CN111062919A - Bearing ring appearance defect detection method - Google Patents
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- 230000007547 defect Effects 0.000 title claims abstract description 86
- 238000001514 detection method Methods 0.000 title claims abstract description 31
- 238000000034 method Methods 0.000 claims abstract description 38
- 230000011218 segmentation Effects 0.000 claims abstract description 13
- 230000008447 perception Effects 0.000 claims abstract description 7
- 238000013528 artificial neural network Methods 0.000 claims abstract description 6
- 238000003062 neural network model Methods 0.000 claims description 11
- 238000012549 training Methods 0.000 claims description 8
- 238000004458 analytical method Methods 0.000 claims description 6
- 238000007781 pre-processing Methods 0.000 claims description 6
- 238000012545 processing Methods 0.000 claims description 4
- 238000012790 confirmation Methods 0.000 claims description 3
- 238000005520 cutting process Methods 0.000 claims description 3
- 230000002452 interceptive effect Effects 0.000 claims description 3
- 238000012216 screening Methods 0.000 claims description 3
- 238000006243 chemical reaction Methods 0.000 claims description 2
- 230000000694 effects Effects 0.000 description 3
- 238000012360 testing method Methods 0.000 description 3
- XEEYBQQBJWHFJM-UHFFFAOYSA-N Iron Chemical compound [Fe] XEEYBQQBJWHFJM-UHFFFAOYSA-N 0.000 description 2
- 230000004075 alteration Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000015572 biosynthetic process Effects 0.000 description 1
- 230000002950 deficient Effects 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 229910052742 iron Inorganic materials 0.000 description 1
- JEIPFZHSYJVQDO-UHFFFAOYSA-N iron(III) oxide Inorganic materials O=[Fe]O[Fe]=O JEIPFZHSYJVQDO-UHFFFAOYSA-N 0.000 description 1
- 238000003754 machining Methods 0.000 description 1
- 238000004519 manufacturing process Methods 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
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- 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
- G06T7/0004—Industrial image inspection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
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- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
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Abstract
The invention relates to a bearing ring appearance defect detection method, which comprises the following steps: s1, identifying the appearance image of the bearing ring by using a multilayer perception neural network algorithm, if a defect is detected, directly judging the bearing ring to be NG, otherwise, entering the step S2; and S2, identifying the bearing ring appearance image by using a segmentation method, judging the bearing ring appearance image to be NG if the defect is detected, and judging the bearing ring appearance image to be OK if the defect is not detected. The method has high defect detection accuracy, and can solve the compatibility problems caused by the difference of the surface gloss of the product and the difference between products processed by different equipment.
Description
Technical Field
The invention relates to the field of bearing ring production, in particular to a bearing ring appearance defect detection method.
Background
The existing bearing ring ferrule appearance defect detection method is carried out by adopting a traditional image processing method (a region segmentation method), the method has poor recognition effect on some special defects (such as collision damage, slight rust, seal damage, clamping damage, slight lathe tool line slight iron nail and the like), frequent omission detection, incompatibility with product diversity (difference of surface glossiness of the product and difference between products processed by different equipment), and unsatisfactory stability.
Disclosure of Invention
The invention aims to provide a bearing ring appearance defect detection method to solve the problems. Therefore, the invention adopts the following specific technical scheme:
a bearing ring appearance defect detection method can comprise the following steps:
s1, identifying the appearance image of the bearing ring by using a multilayer perception neural network algorithm, if a defect is detected, directly judging that the bearing ring is NG, otherwise, entering the step S2;
and S2, identifying the bearing ring appearance image by using a segmentation method, judging the bearing ring appearance image to be NG if the defect is detected, and judging the bearing ring appearance image to be OK if the defect is not detected.
Further, step S1 specifically includes the following processes:
s11, carrying out enhancement pretreatment on the transmitted bearing ring appearance image;
s12, acquiring image characteristic information;
s13, calling a multilayer perception neural network model to identify the image;
s14, processing the recognition result: if the recognition result is OK, the process proceeds to step S2; and if the identification result is NG, storing the original image and the detection result picture for subsequent checking and analysis.
Further, the method further includes step S0 of training the multilayer perceptive neural network model, where the step is performed as needed, and specifically includes the following processes:
s01, reading in appearance sample images of the bearing ring, wherein the appearance sample images comprise an OK sample image and an NG sample image;
s02, carrying out enhancement preprocessing on the image to increase the contrast of the image;
s03, creating a multi-layer perceptive neural network model handle;
s04, acquiring image characteristic information: scaling the image for multiple times and acquiring characteristic information;
s05, adding the image characteristics and the category ID to the model handle;
and S06, starting to train the model, and clearing the model handle after the training is finished.
