CN109975308A - A kind of surface inspecting method based on deep learning - Google Patents
A kind of surface inspecting method based on deep learning Download PDFInfo
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; 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 OR CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
- G01N2021/8854—Grading and classifying of flaws
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
- G01N2021/8887—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges based on image processing techniques
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- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/30—Computing systems specially adapted for manufacturing
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Abstract
The present invention provides a kind of surface inspecting method based on deep learning, sweeps camera by line and acquires image, does left and right sides edge detection to acquisition image later and border extended obtains first object image；Background subtraction algorithm is carried out to first object image and obtains the second target image；Binaryzation is carried out to the second target image and obtains bianry image；RLE is carried out to bianry image to encode to obtain RLE image；Particle filter is carried out to RLE image and obtains defective particles position；First object image corresponding region progress ROI is found to defective particles position to cut to obtain cutting image, is able to detect close to the flaw and defect in the cylinder material of noise level.
Description
Technical field
The present invention relates to a kind of surface inspecting methods based on deep learning.
Background technique
" cylinder material " is a large amount of and with very high rate quantity-produced flat material.Common cylinder material includes
Fabric, metal plate, paper, film, non-woven fabrics and nonwoven plastic etc..It needs to detect web surface in process of production to send out
Existing tiny flaw and defect.Existing technology uses common binaryzation technology, is merely able to detect apparent big defect, for small
Defect and the relatively low unconspicuous defects detection of contrast do not come out, fail to detect that these flaws and defect may be led
It causes the cylinder material of batch to be unable to satisfy customer requirement, causes serious consequence.
Summary of the invention
The technical problem to be solved in the present invention is to provide a kind of surface inspecting method based on deep learning, meets and use
Family uses.
The present invention is implemented as follows: a kind of surface inspecting method based on deep learning, sweeps phase comprising at least one line
Machine includes the following steps:
Step 1 sweeps camera acquisition image by line, does left and right sides edge detection and border extended to acquisition image later
Obtain first object image；
Step 2 obtains the second target image to first object image progress background subtraction algorithm；
Step 3 obtains bianry image to the second target image progress binaryzation；
Step 4 encodes to obtain RLE image to bianry image progress RLE；
Step 5 obtains defective particles position to RLE image progress particle filter；
Step 6 finds first object image corresponding region to defective particles position and carries out ROI and cut to obtain to cut image.
Further, further include step 7, defect with deep learning carried out to image is cut using faster-rcnn algorithm
Particle precise classification.
Further, the step 1 is further specifically: sweeps camera by line and acquires image, uses the edge Canny later
Detection algorithm does left and right sides edge detection to acquisition image, then carries out border extended and obtain first object image.
Further, the step 2 is further specifically: is carried on the back using Gabor filtering algorithm to first object image
Scape subduction algorithm obtains the second target image.
Further, the step 3 is further specifically: using Niblack Binarization methods to the second target image into
Row binaryzation obtains bianry image.
Further, the step 4 is further specifically: using RLE run length encoding compression algorithm to bianry image
RLE is carried out to encode to obtain RLE image.
Further, the step 6 is further specifically: finds the to defective particles position using bilinear interpolation algorithm
One target image corresponding region carries out ROI and cuts to obtain cutting image.
The present invention has the advantage that the present invention may be connected to the web surface detection system of existing factory's Ethernet
System；Accurate classification can be made to defect；It is able to detect close to the flaw and defect in the cylinder material of noise level.
Detailed description of the invention
The present invention is further illustrated in conjunction with the embodiments with reference to the accompanying drawings.
Fig. 1 is the method for the present invention execution flow chart.
Specific embodiment
As shown in Figure 1, the present invention is implemented as follows: a kind of surface inspecting method based on deep learning, includes at least
One line sweeps camera, includes the following steps:
Step 1 sweeps camera acquisition image by line, is controlled later using Canny edge detection algorithm to acquisition image
Both sides of the edge detection, then carry out border extended and obtain first object image；
Step 2 obtains the second target figure to first object image progress background subtraction algorithm using Gabor filtering algorithm
Picture；
Step 3 obtains bianry image to the second target image progress binaryzation using Niblack Binarization methods；
Step 4 encodes to obtain RLE image using RLE run length encoding compression algorithm to bianry image progress RLE；
Step 5 obtains defective particles position to RLE image progress particle filter；
Step 6 finds first object image corresponding region progress ROI to defective particles position using bilinear interpolation algorithm
Cutting obtains cutting image.
Step 7 carries out defective particles precise classification with deep learning to cutting image using faster-rcnn algorithm.
