U.S. patent application number 15/678310 was filed with the patent office on 2019-02-21 for method and system for detecting line defects on surface of object.
The applicant listed for this patent is Siemens Energy, Inc.. Invention is credited to Kevin P. Bailey, Jan Ernst, Rameswar Panda, Ziyan Wu.
Application Number | 20190057498 15/678310 |
Document ID | / |
Family ID | 65032184 |
Filed Date | 2019-02-21 |
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United States Patent
Application |
20190057498 |
Kind Code |
A1 |
Panda; Rameswar ; et
al. |
February 21, 2019 |
METHOD AND SYSTEM FOR DETECTING LINE DEFECTS ON SURFACE OF
OBJECT
Abstract
Method and system for detecting line defects on surface of
object are presented. An imaging device captures images of surface
of object under ambient and dark field illumination conditions. The
images are processed with a plurality of image operations to detect
areas of potential defects based on predictable pattern consisting
of bright and shadow regions. Areas of potential defect are cut
from processed images to sub images. Sub images are stitched
together to generate hypotheses of potential defects at locations
on surface of object. The hypotheses are classified to determine
whether the potential defects are true defects at the locations.
Line defect is detected by refining line segments detected on the
processed image based on criteria. The criteria include distance
from the true defects to the line segments and slops between the
true defects and the line segments are less than threshold
values.
Inventors: |
Panda; Rameswar; (Riverside,
CA) ; Wu; Ziyan; (Plainsboro, NJ) ; Ernst;
Jan; (Plainsboro, NJ) ; Bailey; Kevin P.;
(Chuluota, FL) |
|
Applicant: |
Name |
City |
State |
Country |
Type |
Siemens Energy, Inc. |
Orlando |
FL |
US |
|
|
Family ID: |
65032184 |
Appl. No.: |
15/678310 |
Filed: |
August 16, 2017 |
Current U.S.
Class: |
1/1 |
Current CPC
Class: |
G06T 2207/20036
20130101; G06T 7/11 20170101; G06T 2207/20076 20130101; G06T
2207/20081 20130101; G06T 7/73 20170101; G06T 5/30 20130101; G06T
7/0008 20130101; G06K 2209/19 20130101; G06T 2207/20061 20130101;
G06T 7/0004 20130101; G06T 7/13 20170101; G06T 5/50 20130101; G06K
9/6256 20130101; G06T 2207/20132 20130101; G06T 2207/20021
20130101; G06T 2207/20032 20130101; G06K 9/4609 20130101; G06K
9/6267 20130101; G06T 2207/10152 20130101 |
International
Class: |
G06T 7/00 20060101
G06T007/00; G06T 7/11 20060101 G06T007/11; G06T 5/50 20060101
G06T005/50; G06K 9/62 20060101 G06K009/62; G06T 5/30 20060101
G06T005/30 |
Claims
1. A method for detecting a line defect on a surface of an object
comprising: supporting the object on a platform; illuminating the
surface of the object with a plurality of illumination sources
comprising at least one ambient illumination source and at least
one dark field illumination source; capturing images of the surface
of the object under illumination conditions with the illumination
sources using an imaging device; processing the captured images
with a plurality of image operations using the image processor to
detect areas of potential defects at locations on the surface of
the object; cutting the areas of the potential defects from the
processed images to sub images using the image processor; stitching
the sub images at same location together to generate a set of
hypotheses of the potential defects at the locations on the surface
of the object using the image processor; classifying the hypotheses
in the stitched images with a classifier to determine whether the
potential defects are true defects using the image processor,
wherein the classifier is trained with training data having
characteristics of the true defects, and wherein the detected true
defects are discrete true defects at the locations on the surface
of the object; determining whether the discrete true defects
consist of a line defect by refining line segments detected on one
of the processed image based on a criterion; and generating an
output comprising the line defect on the surface of the object.
2. The method as claimed in claim 1, wherein the criterion
comprises a distance from each of the discrete true defects to each
of the line segments is less than a threshold value.
3. The method as claimed in claim 1, wherein the criterion
comprises a difference between a slope of each of the discrete true
defects and a slope of each of the line segments is less than a
threshold value.
