CN101320004A - Bamboo strip defect on-line detection method based on machine vision - Google Patents
Bamboo strip defect on-line detection method based on machine vision Download PDFInfo
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Abstract
The invention discloses a bamboo strip defect online detection method based on machine vision. A camera is used for photographing the four surfaces of a bamboo strip continuously along the length direction of the bamboo strip to obtain the gray images of the surfaces of the bamboo strip. The image treatment is implemented on every captured frame of digital images and whether defects exist on the small section of the bamboo strip in the frame of the images is judged according to the bamboo strip defect detection method. If the small section of the bamboo strip in the frame of the images is provided with detects, the whole bamboo strip is considered to be provided with detects and simultaneously an instruction of the existing defects is sent to a sorting mechanism. If small sections of the bamboo strip in all frames of the images of the whole bamboo strip are provided with no detects, the bamboo strip is considered to be provided no detects. Simultaneously, an instruction with no detects is sent to the sorting mechanism. The method can implement the online detection on the defects of bamboos with different color shades and simultaneously detect the four surfaces along the length direction of the bamboo strip.
Description
Technical field
The invention belongs to field of visual inspection, specifically is a kind of non-contact detection method of bamboo strip defect.
Background technology
China's bamboo has 39 to belong to, kind more than 500, and more than 500 ten thousand hectares of distribution areas, bamboo resource is abundant and power of regeneration is strong, thereby is more and more widely adopted.After the general bamboo afforestation 5-10, just can cut down utilization every year.Yet owing to reasons such as geographical environment, weather, humidity, some bamboo can putresce from root in growth course, can be infested; If it is proper to store keeping after cutting down, meeting is mouldy and crackle can occur; And in bamboo processing, also can occur cutting sth. askew, cross damaged situation such as cut, thereby can occur on the bamboo bar rotting, channel, mouldy, crackle and defective such as cut sth. askew.The bamboo product processes mainly is made up of two big steps: the first step is base material processing, and second step was to process after the substrate combination.Between first and second step, must carry out defects detection, otherwise can have a strong impact on the qualification rate of bamboo product base material (being the bamboo bar).Raising along with the robotization working ability of bamboo product, increasing bamboo bar needs to wait for defects detection before second step, and only depend on artificial visual check at present, not only the human factor influence is bigger, and multiple in various degree defective makes the people produce visual fatigue easily, thereby causes erroneous judgement and omission to bamboo strip defect.The at present domestic visual pattern that also do not utilize carries out the method that automatic on-line detects to bamboo strip defect.
Summary of the invention
In order to overcome the deficiency that prior art can not detect bamboo strip defect automatically, the invention provides a kind of bamboo strip defect online test method, can lip-deeply rot to along its length four of bamboo bar, channel, mouldy, crackle and defective such as cut sth. askew carry out online automatic detection based on machine vision.
The technical solution adopted for the present invention to solve the technical problems may further comprise the steps:
One. along bamboo bar length direction, use camera that four surfaces of bamboo bar are taken continuously, obtain the gray level image on bamboo bar surface; Predefine: for gray level image, the more little brightness of gray values of pixel points is dark more, the big more brighter display of gray-scale value;
Two. each frame of digital image of catching is carried out Flame Image Process,, judge on the segment bamboo bar in this two field picture whether have defective according to the bamboo strip defect detection method;
Three. if there is defective in the segment bamboo bar in this two field picture, thinks that then there is defective in whole bamboo bar, sends the instruction that has defective to mechanism for sorting simultaneously;
Four. if the segment bamboo bar in all two field pictures of whole bamboo bar does not all have defective, thinks that then this root bamboo bar does not have defective; Send the instruction that does not have defective to mechanism for sorting simultaneously.
Described bamboo strip defect detection method may further comprise the steps:
One, to the view picture gray level image, background is removed on the border that obtains the bamboo bar by rim detection, obtains the image f in bamboo bar zone, and duplicates the view data f ' in bamboo bar zone, obtains two the boundary straight line L1 and the L2 of bamboo bar among the image f.Calculate the angle theta between L1 and the L2, if θ 〉=decision threshold Tp, (Tp≤2 °) are thought that then two boundary straight line of this bamboo bar are not parallel, are had the defective of " triangular strip ", go to step 6, provide the testing result signal that there is defective in the bamboo bar; Otherwise think that two boundary straight line of bamboo bar are approximate parallel in this width of cloth image, proceed next step detection.