Further, the method further includes a step of setting relevant parameters in the region segmentation method before performing step S2, where the step is performed as needed, specifically:
1. selecting an area to be detected, and automatically recording and storing a parameter A1 after clicking confirmation;
2. setting an area to accurately extract a parameter A2, importing a picture, dragging a slider, clicking to write when the accuracy of observation from an interactive interface is high, and automatically recording and storing the parameter A2;
3. performing region subdivision on the accurately extracted region again, and setting corresponding parameters A3, wherein the region is subdivided into a chamfer, an end face, an inner chamfer and an inner wall;
4. setting relevant parameter range thresholds of various defects, including minimum defect width, minimum defect height, minimum defect area, minimum defect gray value, maximum defect width, maximum defect height, maximum area and maximum gray value.
Further, step S2 specifically includes the following processes:
s21, carrying out data conversion on the incoming image;
s22, automatically limiting the approximate detection range according to the previously set parameter A1;
s23, accurately extracting a reference point of the detection area according to the set parameter A2;
s24, subdividing the accurate area again according to the set parameter A3;
s25, preprocessing the original image and increasing the image contrast;
s26, cutting out the parts to be detected according to the subdivided areas;
s27, automatically forming n small regions according to the set region division number n;
s28, solving the intersection of the n small areas to obtain a required detection area;
s29, detecting the image circularly, and identifying defects, specifically:
s291, obtaining histogram information of each region;
s292, judging whether the area has defects according to the mean square deviation of the histogram of the current area;
s293, if the defect exists, performing binarization according to the minimum gray value of the current region as a lower limit and a constant added to the lower limit as an upper limit, and screening out the defect according to a set size range;
s294, solving the coordinates and the size of the defect again;
s295, identifying a defect position and a defect area according to the obtained coordinates;
s296, if no defect exists, entering the next cycle, and detecting the next area;
s297, outputting the identification result, judging the image to be NG if the defect is detected, and saving the original image and the detection result image for subsequent viewing and analysis; otherwise, the result is determined as OK.
By adopting the technical scheme, the invention has the beneficial effects that: the method has high defect detection accuracy, and can solve the compatibility problems caused by the difference of the surface gloss of the product and the difference between products processed by different equipment.
Drawings
To further illustrate the various embodiments, the invention provides the accompanying drawings. The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate embodiments of the invention and, together with the description, serve to explain the principles of the embodiments. Those skilled in the art will appreciate still other possible embodiments and advantages of the present invention with reference to these figures. Elements in the figures are not drawn to scale and like reference numerals are generally used to indicate like elements.
FIG. 1 is a general flow diagram of the process of the present invention;
FIG. 2 is a flow chart of a method of the present invention for training a multi-layered perceptive neural network model;
FIG. 3 is a flow chart of the method of the present invention for identifying defects using a multi-layer perceptive neural network algorithm;
FIG. 4 is a flow chart of the method of the present invention for identifying defects using region segmentation;
FIG. 5 is a schematic representation of the results of a bearing ring test using the method of the present invention.
Detailed Description
The invention will now be further described with reference to the accompanying drawings and detailed description.
As shown in fig. 1 to 4, a method for detecting appearance defects of a bearing ring may include the following steps:
and S1, identifying the appearance image of the bearing ring by using a multilayer perception neural network algorithm, if a defect is detected, directly judging the bearing ring to be NG (bad, defective), and if not, entering the step S2. Step S1 specifically includes the following processes:
s11, carrying out enhancement pretreatment on the transmitted bearing ring appearance image, and improving the image contrast so as to facilitate the formation of obvious chromatic aberration between the defect and the background and facilitate the feature extraction of the subsequent steps;
s12, acquiring image characteristic information, wherein the image characteristic information comprises defect height, width, radius, area, gray scale, roundness, rectangularity, contour, shape, texture and the like;
s13, calling a multilayer perception neural network model to identify the image;
s14, processing the recognition result: if the identification result is OK (good ), the process proceeds to step S2; and if the identification result is NG, storing the original image and the detection result picture for subsequent checking and analysis.
It should be noted that the multi-layer perceptual neural network model needs to be trained before image detection is performed. That is, the method further includes step S0 of training the multi-layer perceptive neural network model, which is performed as required, for example, the training is required again when the bearing ring appearance sample image is increased to ensure the result is correct.