4. The method as claimed in claim 1, wherein the processing step
further comprises implementing convolution operations to the
captured images using corresponding kernels.
5. The method as claimed in claim 4, wherein the kernels are
defined corresponding to configuration of the dark field
illumination source to detect the potential defect.
6. The method as claimed in claim 4, wherein the processing step
further comprises implementing dilation operations to the
convoluted images.
7. The method as claimed in claim 5, wherein the processing step
further comprises multiplying the convoluted and dilated images to
one image.
8. The method as claimed in claim 6, wherein the processing step
further comprises implementing median filtering operations to the
multiplied image, and wherein the median filtered image is the
output processed image.
9. The method as claimed in claim 7, wherein the processing step
further comprises implementing magnitude operation to the median
filtered image.
10. The method as claimed in claim 8, wherein the processing step
further comprises multiplying the magnitude image with one of the
captured images under ambient illumination condition and processed
with convolution and dilation operations, and wherein the
multiplied image is the output processed image.
11. A system for detecting a line defect on a surface of an object
comprising: a platform for supporting the object; a plurality of
illumination sources comprising at least one ambient illumination
source and at least one dark field illumination source for
illuminating the surface of the object; an imaging device for
capturing images of the surface of the object under illumination
conditions with the illumination sources; and an image processor
for: processing the captured images with a plurality of image
operations to detect areas of potential defects at locations on the
surface of the object; cutting the areas of the potential defects
from the processed images to sub images; stitching the sub images
at same location together to generate a set of hypotheses of the
potential defects at the locations on the surface of the object;
classifying the hypotheses in the stitched images with a classifier
to determine whether the potential defects are true defects using
the image processor, wherein the classifier is trained with
training data having characteristics of the true defects, and
wherein the detected true defects are discrete true defects at the
locations on the surface of the object; determining whether the
discrete true defects consist of a line defect by refining line
segments detected on one of the processed image based on a
criterion; and generating an output comprising the line defect on
the surface of the object.
12. The system as claimed in claim 11, wherein the criterion
comprises a distance from each of the discrete true defects to each
of the line segments is less than a threshold value.
13. The system as claimed in claim 11, wherein the criterion
comprises a difference between a slope of each of the discrete true
defects and a slope of each of the line segments is less than a
threshold value.
14. The system as claimed in claim 11, wherein the processing
operations comprise convolution operations to the captured images
using corresponding kernels.
15. The system as claimed in claim 14, wherein the processing
operations comprise dilation operations to the convoluted
images.
16. The system as claimed in claim 15, wherein the processing
operations comprise multiplying the convoluted and dilated images
to one image.
17. The system as claimed in claim 16, wherein the processing
operations comprise median filtering operations to the multiplied
image, and wherein the median filtered image is the output
processed image.
18. The system as claimed in claim 17, wherein the processing
operations comprise magnitude operation to the median filtered
image.
19. The system as claimed in claim 18, wherein the processing step
further comprises multiplying the magnitude image with one of the
captured images under ambient illumination condition and processed
with convolution and dilation operations, and wherein the
multiplied image is the output processed image.
20. A computer program executable in a computer for performing a
method of detecting a line defect on a surface of an object,
wherein the computer stores images of the surface of the object
under illumination conditions with illumination sources comprising
at least one ambient illumination source and at least one dark
field illumination source, wherein the method comprises steps of:
processing the captured images with a plurality of image operations
to detect areas of potential defects at locations on the surface of
the object; cutting the areas of the potential defects from the
processed images to sub images; stitching the sub images at same
location together to generate a set of hypotheses of the potential
defects at the locations on the surface of the object; classifying
the hypotheses in the stitched images with a classifier to
determine whether the potential defects are true defects using the
image processor, wherein the classifier is trained with training
data having characteristics of the true defects, and wherein the
detected true defects are discrete true defects at the locations on
the surface of the object; determining whether the discrete true
defects consist of a line defect by refining line segments detected
on one of the processed image based on a criterion; and generating
an output comprising the line defect on the surface of the object.