Two, to image f, carry out mean filter, remove the interference of noise spot, obtain image f1.
Three, to image f1, carry out the filtering texture operation: be the center with the current pixel point,, replace the gray-scale value of current pixel point with the maximal value in the capable left and right sides neighborhood pixels gray-scale value of current pixel.The overall width of left and right sides neighborhood territory pixel is more than or equal to half pixel wide d of bamboo texture in the image
H, smaller or equal to the pixel wide d between adjacent two textures of bamboo in the image
B(in advance by experiment, measure d
HAnd d
B).All pixel pointwises among the image f1 are handled, finally can be obtained the image f2 behind the filtering bamboo texture.
Four, the detection of the big defective of bamboo bar may further comprise the steps:
1. to image f2, adopt maximum kind spacing method to try to achieve average gray value u1, the u2 of segmentation threshold Th and two classes;
2. if (D is in advance by experiment less than the experiment value D that measures in advance for the u1 that tried to achieve of image f2 and the gray scale difference value between the u2, adopt maximum kind spacing method to handle to qualified bamboo bar image, u1 that tries to achieve and the gray scale difference value between the u2), think that then this width of cloth image does not contain the big defective of bamboo bar, jump to step 5, carry out the detection of the little defective of bamboo bar;
3. in image f2, gray-scale value is defect area smaller or equal to the pixel of Th, the total sum of all pixels among the total num of defect area pixel and the image f2 among the statistical picture f2, (Pe is that accuracy of detection requires down if the ratio of num and sum is less than decision threshold Pe, defect area accounts for the minimum decision threshold of bamboo bar total area number percent), then think this width of cloth image zero defect, jump to step 5, carry out the detection of the little defective of bamboo bar, otherwise think that there is defective in this width of cloth image, and image f2 carried out binary conversion treatment, and go to step 6, provide the testing result signal that there is defective in the bamboo bar.
Vertical polishing is carried out on surface to the bamboo bar, in bamboo bar image, bamboo strip defect: borehole, mouldy, surf green is regional and specific luminance is darker mutually in the normal region on bamboo bar surface; The parallelogram defect area and the defect area of cutting sth. askew show as the shadow region of bamboo bar side in image, specific luminance is darker mutually with the normal region on bamboo bar surface.Because above five kinds of bamboo strip defect zones are in gray level image, gray-scale value is less, and area is relatively large, can judge through step 4 whether the bamboo bar exists this five defective.
Five, the detection of the little defective of bamboo bar may further comprise the steps:
1. for the image f ' in bamboo bar zone,, be divided into limited zonule by n pixel wide (n is smaller or equal to 10) along the length direction of bamboo bar;
2. use gradient operator is calculated the Grad of each pixel;
3. add up the gray-scale value Gi of gradient maximal value institute corresponding pixel points in each zonule respectively;
4. for the Gi in all zonules, find out minimum value Gmin wherein;
5. calculate the average gray u of bamboo bar area image f ', (d is test in advance if the difference of u and Gmin is less than d, calculate the u of qualified bamboo bar image gained and the gray scale difference value between the Gmin), think that then there is not defective in this width of cloth image, go to step 6, provide the testing result signal that this image does not have defective;
6. getting segmentation threshold is Gmin, and image f ' is carried out binary conversion treatment, obtains bianry image f ".In image f ', gray-scale value is defect area smaller or equal to the pixel of Gmin.To bianry image f " in the defect area processing of labelling; calculate area A rea, the length breadth ratio Scale of each defect area; if Area 〉=Ta and | Scale-1|≤ξ or Scale 〉=Tb}; (wherein Ta, Tb, ξ are respectively the pixel sum of the minimum borehole of accuracy of detection under requiring, the length breadth ratio, the circularity scope of borehole in short crack); think that then there is defective in this image; go to step 6, provide the testing result signal that there is defective in the bamboo bar; Otherwise think that there is not defective in this width of cloth image, go to step 6, provide the testing result signal that this width of cloth image does not have defective.
For less borehole, crack and crackle, because its pixel wide is less, area is less, can not detect identification through step 4; But the Grad on its border is bigger, can detect identification through step 5.
Six, provide the testing result signal of bamboo bar image.