A kind of specific embodiment of the present invention:
Cylinder material detection system contains at least one line and sweeps camera, for acquiring the cylinder material with flaw or defect
Partial image, each line sweep camera and be connected with an at least PC machine by network interface card, and PC machine is to acquiring image through the invention
Surface inspecting method based on deep learning carries out image procossing, analysis obtains each flaw and defect information, including position and
Defect type；PC machine sends server by Ethernet for defect information and is shown by server graphical user interface.
For this conventional machines study of the present invention with the embodiment of deep learning method, software-programming languages use C/C++
Language, has the advantages that cross-platform, can adapt to the PC machine of present Intel chip and the embedded device based on ARM.Have
Very strong versatility.Detailed step is as follows:
Step 1: left and right sides edge detection being done to acquisition image and border extended obtains target image 1；Step 2: to mesh
Logo image 1 carries out background subtraction algorithm and obtains target image 2；Step 3: binaryzation being carried out to target image 2 and obtains bianry image；
Step 4: RLE being carried out to bianry image and encodes to obtain RLE image；Step 5: particle filter being carried out to RLE image and obtains defect grain
Sub- position；Step 6: 1 corresponding region of target image progress ROI being found to defective particles position and cuts to obtain cutting image；Step
7: carrying out defective particles precise classification with deep learning to image is cut；
Further, step 1, left and right sides edge detection is done to acquisition image and border extended obtains target image 1；Implement
Algorithm is Canny edge detection algorithm.
Further, step 2, background detection algorithm is carried out to target image 1 and obtains target image 2, background subtraction algorithm is
Gabor filtering algorithm.
Further, step 3, binaryzation is carried out to target image 2 and obtains bianry image, Binarization methods are Niblack bis-
Value algorithm.
Further, step 4, it carries out RLE to bianry image to encode to obtain RLE image, RLE algorithm is RLE stroke length volume
Code compression algorithm.
Further, step 5, particle filter is carried out to RLE image and obtains defective particles position, particle filter algorithm.
Further, step 6,1 corresponding region of image object progress ROI is found to defective particles position to cut to obtain cutting figure
Picture, trimming algorithm are that bilinear interpolation algorithm is cut.
Further, step 7, defective particles precise classification is carried out with deep learning to cutting image, deep learning algorithm is
Faster-rcnn algorithm.
Although specific embodiments of the present invention have been described above, those familiar with the art should be managed
Solution, we are merely exemplary described specific embodiment, rather than for the restriction to the scope of the present invention, it is familiar with this
The technical staff in field should be covered of the invention according to modification and variation equivalent made by spirit of the invention
In scope of the claimed protection.
Claims (7)
1. a kind of surface inspecting method based on deep learning, it is characterised in that: sweep camera comprising at least one line, including as follows
Step:
Step 1 sweeps camera acquisition image by line, does left and right sides edge detection to acquisition image later and border extended obtains
First object image；
Step 2 obtains the second target image to first object image progress background subtraction algorithm；
Step 3 obtains bianry image to the second target image progress binaryzation；
Step 4 encodes to obtain RLE image to bianry image progress RLE；
Step 5 obtains defective particles position to RLE image progress particle filter；
Step 6 finds first object image corresponding region to defective particles position and carries out ROI and cut to obtain to cut image.
2. a kind of surface inspecting method based on deep learning according to claim 1, it is characterised in that: further include step
7, defective particles precise classification is carried out with deep learning to cutting image using faster-rcnn algorithm.
3. a kind of surface inspecting method based on deep learning according to claim 1, it is characterised in that: the step 1
Further specifically: camera is swept by line and acquires image, left and right two is done to acquisition image using Canny edge detection algorithm later
Side edge detection, then carry out border extended and obtain first object image.
4. a kind of surface inspecting method based on deep learning according to claim 1, it is characterised in that: the step 2
Further specifically: background subtraction algorithm is carried out to first object image using Gabor filtering algorithm and obtains the second target image.
5. a kind of surface inspecting method based on deep learning according to claim 1, it is characterised in that: the step 3
Further specifically: binaryzation is carried out to the second target image using Niblack Binarization methods and obtains bianry image.
6. a kind of surface inspecting method based on deep learning according to claim 1, it is characterised in that: the step 4
Further specifically: RLE is carried out to bianry image using RLE run length encoding compression algorithm and encodes to obtain RLE image.
7. a kind of surface inspecting method based on deep learning according to claim 1, it is characterised in that: the step 6
Further specifically: first object image corresponding region is found to defective particles position using bilinear interpolation algorithm and carries out ROI
Cutting obtains cutting image.
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| CN201910198350.6A CN109975308B (en) | 2019-03-15 | 2019-03-15 | Surface detection method based on deep learning |
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