Description
TECHNICAL FIELD
[0001] This invention relates generally to a method and a system
for detecting a line defect on a surface of an object.
DESCRIPTION OF RELATED ART
[0002] Defect detection on a surface of an object is an important
aspect of industrial production quality assurance process which may
provide an important guarantee for quality of products. Defects may
include line defects on a surface of an object. Methods of line
defect detection may include applying edge detection on an image of
the surface of the object. However, the edges detected in the image
may include a plurality of false detections, such as borders of the
image may be detected as true line defects.
SUMMARY OF INVENTION
[0003] Briefly described, aspects of the present invention relate
to a method and a system for detecting a line defect on a surface
of an object.
[0004] According to an aspect, a method for detecting a line defect
on a surface of an object is presented. The method comprises
supporting the object on a platform. The method comprises
illuminating the surface of the object with a plurality of
illumination sources comprising at least one ambient illumination
source and at least one dark field illumination source. The method
comprises capturing images of the surface of the object under
illumination conditions with the illumination sources using an
imaging device. The method comprises processing the captured images
with a plurality of image operations using an image processor to
detect areas of potential defects at locations on the surface of
the object. The method comprises cutting the areas of the potential
defects from the processed images to sub images using the image
processor. The method comprises stitching the sub images at same
location together to generate a set of hypotheses of the potential
defects at the locations on the surface of the object using the
image processor. The method comprises classifying the hypotheses in
the stitched images with a classifier to determine whether the
potential defects are true defects using the image processor. The
classifier is trained with training data having characteristics of
the true defects. The detected true defects are discrete true
defects at the locations on the surface of the object. The method
comprises determining whether the discrete true defects consist of
a line defect by refining line segments detected on one of the
processed image based on a criterion. The method comprises
generating an output comprising the line defect on the surface of
the object.
[0005] According to an aspect, a system for detecting a line defect
on a surface of an object is presented. The system comprises a
platform for supporting the object. The system comprises a
plurality of illumination sources comprising at least one ambient
illumination source and at least one dark field illumination source
for illuminating the surface of the object. The system comprises an
imaging device for capturing images of the surface of the object
under illumination conditions with the illumination sources. The
system comprises an image processor. The image processor processes
the captured images with a plurality of image operations using to
detect areas of potential defects at locations on the surface of
the object. The image processor cuts the areas of the potential
defects from the processed images to sub images. The image
processor stitches the sub images at same location together to
generate a set of hypotheses of the potential defects at the
locations on the surface of the object. The image processor
classifies the hypotheses in the stitched images with a classifier
to determine whether the potential defects are true defects. The
classifier is trained with training data having characteristics of
the true defects. The detected true defects are discrete true
defects at the locations on the surface of the object. The image
processor determines whether the discrete true defects consist of a
line defect by refining line segments detected on one of the
processed image based on a criterion. The image processor generates
an output an output comprising the line defect on the surface of
the object.
[0006] According to an aspect, a computer program executable in a
computer for performing a method of detecting a line defect on a
surface of an object is presented. The computer stores images of
the surface of the object under illumination conditions with
illumination sources comprising at least one ambient illumination
source and at least one dark field illumination source. The method
comprises step of processing the images with a plurality of image
operations to detect areas of potential defects at locations on the
surface of the object. The method comprises step of cutting the
areas of the potential defects from the processed images to sub
images. The method comprises step of stitching the sub images at
same location together to generate a set of hypotheses of the
potential defects at the locations on the surface of the object.
The method comprises step of classifying the hypotheses in the
stitched images with a classifier to determine whether the
potential defects are true defects. The classifier is trained with
training data having characteristics of the true defects. The
detected true defects are discrete true defects at the locations on
the surface of the object. The method comprises step of determining
whether the discrete true defects consist of a line defect by
refining line segments detected on one of the processed image based
on a criterion. The method comprises step of generating an output
comprising the line defect on the surface of the object.
[0007] Various aspects and embodiments of the application as
described above and hereinafter may not only be used in the
combinations explicitly described, but also in other combinations.
Modifications will occur to the skilled person upon reading and
understanding of the description.
BRIEF DESCRIPTION OF DRAWINGS
[0008] Exemplary embodiments of the application are explained in
further detail with respect to the accompanying drawings. In the
drawings:
[0009] FIG. 1 illustrates a schematic side view of a system for
detecting a defect at a surface of an object according to an
embodiment of the invention;
[0010] FIG. 2 illustrates a schematic top view of a system for
detecting a defect at a surface of an object according to an
embodiment of the invention;
[0011] FIG. 3 illustrates a schematic diagram of a pattern
consisting of a bright region and a shadow region of a defect on a
surface under a dark field illumination source according to an
embodiment of the invention;
[0012] FIG. 4 illustrates a schematic flow chart of a method for
detecting a line defect at a surface of an object according to an
embodiment of the invention; and
[0013] FIG. 5 illustrates a schematic flow chart of a step for
processing images of the method as illustrated in FIG. 4 according
to an embodiment of the invention.
[0014] To facilitate understanding, identical reference numerals
have been used, where possible, to designate identical elements
that are common to the figures.
DETAILED DESCRIPTION OF INVENTION
[0015] A detailed description related to aspects of the present
invention is described hereafter with respect to the accompanying
figures.
[0016] FIGS. 1 and 2 respectively illustrate a schematic side view
and top view of a system 100 for detecting a defect at a surface
112 of an object 110 according to an embodiment of the invention.
The system 100 may include a platform 120 that supports the object
110. The system 100 may include a motor 122. The platform 120 may
be movable along at least one direction by the motor 122. The motor
122 may have a motor controller 124 that controls a movement of the
platform 120.
[0017] The system 100 may include a hood 130 arranged above the
platform 120. The hood 130 may have a hollow cone shape. The system
100 may have an imaging device 140. The imaging device 140 may be
arranged inside the hood 130. The imaging device 140 may be located
at top of the hood 130. The imaging device 140 may include, for
example, a camera. The imaging device 140 may include a lens 142.
The lens 142 may be arranged at a location relative to the surface
112 of the object 110 such that the imaging device 140 may have a
desirable field of view 144 on the surface 112 of the object 110.
The imaging device 140 may pan and tilt relative to the surface 112
of the object 110 to achieve a desired field of view 144 on the
surface 112 of the object 100. The motor 122 may move the platform
120 along with the object 110 so that the field of view 144 of the
imaging device 140 may cover different areas of the surface 112 of
the object 110.
[0018] The system 100 may have at least one ambient illumination
source 150. The ambient illumination source 150 may be arranged
inside the hood 130. The ambient illumination source 150 may be
located at top of the hood 130. The ambient illumination source 150
may provide an ambient illumination condition at the surface 112 of
the object 110. The ambient illumination source 150 may include,
for example, a light emitting diode (LED) strobe light. The ambient
illumination source 150 may have a ring shape. According to an
exemplary embodiment as illustrated in FIG. 1, the lens 142 may
extend through the ring shaped ambient illumination source 150.
[0019] The system 100 may include at least one dark field
illumination source 160. The dark field illumination source 160 may
be arranged at bottom of the hood 130. The dark field illumination
source 160 may provide a dark field illumination condition on the
surface 112 of the object 110. The dark field illumination source
160 may include, for example, a LED strobe light. The dark field
illumination source 160 may be oriented at a location relative to
the surface 112 of the object 110 such that a dark field
illumination condition may be provided within the field of view 144
of the lens 142 at the surface 112 of the object 110. Under a dark
field illumination condition, a defect may have a predictable
pattern consisting of a bright region that is illuminated by the
dark field illumination source 160 and a dark or shadow region that
is not illuminated by the dark field illumination source 160.
[0020] With reference to FIG. 2 which illustrates a schematic top
view of the system 100, four dark field illumination sources 160
are arranged at bottom of the hood 130. Two dark field illumination
sources 160 are arranged along an x-axis in a plane of the field of
view 144. The two dark field illumination sources 160 are located
at two sides of the field of view 144 respectively, denoted
x-positive and x-negative. Two other dark field illumination
sources 160 are arranged along a y-axis in the plane of the field
of view 144. The two other dark field illumination sources 160 are
located at two sides of the field of view 144 respectively, denoted
y-positive and y-negative. Different numbers of dark field
illumination sources 160 may be arranged at bottom of the hood
130.