The invention has the beneficial effects as follows: the present invention can carry out online detection to the bamboo strip defect of the different colours depth, detects simultaneously along four surfaces on the bamboo bar length direction.The kind of detectable bamboo strip defect comprises as shown in Figure 3: borehole, mouldy, crack, crackle, parallel four limits, triangular strip, surf green, cut sth. askew, effectively solve because erroneous judgement and the omission to bamboo strip defect that human factor caused.Detect effect as shown in Figure 4, bamboo strip defect: borehole, mouldy, crack, crackle, parallel four limits, surf green, cutting sth. askew is shown as black in the drawings, and the triangular strip defective is shown as two boundary straight line of bamboo bar.
If adopting resolution is that 752 * 480 industrial camera, frame frequency are 60 frame/seconds, 16mm camera lens, visual field 70mm * 45mm; Computing machine adopts: the 2.0GHz of CPU Celeron, and internal memory 512MB, video card 64M, then laboratory test results is as shown in the table:
| The bamboo bar kind that detects | Working time | Identification | Identification error |
| The bamboo bar that big defective is arranged | 51.8～62ms | 96％ | 4％ |
| The bamboo bar that little defective is arranged | 75～82ms | 92％ | 8％ |
| The bamboo bar that does not have defective | 80～83ms | 97％ | 3％ |
The average handling time 80ms of every width of cloth image, the testing result accuracy rate is 92%, the detection speed of bamboo bar is greater than 0.87m/s.
The present invention is further described below in conjunction with drawings and Examples.
Description of drawings
Fig. 1 is an overall detection steps flow chart of the present invention;
Fig. 2 is the flow process of bamboo strip defect detection method;
Fig. 3 is 8 kinds of bamboo strip defects that the present invention can detect.Among the figure: (a)-borehole, (b)-mouldy, (c)-crack, (d)-crackle, (e)-parallel four limits, (f)-triangular strip, (g)-surf green, (h)-cut sth. askew;
Fig. 4 is the display result after the present invention detects 8 kinds of bamboo strip defects.Among the figure: (a)-borehole, (b)-mouldy, (c)-crack, (d)-crackle, (e)-parallel four limits, (f)-triangular strip, (g)-surf green, (h)-cut sth. askew.
Embodiment
The bamboo bar width that the present invention detected is 14mm～25mm, and thickness is 6mm～10mm, and length is 0.9m～2.6m, and the bamboo bar belongs to the narrow long type workpiece.Vertical irradiation is carried out on four surfaces of using light source that the bamboo bar is gone up along its length, and in the bamboo bar image that obtains, two boundary straight line that the triangular strip defective shows as the bamboo bar have bigger angle; Borehole, mouldy, surf green is regional and specific luminance is darker mutually in the normal region on bamboo bar surface, gray-scale value is less, area is bigger; The parallelogram defect area and the defect area of cutting sth. askew are revealed as the shadow region of bamboo bar side in image, specific luminance is darker mutually with the normal region on bamboo bar surface, and gray-scale value is less, and area is bigger; For less borehole, crack and crackle, be the slender type feature, defect area is less, but the Grad on its border is bigger.
Concrete enforcement, as shown in Figure 1: along bamboo bar length direction, use camera that four surfaces of bamboo bar are taken continuously, obtain the gray level image on bamboo bar surface; Each frame of digital image of catching is carried out Flame Image Process,, judge on the segment bamboo bar in this two field picture whether have defective according to the bamboo strip defect detection method; If there is defective in the segment bamboo bar in this two field picture, think that then there is defective in whole bamboo bar, send the instruction that has defective to mechanism for sorting simultaneously; If the segment bamboo bar in all two field pictures of whole bamboo bar does not all have defective, think that then this root bamboo bar does not have defective; Send the instruction that does not have defective to mechanism for sorting simultaneously.
Described bamboo strip defect detection method as shown in Figure 2, may further comprise the steps:
One, to the view picture gray level image, background is removed on the border that obtains the bamboo bar by rim detection, obtains the image f in bamboo bar zone, and duplicates the view data f ' in bamboo bar zone, obtains two the boundary straight line L1 and the L2 on bamboo bar surface among the image f.Calculate the angle theta between L1 and the L2, if θ 〉=Tp, (Tp≤2 °) are thought that then two boundary straight line of this bamboo bar are not parallel, are had the defective of " triangular strip ", go to step 6, provide the testing result signal that there is defective in the bamboo bar; Otherwise think that two borders of bamboo bar are approximate parallel in this width of cloth image, proceed next step detection.