Specifically, the method comprises the following steps:
s01, reading in appearance sample images of the bearing ring, wherein the appearance sample images comprise an OK sample image and an NG sample image;
s02, carrying out enhancement preprocessing on the image to increase the contrast of the image;
s03, creating a multi-layer perceptive neural network model handle;
s04, acquiring image characteristic information: scaling the image for multiple times and acquiring characteristic information;
s05, adding the image characteristics and the category ID to the model handle;
s06, starting to train the model, and clearing the model handle after finishing training
And S2, identifying the bearing ring appearance image by using a segmentation method, judging the bearing ring appearance image to be NG if the defect is detected, and judging the bearing ring appearance image to be OK if the defect is not detected. Fig. 5 shows the bearing ring in which machining failure was detected.
It should be noted that the method further includes a step of setting relevant parameters in the region segmentation method before performing step S2, and the step is performed according to the need, for example, some parameters are changed correspondingly according to different product sizes, products processed by different processing devices (different surface gloss), and the like, specifically:
1. selecting an area to be detected, and automatically recording and storing a parameter A1 after clicking confirmation;
2. setting an area to accurately extract a parameter A2, importing a picture, dragging a slider, clicking to write when the accuracy of observation from an interactive interface is high, and automatically recording and storing the parameter A2;
3. performing region subdivision on the accurately extracted region again, and setting corresponding parameters A3, wherein the region can be subdivided into a chamfer, an end face, an inner chamfer, an inner wall and the like;
4. the threshold values of the relevant parameter ranges of various defects are set well, and can comprise defect minimum width, defect minimum height, defect minimum area, defect minimum gray value, defect maximum width, maximum height, maximum area, maximum gray value and the like.
Further, step S2 specifically includes the following processes:
s21, converting the data stream collected by the camera to obtain a BMP format digital image and storing the BMP format digital image in an internal memory;
s22, automatically limiting the approximate detection range according to the previously set parameter A1;
s23, accurately extracting a reference point of the detection area according to the set parameter A2;
s24, subdividing the accurate area again according to the set parameter A3;
s25, preprocessing the original image and increasing the image contrast;
s26, cutting out the parts to be detected according to the subdivided areas;
s27, automatically forming n small regions according to the set region segmentation number n, wherein n can be determined according to actual needs, for a round product, n is usually a multiple of 6, and for a rectangular product, n must be a multiple of 4, and for an algorithm, longitudinal segmentation is fixed to be 4;
s28, solving the intersection of the n small areas to obtain a required detection area;
s29, detecting the image circularly, and outputting an identification result, specifically:
s291, obtaining histogram information of each region;
s292, judging whether the current region has defects according to the histogram average difference of the current region, wherein the average difference parameter can be flexibly set according to actual requirements, for example, the slight defect to be detected can be set to be very small, the minimum is 0, only the obvious defect is detected, the maximum can be set to be 255, and the normal detection is generally set to be about 20;
s293, if the defect exists, performing binarization according to the minimum gray value of the current region as a lower limit and a constant added to the lower limit as an upper limit, and screening out the defect according to a set size range;
s294, solving the coordinates and the size of the defect again;
s295, identifying a defect position and a defect area according to the obtained coordinates;
s296, if no defect exists, entering the next cycle, and detecting the next area;
s297, after all the areas are detected, outputting a recognition result, if the defects are detected, judging the detection result to be NG, and storing an original image and a detection result picture for subsequent checking and analysis; otherwise, the result is determined as OK.
Internal test comparison results
Test objects: 100 products, wherein OK is 80, special defects are 5, and other natural defects are 15.
The detection result of the traditional algorithm is as follows: OK 90, NG 10, wherein 3 special defects are missed, and 7 natural defects are missed.
The method has the following detection results: OK 80, NG 20, all defects were detected.
In addition, the number of divided regions was changed (24 to 12): OK 84, NG 16, wherein all special defects are detected, and 4 natural defects are missed.
In summary, the multilayer perceptive neural network algorithm is used in cooperation with the segmentation method, the effect is ideal, and the desired effect can be achieved by setting different segmentation quantities according to different products.
While the invention has been particularly shown and described with reference to a preferred embodiment, it will be understood by those skilled in the art that various changes in form and detail may be made therein without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (5)
1. A bearing ring appearance defect detection method is characterized by comprising the following steps:
s1, identifying the appearance image of the bearing ring by using a multilayer perception neural network algorithm, if a defect is detected, directly judging the bearing ring to be NG, otherwise, entering the step S2;
and S2, identifying the bearing ring appearance image by using a segmentation method, judging the bearing ring appearance image to be NG if the defect is detected, and judging the bearing ring appearance image to be OK if the defect is not detected.