[0021] With reference to FIG. 1, the system 100 may include a
trigger controller 170. The trigger controller 170 may functionally
connect to the imaging device 140, the ambient illumination source
150 and the dark field illumination sources 160. The trigger
controller 170 may trigger the ambient illumination source 150 and
the dark field illumination sources 160 at a defined pattern,
sequence, or simultaneously. The trigger controller 170 may trigger
the imaging device 140 to capture images of the surface 112 of the
object 110 under the triggered illumination conditions
respectively. The trigger controller 170 may control configurations
of the dark field illumination sources 160. The configurations of
the dark field illumination sources 160 may include orientations of
the dark field illumination sources 160 relative to the surface 112
of the object 110, illumination intensities of the dark field
illumination sources 160, etc. According to an embodiment, the
trigger controller 170 may be a computer having a computer program
implemented.
[0022] The system 100 may include an image processor 180. The image
processor 180 may functionally connect to the imaging device 140.
The image processor 180 may process the images captured by the
imaging device 140 to detect defects on the surface 112 of the
object 110. According to an embodiment, the image processor 180 may
be a computer having a computer program implemented. According to
an embodiment, the trigger controller 170 and the image processor
180 may be integrated parts of one computer. The system 100 may
include a display device 190 functionally connected to the imaging
processor 180. The display device 190 may display the captured
images. The display device 190 may display the processed images.
The display device 190 may display an output including information
of a detected defect. The display device 190 may be a monitor. The
display device 190 may be an integrated part of the imaging
processor 180.
[0023] According to an embodiment, a shape of a potential defect,
such as size or length of the defect, may have a specific and
predictable pattern consisting of bright region and shadow region
in an image under different configurations of the dark field
illumination sources 160. FIG. 3 illustrates a schematic diagram of
a pattern consisting of a bright region 115 and a shadow region 117
of a defect 113 on a surface 112 under a dark field illumination
source 160 according to an embodiment. As illustrated in FIG. 3, a
surface 112 may have a V-shaped defect 113. A first portion 114 of
the V-shaped defect 113 is illuminated by the dark filed
illumination source 160 that forms a bright region 115 in an image.
A second portion 116 of the V-shaped defect 113 is not illuminated
by the dark filed illumination source 160 that forms a shadow
region 117 in the image. The bright region 115 and the shadow
region 117 are within a field of view 144 of an imaging device 140.
The V-shaped defect 113 may be a micro defect, for example, scale
of the V-shaped 113 defect may be as small as micrometers.
[0024] FIG. 4 illustrates a schematic flow chart of a method 200
for detecting a line defect at a surface 112 of an object 110 using
an image processor 180 according to an embodiment of the invention.
In step 210, the imaging device 140 may be calibrated relative to
the surface 112 of the object 110 to be inspected. The calibration
may estimate parameters of the lens 142 of the imaging device 140
to correct distortion of the lens 142 of the imaging device 140
when capturing images of the surface 112 of the object 110.
[0025] In step 220, the ambient illumination source 150 and the
dark field illumination sources 160 may be triggered by the trigger
controller 170 to illuminate the surface 112 of the object 110 with
an ambient illumination condition and dark field illumination
conditions. The imaging device 140 may be triggered by the trigger
controller 170 to capture images of the surface 112 of the object
110 under the ambient illumination condition and the dark field
illumination conditions respectively. Each image captures a field
of view 144 of the surface 112 of the object 110 under different
illumination conditions. Each image may contain potential defects
having specific shapes on the surface 112 of the object 100. Shapes
of the potential defects have predictable patterns consisting of
bright region and shadow region based on configurations of the dark
field illumination sources 160.