Two, to image f, carry out mean filter, remove the interference of noise spot, obtain image f1.
Three, to image f1, carry out the filtering texture: be the center with the current pixel point,, replace the gray-scale value of current pixel point with the maximal value in the capable left and right sides neighborhood pixels gray-scale value of current pixel.The overall width of left and right sides neighborhood territory pixel is more than or equal to half pixel wide d of bamboo texture in the image
H, smaller or equal to the pixel wide d between adjacent two textures of bamboo in the image
B(in advance by experiment, measure d
HAnd d
B).All pixel pointwises among the image f1 are handled, finally can be obtained the image f2 behind the filtering bamboo texture.
Four, the detection of the big defective of bamboo bar may further comprise the steps:
1. to image f2, adopt maximum kind spacing method to try to achieve average gray value u1, the u2 of segmentation threshold Th and two classes;
2. if (D is in advance by experiment less than the experiment value D that measures in advance for the u1 that tried to achieve of certain width of cloth image and the gray scale difference value between the u2, qualified bamboo strip adoption maximum kind spacing method is handled, try to achieve the gray scale difference value between u1 and the u2), think that then this width of cloth image does not contain the big defective of bamboo bar, jump to step 5, carry out the detection of the little defective of bamboo bar;
3. in image f2, gray-scale value is defect area smaller or equal to the pixel of Th, the total sum of all pixels of the total num of bamboo strip defect area pixel point and bamboo bar zone among the statistical picture f2, (Pe is that accuracy of detection requires down if the ratio of hum and sum is less than decision threshold Pe, defect area accounts for the minimum decision threshold of bamboo bar total area number percent), then think this width of cloth image zero defect, jump to step 5, carry out the detection of the little defective of bamboo bar, otherwise think that there is defective in this width of cloth image, and image f2 carried out binary conversion treatment, and go to step 6, provide the testing result signal that there is defective in the bamboo bar;
Five, the detection of the little defective of bamboo bar may further comprise the steps:
1. for the image f ' in bamboo bar zone, the image in bamboo bar zone is pressed n pixel wide (n is smaller or equal to 10), on average be divided into limited zonule along the length direction of bamboo bar;
2. use gradient operator is calculated the Grad of each pixel;
3. add up the gray-scale value Gi of gradient maximal value institute corresponding pixel points in each zonule respectively;
4. for the Gi in all zonules, find out minimum value Gmin wherein;
5. calculate the average gray u of bamboo bar area image f ', if the difference d of u and Gmin (d calculates the u of qualified bamboo bar gained and the gray scale difference value between the Gmin for test in advance) thinks that then there is not defective in this width of cloth image, go to step 6, provide the testing result signal that this image does not have defective;
6. getting segmentation threshold is Gmin, and f ' carries out binary conversion treatment to bamboo bar area image, obtains image f ".In bamboo bar area image f ', gray-scale value is defect area smaller or equal to the pixel of Gmin.To image f " in the defect area processing of labelling; calculate area A rea, the length breadth ratio Scale of each defect area; if Area 〉=Ta and | Scale-1|≤ξ or Scale 〉=Tb}; (wherein Ta, Tb, ξ are respectively the pixel sum of the minimum borehole of accuracy of detection under requiring, the length breadth ratio, the circularity scope of borehole in short crack); think that then there is defective in this image; go to step 6, provide the testing result signal that there is defective in the bamboo bar; Otherwise think that there is not defective in this width of cloth image, go to step 6, provide the testing result signal that this width of cloth image does not have defective.
Six, provide the testing result signal of bamboo bar image.
For bamboo bar image, step 1 can judge whether the bamboo bar exists the defective of " triangular strip " after testing; Step 4 after testing can judge that the defect kind whether the bamboo bar exists has: borehole, mouldy, surf green, parallel four limits, cut sth. askew; Step 5 can judge that the defect kind whether the bamboo bar exists has: less borehole, crack, crackle after testing.
Claims (2)
1, based on the bamboo strip defect online test method of machine vision, it is characterized in that comprising the steps:
(a) along bamboo bar length direction, use camera that four surfaces of bamboo bar are taken continuously, obtain the gray level image on bamboo bar surface;
(b) each frame of digital image of catching is carried out Flame Image Process,, judge on the segment bamboo bar in this two field picture whether have defective according to the bamboo strip defect detection method;
(c) if there is defective in the segment bamboo bar in this two field picture, think that then there is defective in whole bamboo bar, send the instruction that has defective to mechanism for sorting simultaneously;
(d), think that then this root bamboo bar does not have defective if the segment bamboo bar in all two field pictures of whole bamboo bar does not all have defective; Send the instruction that does not have defective to mechanism for sorting simultaneously.
2,, it is characterized in that described bamboo strip defect detection method may further comprise the steps according to the bamboo strip defect online test method based on machine vision of claim 1:
(a) to the view picture gray level image, background is removed on the border that obtains the bamboo bar by rim detection, obtains the image f in bamboo bar zone, and duplicates the view data f ' in bamboo bar zone, obtains two the boundary straight line L1 and the L2 of bamboo bar among the image f; Calculate the angle theta between L1 and the L2, if θ 〉=decision threshold Tp, Tp≤2 ° think that then two boundary straight line of this bamboo bar are not parallel, have defective, go to step (f), provide the testing result signal that there is defective in the bamboo bar; Otherwise think that two boundary straight line of bamboo bar are approximate parallel in this width of cloth image, proceed next step detection;
(b) to image f, carry out mean filter, remove the interference of noise spot, obtain image f1;
(c) to image f1, carry out the filtering texture operation: be the center with the current pixel point,, replace the gray-scale value of current pixel point with the maximal value in the capable left and right sides neighborhood pixels gray-scale value of current pixel.The overall width of left and right sides neighborhood territory pixel is more than or equal to half pixel wide d of bamboo texture in the image
H, smaller or equal to the pixel wide d between adjacent two textures of bamboo in the image
BAll pixel pointwises among the image f1 are handled, finally can be obtained the image f2 behind the filtering bamboo texture;
(d) detection of the big defective of bamboo bar may further comprise the steps:
A. to image f2, adopt maximum kind spacing method to try to achieve average gray value u1, the u2 of segmentation threshold Th and two classes;
If B. the u1 that tried to achieve of image f2 and the gray scale difference value between the u2 think then that less than the experiment value D that measures in advance this width of cloth image does not contain the big defective of bamboo bar, jump to step (e), carry out the detection of the little defective of bamboo bar;
C. in image f2, gray-scale value is defect area smaller or equal to the pixel of Th, the total sum of all pixels among the total num of defect area pixel and the image f2 among the statistical picture f2 is if the ratio of num and sum, is then thought this width of cloth image zero defect less than decision threshold Pe, jump to step (e), carry out the detection of the little defective of bamboo bar, otherwise think that there is defective in this width of cloth image, and image f2 is carried out binary conversion treatment, go to step (f), provide the testing result signal that there is defective in the bamboo bar;
(e) detection of the little defective of bamboo bar may further comprise the steps:
A. for the image f ' in bamboo bar zone, along the length direction of bamboo bar, be divided into limited zonule by n pixel is wide, n is smaller or equal to 10;
B. use gradient operator, calculate the Grad of each pixel;
C. add up the gray-scale value Gi of gradient maximal value institute corresponding pixel points in each zonule respectively;
D. for the Gi in all zonules, find out minimum value Gmin wherein;
E. calculate the average gray u of bamboo bar area image f ',, go to step (f), provide the testing result signal that this image does not have defective if the difference of u and Gmin, thinks then that there is not defective in this width of cloth image less than the gray scale difference value d of qualified bamboo bar image;
F. getting segmentation threshold is Gmin, and image f ' is carried out binary conversion treatment, obtains bianry image f ".In image f ', gray-scale value is defect area smaller or equal to the pixel of Gmin; To bianry image f " in the defect area processing of labelling; calculate area A rea, the length breadth ratio Scale of each defect area; if Area 〉=Ta and | Scale-1|≤ξ or Scale 〉=Tb}; wherein Ta, Tb, ξ are respectively the pixel sum of the minimum borehole of accuracy of detection under requiring, the length breadth ratio, the circularity scope of borehole in short crack; think that then there is defective in this image; go to step (f), provide the testing result signal that there is defective in the bamboo bar; Otherwise think that there is not defective in this width of cloth image, go to step (f), provide the testing result signal that this width of cloth image does not have defective; (f) provide the testing result signal of bamboo bar image.
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