2. The method for detecting the appearance defects of the bearing ring according to claim 1, wherein the step S1 specifically comprises the following steps:
s11, carrying out enhancement pretreatment on the transmitted bearing ring appearance image;
s12, acquiring image characteristic information;
s13, calling a multilayer perception neural network model to identify the image;
s14, processing the recognition result: if the recognition result is OK, the process proceeds to step S2; and if the identification result is NG, storing the original image and the detection result picture for subsequent checking and analysis.
3. The method for detecting the appearance defect of the bearing ring according to claim 2, wherein the method further comprises a step S0 of training a multi-layer perceptive neural network model, and the step is performed as required, and specifically comprises the following processes:
s01, reading in appearance sample images of the bearing ring, wherein the appearance sample images comprise an OK sample image and an NG sample image;
s02, carrying out enhancement preprocessing on the image to increase the contrast of the image;
s03, creating a multi-layer perceptive neural network model handle;
s04, acquiring image characteristic information: scaling the image for multiple times and acquiring characteristic information;
s05, adding the image characteristics and the category ID to the model handle;
and S06, starting to train the model, and clearing the model handle after the training is finished.
4. The method for detecting the appearance defects of the bearing ring according to claim 1, further comprising the step of setting relevant parameters in a region segmentation method before the step of S2 is performed, wherein the step is performed as required, and specifically comprises the steps of:
1. selecting an area to be detected, and automatically recording and storing a parameter A1 after clicking confirmation;
2. setting an area to accurately extract a parameter A2, importing a picture, dragging a slider, clicking to write when the accuracy of observation from an interactive interface is high, and automatically recording and storing the parameter A2;
3. performing region subdivision on the accurately extracted region again, and setting corresponding parameters A3, wherein the region is subdivided into a chamfer, an end face, an inner chamfer and an inner wall;
4. setting relevant parameter range thresholds of various defects, including minimum defect width, minimum defect height, minimum defect area, minimum defect gray value, maximum defect width, maximum defect height, maximum area and maximum gray value.
5. The method for detecting the appearance defects of the bearing ring according to claim 4, wherein the step S2 specifically comprises the following steps:
s21, carrying out data conversion on the incoming image;
s22, automatically limiting the approximate detection range according to the previously set parameter A1;
s23, accurately extracting a reference point of the detection area according to the set parameter A2;
s24, subdividing the accurate area again according to the set parameter A3;
s25, preprocessing the original image and increasing the image contrast;
s26, cutting out the parts to be detected according to the subdivided areas;
s27, automatically forming n small regions according to the set region division number n;
s28, solving the intersection of the n small areas to obtain a required detection area;
s29, detecting the image circularly, and outputting an identification result, specifically:
s291, obtaining histogram information of each region;
s292, judging whether the area has defects according to the mean square deviation of the histogram of the current area;
s293, if the defect exists, performing binarization according to the minimum gray value of the current region as a lower limit and a constant added to the lower limit as an upper limit, and screening out the defect according to a set size range;
s294, solving the coordinates and the size of the defect again;
s295, identifying a defect position and a defect area according to the obtained coordinates;
s296, if the defect does not exist, entering the next cycle, and detecting the next area;
s297, outputting the identification result, judging the image to be NG if the defect is detected, and saving the original image and the detection result image for subsequent viewing and analysis; otherwise, the result is determined as OK.
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| CN112858329A (en) * | 2020-12-31 | 2021-05-28 | 慈溪迅蕾轴承有限公司 | Image detection method for bearing deformed ring |
| CN113532327A (en) * | 2021-07-15 | 2021-10-22 | 合肥图迅电子科技有限公司 | Detection method for chip shape in material tray based on stripe projection 3D imaging |
| CN116136393A (en) * | 2023-03-02 | 2023-05-19 | 宁波川原精工机械有限公司 | Bearing ring inner ring detection system and method |
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| CN113532327B (en) * | 2021-07-15 | 2023-09-12 | 合肥图迅电子科技有限公司 | Method for detecting chip morphology in tray based on stripe projection 3D imaging |
| CN116136393A (en) * | 2023-03-02 | 2023-05-19 | 宁波川原精工机械有限公司 | Bearing ring inner ring detection system and method |
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