[0026] In step 230, the captured images of the surface 112 of the
object 110 are processed by the image processor 180. The processing
step 230 may implement a plurality of image operations to the
captured images to detect areas of potential defects at locations
on the surface 112 of the object 110. A plurality of areas may be
detected having potential defects. Each area may have a potential
defect. The locations of the plurality of areas may be represented
by, such as x, y locations on a plane of the surface 112. The
potential defects may be detected by patterns consisting of bright
region and shadow region in the processed images. The plurality of
image operations may enhance shapes of potential defects and reduce
false detection rate.
[0027] In step 240, the areas showing the potential defects at
locations on the surface 112 of the object 110 are cut off from the
processed images to sub images. Size of each area to be cut off may
be small enough to detect a micro defect. For example, size of each
area may be less than 100 by 100 pixels, depending on resolution of
the image. Areas that do not show indications of potential defects
may be pruned out and do not need further processing.
[0028] In step 250, sub images having potential defects at the same
location on the surface 112 of the object 110 are stitched together
to generate a set of hypotheses of the potential defects at the
locations on the surface 112 of the object 110.
[0029] In step 260, the stitched images are classified with a
classifier to determine whether the hypotheses of the potential
defects are true defects on the surface 112 of the object 110. The
classifier may be trained with a training data having
characteristics of a true defect. The classification outputs a
plurality of discrete true defects at the locations on the surface
112 of the object 110. According to an embodiment, a random forest
classifier may be used to classify the potential defects. The
random forest classifier may classify hypotheses with high
efficiency and scalability in large scale applications.
[0030] In step 270, the discrete true defects are determined
whether they consist of a line defect on the surface 112 of the
object 110 by refining line segments detected on one of the
processed images. The line segments are refined based on certain
criteria.
[0031] Line segments may be detected on one of the processed images
by step 242 and step 244. In step 242, edges are detected on one of
the processed images by applying edge detection. The edges may be
detected, for example, by Canny edge detector. In step 244, line
segments may be detected from the edges by applying Hough
transform. A probabilistic Hough transform may be used for
detecting the line segments.
[0032] According to an embodiment, a criterion for refining the
line segments may include that a distance from each of the discrete
true defects to each of the line segments is less than a threshold
value. The threshold value may be, for example, 5 pixels, or 8
pixels, or 10 pixels. The distance may be a perpendicular distance
from a center of the discrete true defect to each of the line
segments. A criterion for refining the line segments may include
that a difference between a slope of each of the discrete true
defects and a slope of each of the line segments is less than a
threshold value. The threshold value may be, for example, in a
range of -7 to 7 degrees, or in a range of -5 to 5 degrees, or in a
range of -3 to 3 degrees. The slop of each of the discrete true
defects may be obtained by Hough transform. The threshold values
for distance and slop may be defined depending on resolution of the
image. The line defect is detected by iteratively removing the line
segments that do not satisfy the criteria. The line defect may
consist of a plurality of connected line segments. The line defect
may have a curved line shape.
[0033] In step 280, an output is generated. The output may include
the detected line defect on the surface 112 of the object 110. The
output may be a report form. The output may be an image with the
detected true defects marked at the locations. The image may be one
of the captured images or one of the processed images. The output
may be stored in the imaging processor 180, or displayed on a
display device 190, or print out by a printer.
[0034] FIG. 5 illustrates a schematic flow chart of a step 230 for
processing images of the method 200 as illustrated in FIG. 4
according to an embodiment of the invention. Referring to FIG. 2
and FIG. 4 step 220, the trigger controller 170 may sequentially
turn the ambient illumination source 150 and the dark field
illumination sources 160 on and off. The trigger controller 170 may
trigger the imaging device 140 to sequentially capture images of
the surface 112 of the object 110 under ambient illumination
condition and dark field illumination conditions respectively. The
dark filed illumination sources 160 may be sequentially turned on
and off by the trigger controller 170 so that images are
sequentially captured by the imaging device 140 under sequential
dark field illumination conditions. For example, image captured
with the ambient illumination source 150 turned on is denoted as
image_amb. Image captured with the dark field illumination source
160 located on x-positive position turned on is denoted as
image_xpos. Image captured with the dark field illumination source
160 located on x-negative position turned on is denoted as
image_xneg. Image captured with the dark field illumination source
160 located on y-positive location turned on is denoted as
image_ypos. Image captured with the dark field illumination source
160 located on y-negative location turned on is denoted as
image_yneg.
[0035] In step 231 of the step 230, convolution operations are
implemented to the captured images with corresponding kernels.
Convolution operations may filter noises in the captured images.
Kernels are defined corresponding to predefined configurations of
the dark field illumination sources 160 to enhance detecting a
potential defect in the captured images based on a pattern
consisting of bright region and shadow region under the predefined
configurations of the dark field illumination sources 160. Shape of
a potential defect, such as size or length of the potential defect,
may have a specific and predictable pattern consisting of bright
region and shadow region in an image under different configurations
of the dark field illumination sources 160.
[0036] Different kernels affect output filtered images. For
example, assuming color code as: black=1, white=0, grey=-1, the
kernels for the five different images captured with certain
predefined configurations of dark field illumination sources 160
may be defined as followings:
kernel_xpos = [ - 1 - 1 - 1 - 1 - 1 1 1 0 0 0 0 ] , kernel_xneg = [
0 0 0 0 1 1 - 1 - 1 - 1 - 1 - 1 ] , kernel_ambx = [ 1 1 1 - 1 - 1 -
1 - 1 - 1 1 1 1 ] ##EQU00001## kernel_ypos = [ kernel_xpos ] T = [
- 1 , - 1 , - 1 , - 1 , - 1 , 1 , 1 , 0 , 0 , 0 , 0 ]
##EQU00001.2## kernel_yneg = [ kernel_xneg ] T = [ 0 , 0 , 0 , 0 ,
1 , 1 , - 1 , - 1 , - 1 , - 1 , - 1 ] ##EQU00001.3## kernel_amby =
[ kernel_ambx ] T = [ 1 , 1 , 1 , - 1 , - 1 , - 1 , - 1 , - 1 , 1 ,
1 , 1 ] ##EQU00001.4## kernel_ambx _y = [ - 0.1667 - 0.1667 -
0.1667 - 0.1667 - 0.1667 - 0.1667 - 0.1667 - 0.1667 - 0.1667 -
0.1667 - 0.1667 - 0.1667 - 0.1667 - 0.1667 - 0.1667 - 0.1667 -
0.1667 - 0.1667 - 0.1667 - 0.1667 - 0.1667 - 0.1667 - 0.1667 -
0.1667 - 0.1667 - 0.1667 - 0.1667 - 0.1667 - 0.1667 - 0.1667 -
0.1667 - 0.1667 - 0.1667 - 0.1667 - 0.1667 - 0.1667 ] Wherein :
kernel_xpos is the convolution operator for image_xpos ,
kernel_xneg is the convolution operator for image_xneg kernel_ambx
is the convolution operator for image_amb . kernel_ypos is the
convolution operator for image_ypos . kernel_yneg is the
convolution operator for image_yneg , kernel_amby is the
convolution operator for image_amb , kernel_ambx _y is the
convolution operator for image_amb . ##EQU00001.5##
[0037] The kernels may be redefined to detect potential defects in
the captured images once configurations of the dark field
illumination sources 160 changes, such as orientation, intensities,
etc. The kernels are redefined based on different patterns of
potential defects consisting of bright region and shadow region in
the captured images under different configurations of the dark
field illumination sources 160.
[0038] In step 232, dilation operations are implemented to the
images filtered by the convolutions. Dilation operations are
morphological operations that may probe and expand shapes of
potential defects 112 in the filtered images using structuring
elements. According to an embodiment, three-by-three flat
structuring elements may be used in the dilation operations at step
232.
[0039] In step 233, multiply operations are implemented to the
convoluted and dilated images. The multiply operations may further
filter noises in the images. With reference to FIG. 4, images
captured with dark field illumination sources 160 located at x-axis
including image_xpos and image_xneg after operations of convolution
using kernel_xpos and kernel_xneg respectively and dilations are
multiplied with image_amb after operations of convolution using
kernel_ambx and dilation to one image. Images captured with dark
field illumination sources 160 located at y-axis including
image_ypos and image_yneg after operations of convolution using
kernel_ypos and kernel_yneg respectively and dilations are
multiplied with image_amb after operations of convolution using
kernel_amby and dilation to another image.
[0040] In step 234, median filtering operations are implemented to
the multiplied images for further filtering the images. Median
filtering operations may preserve potential defects 112 in the
image while removing noises. The output images after median
filtering operations may be detonated as image_x and image_y and
may be output as two output images of imaging processing step
230.
[0041] In step 235, a magnitude operation is implemented to the two
images image_x and image_y after the median filtering operations.
Magnitude operation may maximize signal-to-nose ratio. The output
image after magnitude operation is multiplied with image_amb after
operations of convolution using kernel_ambx_y and dilation to one
image detonated as image_xy.
[0042] In step 236, the processed images are output to three
processed images of the imaging processing step 230. The three
output processed images may include image_x, image_y, and image_xy.
The three output processed images are enhanced from the captured
images to detect areas of potential defects at locations on the
surface 112 of the object 110. Areas of potential defects in each
of the three output processed images are cut to sub images in step
240. Image_xy may be used in step 242 for edge detection and
followed by line segment detection in step 244.
[0043] According to an aspect, the proposed system 100 and method
use a plurality of image processing techniques, such as image
enhancement, morphological operation and machine learning tools
including hypothesis generation and classification to accurately
detect and quantify defects on any type of surfaces 112 of any
objects 110 without relying on strong assumptions on
characteristics of defects. The proposed system 100 and method
iteratively prune false defects and detect true defect by focusing
on smaller areas on a surface 112 of an object 110. Micro defects
may be detected on a surface 112 of an object 110. The micro
defects may be a micro crack. The micro defects may be as small as
micrometers. The micro defects are further processed to detect
significant line defects on a surface 112 of an object 110 by
iteratively refining line segments detected on the enhanced image.
The proposed system 100 and method may be used in power generation
industry to accurately detect and quantify line defects on surfaces
of generator wedges.
[0044] According to an aspect, the proposed system 100 and method
may be automatically operated by a computer to detect line defects
on a surface 112 of an object 110. The proposed system 100 and
method may provide efficient automated line defect detection on a
surface 112 of an object 110. The proposed system 100 and method
may provide a plurality of advantages in detecting line defects on
a surface 112 of an object 110, such as higher detection accuracy,
cost reduction, and consistent detection performance, etc.
[0045] Although various embodiments that incorporate the teachings
of the present invention have been shown and described in detail
herein, those skilled in the art can readily devise many other
varied embodiments that still incorporate these teachings. The
invention is not limited in its application to the exemplary
embodiment details of construction and the arrangement of
components set forth in the description or illustrated in the
drawings. The invention is capable of other embodiments and of
being practiced or of being carried out in various ways. Also, it
is to be understood that the phraseology and terminology used
herein is for the purpose of description and should not be regarded
as limiting. The use of "including," "comprising," or "having" and
variations thereof herein is meant to encompass the items listed
thereafter and equivalents thereof as well as additional items.
Unless specified or limited otherwise, the terms "mounted,"
"connected," "supported," and "coupled" and variations thereof are
used broadly and encompass direct and indirect mountings,
connections, supports, and couplings. Further, "connected" and
"coupled" are not restricted to physical or mechanical connections
or couplings.
REFERENCE LIST
[0046] 100: System [0047] 110: Object [0048] 112: Surface of the
Object [0049] 113: V-shaped Defect [0050] 114: First Portion of the
V-shaped Defect [0051] 115: Bright Region [0052] 116: Second
Portion of the V-shaped Defect [0053] 117: Shadow Region [0054]
120: Platform [0055] 122: Motor [0056] 124: Motor Controller [0057]
130: Hood [0058] 140: Imaging Device [0059] 142: Lens [0060] 144:
Field of View [0061] 150: Ambient Illumination Source [0062] 160:
Dark Filed Illumination Source [0063] 170: Trigger Controller
[0064] 180: Image Processor [0065] 190: Display Device [0066] 200:
Method
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