2020-06-19 | 深圳市魔眼科技有限公司 | Method, device and equipment for determining palm area in gesture recognition |
| CN107085836B (en) * | 2017-05-16 | 2020-03-03 | 合肥工业大学 | Universal ghost eliminating method in moving object detection |
| CN107808378B (en) * | 2017-11-20 | 2020-08-25 | 浙江大学 | Method for detecting potential defects of complex-structure casting based on vertical longitudinal and transverse line profile features |
| CN108355981B (en) * | 2018-01-08 | 2020-03-17 | 西安交通大学 | Battery connector quality detection method based on machine vision |
| CN109146838A (en) * | 2018-06-20 | 2019-01-04 | 湖南自兴智慧医疗科技有限公司 | A kind of aobvious band adhering chromosome dividing method of the G merged based on geometrical characteristic with region |
| CN109345551B (en) * | 2018-09-18 | 2020-11-20 | 清华大学 | Method and system for detecting concave envelope in image outer contour and computer storage medium |
| CN109636824B (en) * | 2018-12-20 | 2022-10-11 | 巢湖学院 | Multi-target counting method based on image recognition technology |
| CN109447989A (en) * | 2019-01-08 | 2019-03-08 | 哈尔滨理工大学 | Defect detecting device and method based on motor copper bar burr growth district |
| CN109949291A (en) * | 2019-03-19 | 2019-06-28 | 苏州润智和智能科技有限公司 | A kind of defect inspection method of Cast Aluminum Auto-parts Abroad radioscopic image |
| CN110060293B (en) * | 2019-04-24 | 2022-06-28 | 中国兵器科学研究院宁波分院 | Defect detection performance limit evaluation method of CT detection system |
-
2019
- 2019-10-31 CN CN201911054137.4A patent/CN112819745B/en active Active
Patent Citations (12)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2235093A1 (en) * | 1998-04-17 | 1999-10-17 | Newnes Machine Ltd. | Method and apparatus for improved identification of probable defects in a workpiece |
| CN105930833A (en) * | 2016-05-19 | 2016-09-07 | 重庆邮电大学 | Vehicle tracking and segmenting method based on video monitoring |
| CN106442525A (en) * | 2016-08-30 | 2017-02-22 | 石河子大学 | Online detecting method for wizened defects of insides of walnuts |
| CN106971393A (en) * | 2017-02-23 | 2017-07-21 | 北京农业信息技术研究中心 | The phenotype measuring method and system of a kind of corn kernel |
| CN107341490A (en) * | 2017-07-17 | 2017-11-10 | 华中科技大学 | A kind of shielding automobile detection method and system based on convex closure analysis |
| CN108230321A (en) * | 2018-01-19 | 2018-06-29 | 深圳市亿图视觉自动化技术有限公司 | Defect inspection method and device |
| CN108613989A (en) * | 2018-04-24 | 2018-10-02 | 南京财经大学 | A kind of detection method of paddy unsound grain |
| CN109685760A (en) * | 2018-11-05 | 2019-04-26 | 华中科技大学 | A kind of SLM powder bed powder laying image convex closure depression defect detection method based on MATLAB |
| CN109598715A (en) * | 2018-12-05 | 2019-04-09 | 山西镭谱光电科技有限公司 | Material size online test method based on machine vision |
| CN109961071A (en) * | 2019-03-27 | 2019-07-02 | 赵杨 | A kind of determination method of forward direction watermelon quirk pollination state |
| CN110033516A (en) * | 2019-04-16 | 2019-07-19 | 重庆交通大学 | Flat-elongated particles detection method of content based on the identification of binocular camera Image Acquisition |
| CN110261410A (en) * | 2019-05-24 | 2019-09-20 | 西安交通大学 | A kind of detection device and method of glass lens defect |
Non-Patent Citations (5)
| Title |
|---|
| computer vision based system for apple surface defect detection;Qingzhong Li 等;《computers and electronics in agriculture》;20021130;第36卷(第2-3期);第215-223页 * |
| 交通视频监控中车辆识别与粘连车辆分割方法的研究;刘洋;《中国优秀硕士学位论文全文数据库 信息科技辑》;20130215;第2013年卷(第2期);I138-1386 * |
| 智能交通系统中车辆检测的研究与实现;黄伟正;《中国优秀硕士论文全文数据库 工程科技Ⅱ辑》;20180615;第2018年卷(第6期);第46-48页 * |
| 软X射线技术对核桃内部品质的无损检测研究;黄星奕 等;《食品工业科技》;20111231;第32卷(第2期);第2节 * |
| 黄星奕 等.软X射线技术对核桃内部品质的无损检测研究.《食品工业科技》.2011,第32卷(第2期), * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112819745A (en) | 2021-05-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109377485A (en) | A kind of instant noodles packaging defect machine vision detection method | |
| CN103942816B (en) | A kind of wood single-plate knot quantity based on image procossing and the recognition methods of size | |
| CN115880248B (en) | Surface scratch defect identification method and visual detection equipment | |
| CN107490582B (en) | Assembly line workpiece detection system | |
| Said et al. | Robust automatic void detection in solder balls | |
| KR102027986B1 (en) | Bead recognition apparatus using vision camera and method thereof | |
| CN111583216A (en) | Defect detection method for PCBA | |
| CN115578389A (en) | Defect detection method of groove MOS device | |
| EP2302358B1 (en) | Device for determining quality of steel material and method for determining quality of steel material | |
| CN115684174A (en) | Agricultural product transportation conveyor belt safe operation monitoring method | |
| Gopalakrishnan et al. | Optic disc segmentation using circular Hough transform and curve fitting | |
| KR102043316B1 (en) | Apparatus for weld bead recognition of 2d image-based and soot removal method using the same | |
| CN117094975A (en) | Method and device for detecting surface defects of steel and electronic equipment | |
| CN108346138B (en) | Surface defect detection method and system based on image processing | |
| CN112819745B (en) | Nut kernel center worm-eating defect detection method and device | |
| CN107993223A (en) | Scratch detection method, apparatus and electronic equipment | |
| CN112819746B (en) | Nut kernel worm erosion defect detection method and device | |
| CN109934817B (en) | Method for detecting malformation of external contour of fruit body | |
| CN113516608A (en) | Tire defect detection method and device, and tire detection device | |
| CN115159027B (en) | Belt tearing monitoring method | |
| CN114037701B (en) | Mechanical part hydrogen damage detection method based on image processing | |
| CN115587993A (en) | Mask welding spot defect detection method based on AI and machine vision algorithm | |
| CN109191439A (en) | A kind of target workpiece surface knife mark defect inspection method | |
| CN107545560B (en) | Method and device for detecting air bubbles on tire sidewall | |
| KR101713001B1 (en) | Metal panel detecting appratus by processing image for method same |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |