CN104637049A - Automatic detection method for optical fiber coiling quality - Google Patents
Automatic detection method for optical fiber coiling quality Download PDFInfo
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- CN104637049A CN104637049A CN201410784983.2A CN201410784983A CN104637049A CN 104637049 A CN104637049 A CN 104637049A CN 201410784983 A CN201410784983 A CN 201410784983A CN 104637049 A CN104637049 A CN 104637049A
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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
- G06T7/0008—Industrial image inspection checking presence/absence
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V10/00—Arrangements for image or video recognition or understanding
- G06V10/40—Extraction of image or video features
- G06V10/50—Extraction of image or video features by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
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- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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- G06T2207/30108—Industrial image inspection
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Abstract
The invention discloses an automatic detection method for optical fiber coiling quality. The automatic detection method includes collecting an optical fiber coiling image in real time during optical fiber coiling, and performing filtering noise reduction on the optical fiber coiling image by a first median filtering algorithm; applying a threshold segmentation algorithm to the optical fiber coiling image subjected to the filtering noise reduction to acquire an optical fiber coiling area and a background display area; performing image smoothing on the optical fiber coiling area by a second median filtering algorithm to acquire the optical fiber coiling area subjected to image smoothing; performing image sharpening on the optical fiber coiling area subjected to image smoothing through Laplacian sharpening to acquire the optical fiber coiling area subjected to image sharpening; performing coiling section edge trajectory detection on the optical fiber coiling area subjected to image sharpening by a dual-threshold algorithm to acquire the image subjected to coiling section edge trajectory detection; calculating a Harr feature vector of the image subjected to coiling section edge trajectory detection, and determining whether a coiling fault exists or not and a fault mode according to the Harr feature vector by a support vector regression algorithm.
Description
Technical field
The present invention relates to a kind of automatic testing method of optical fiber winding quality.
Background technology
Along with fiber-optics gyroscope developing rapidly at home, the reliability of each application product to optical fibre gyro proposes very high requirement, and as the core component of inertia device, the reliability of fiber optic loop coiling directly affects the reliability of optical fibre gyro, so must improve the reliability of optical fiber coiling.
Optical fiber winding operation completes primarily of optical fiber winding machine, and optical fiber winding machine is applicable to the single mode of coiling various uses and multimode optical fiber etc.The basic functional principle of optical fiber winding machine is shut down by manual control ring winding machine or turns round operation; by video acquisition device, the realtime graphic around ring is shown at display; human eye judges around ring quality; go wrong and control ring winding machine and shut down and 7mm region after returning back to problem area; after utilizing artificial plectrum to be alignd by close fiber optic; continue operation ring winding machine to run, then repaired around ring fault.
At present, the quality determining method of optical fiber winding machine is the judgement completing opposing connection ring image based on the artificial naked eyes of optical fiber coiling imagery exploitation mostly, for the subjective consciousness deviation that human eye differentiates, will directly affect fiber optic loop performance in precision and efficiency between optical fibre gyro lag phase.Therefore, improved the detection technique of optical fiber winding, achieve robotization, the detection of accuracy will directly affect the reliability of optical fibre gyro.For around ring image testing requirement for faster around ring quality detection speed, sensing range dynamic process, sensing range is wide, and ring winding machine optical environment is complicated, uses Conventional visual Processing Algorithm, cannot meet the demand of image procossing.
Summary of the invention
Technical matters to be solved by this invention is: the automatic testing method providing a kind of quality of optical fiber winding fast, can under the prerequisite not changing existing ring winding machine working environment, accurate location, around ring position, accurately judges around circling point quality, thus ensures the quality of optical fiber coiling point.
The present invention includes following technical scheme:
An automatic testing method for optical fiber winding quality, comprises the steps:
(1) Real-time Collection optical fiber winding image in optical fiber winding process, adopts the first median filtering algorithm to carry out filtering and noise reduction to described optical fiber winding image;
(2) Threshold Segmentation Algorithm is adopted to obtain optical fiber winding region and territory, background display area to the optical fiber winding image after filtering and noise reduction;
(3) adopt Second Intermediate Value filtering algorithm to carry out image smoothing to optical fiber winding region and obtain the optical fiber winding region after image smoothing;
(4) adopt laplacian spectral radius process to carry out image sharpening to the optical fiber winding region after image smoothing and obtain the optical fiber winding region after image sharpening;
(5) dual threshold algorithm is adopted to carry out obtaining the image after ring section edge track detection around ring section edge track detection to the optical fiber winding region after image sharpening;
(6) image after opposing connection ring section edge track detection calculates Harr proper vector, utilizes support vector regression algorithm to determine whether there is fault and fault mode around ring according to Harr proper vector.
Described step specifically comprises the steps: in (6)
(a) to the every width imagery exploitation Harr feature calculation in fault pattern base around the position of circling point, edge and direction character to determine the Harr proper vector of every width image; The algorithm of support vector regression is utilized to set up classifying face model around ring quality according to the Harr proper vector of every width image and fault mode type;
B the image after () opposing connection ring section edge track detection calculates Harr proper vector, and the described classifying face model around ring quality of input utilizes the algorithm of support vector regression to determine whether there is fault and fault mode around ring.
The kernel function of support vector regression algorithm is gaussian kernel function.
Compared with prior art, tool has the following advantages in the present invention:
(1) existing optical fiber winding machine cannot initiatively detect quality in real time, workman's naked eyes are relied on to judge its defect completely, frequent appearance is to the erroneous judgement of fault mode and fail to judge, simultaneously can not Real time identification to tiny flaws such as the little space of coiling, the micro-superpositions of coiling, as a significant components of optical fibre gyro, the accuracy of detection of optical fibre gyro will be had a strong impact on defective fiber optic loop.The present invention proposes a kind of automatic testing method of the optical fiber winding quality based on image recognition, the method can under the prerequisite not changing existing ring winding machine working environment, accurate location, around ring position, accurately judges around circling point quality, thus ensures the quality of optical fiber coiling point.
(2) the present invention uses the image filtering Processing Algorithm of improvement to be optimized around ring image existing ring winding machine, existing ring winding machine image is made to have good anti-noise ability, utilize a kind of support vector regression algorithm based on gaussian kernel function simultaneously, improve the precision for Fault Pattern Recognition, reduce the complexity of image modeling, improve the arithmetic speed of algorithm identification.
(3) in order to meet the coiling dot image identification of multiple optical fiber winding image, dual threshold algorithm is proposed, the identification error occurred when effectively have modified rim detection.
(4) of the present inventionly adopting Harr algorithm to carry out feature extraction, support vector regression algorithm around ring fault signature mode method, improve the accuracy for detecting around ring feature mode.
Accompanying drawing explanation
Fig. 1 is based on the automatic testing process of the optical fiber winding quality of image recognition;
Fig. 2 be coiling normal time optical fiber winding image;
Fig. 3 is the optical fiber winding image of superposition fault;
Fig. 4 is the optical fiber winding image of void collapse;
Fig. 5 is the optical fiber winding image based on Harr feature;
Fig. 6 a is 8 direction character vector-valued images of the optical fiber winding gap fault after the binarization segmentation of black and white conversion;
Fig. 6 b is 8 direction character vector-valued images of the optical fiber winding superposition fault after the binarization segmentation of black and white conversion.
Embodiment
Below in conjunction with accompanying drawing, embodiments of the present invention are described in detail.
As shown in Figure 1, the automatic testing process of optical fiber winding quality mainly comprises the steps:
(1) carry out image denoising: when optical fiber winding machine carries out optical fiber winding operation, Real-time Collection optical fiber winding image, adopts median filtering algorithm to carry out filtering and noise reduction, to reject the interfere information in image to the optical fiber winding image gathered.Be illustrated in figure 2 coiling normal time light around ring image, be illustrated in figure 3 exist superposition fault time optical fiber winding image.Be illustrated in figure 4 optical fiber winding image when there is void collapse.
(2) Iamge Segmentation is carried out: adopt Threshold Segmentation Algorithm to carry out process to the optical fiber winding image after filtering and noise reduction and obtain optical fiber winding region and territory, background display area.
(3) image smoothing is carried out: for salt-pepper noise ubiquitous in optical fiber winding region, Gaussian noise and impulsive noise, the odd length pattern plate bolster that have employed median filtering algorithm carries out medium filtering to image, and the intermediate value extracting pixel exports as filtering parameter.Utilize medium filtering as the disposal route of image smoothing, maintain marginal information well, strong adaptability, the processing speed demand of whole testing process can be met simultaneously.Described odd length pattern plate bolster is the masterplate frame of 3*3.
(4) image sharpening is carried out: after employing medium filtering, along with the minimizing of noise, the edge of image is also by obfuscation, the Edge contrast of image is mainly used in strengthening contour edge, details and the Gray Level Jump part in image, form complete object boundary, smoothed image and do not increase unwanted noise prerequisite under select laplacian spectral radius disposal route.
(5) carry out around ring section edge track detection
For optical fiber winding region, image border has direction and amplitude two characteristics, usually along edge to move towards grey scale change mild, the pixel grey scale conversion perpendicular to edge trend is violent.Weigh rate of change and change direction that the most effective two eigenwerts of this change are exactly gray scale, represent with the amplitude of gradient vector and direction respectively.For consecutive image f (x, y), its directional derivative has local maximum in edge direction.Therefore, edge track detection asks local maximum and the direction of image f (x, y) gradient exactly.
The present invention adopts dual threshold algorithm to carry out obtaining the image after ring section edge track detection around ring section edge track detection to the optical fiber winding region after image sharpening, and the process around ring section edge track detection is as follows:
(1) process is carried out to the optical fiber winding region Gaussian smoothing device after image sharpening and obtain level and smooth rear data array s (x, y);
Be shown below: s (x, y)=g (x, y, σ) * f (x, y)
In formula, f (x, y) represents image, and σ is the distribution parameter of Gaussian function, controls smoothness.
(2) to level and smooth rear data array s (x, y) first order derivative operator is used to strengthen the marginal information of image space, obtain x, two array p (x, y) of y partial derivative, q (x, y), then its gradient magnitude m (x, y) and deflection θ (x, y) is calculated: wherein
θ (x, y)=arctan (q (x, y)/p (x, y));
(3) detect with dual threshold algorithm and be connected edge.
The value of magnitude image array m (x, y) is larger, and the image gradient value of its correspondence is also larger, and this is also not enough to the edge determining object.For determining edge, the ridge band in necessary refinement magnitude image, namely only the amplitude retained in image changes maximum point, namely carries out non-maxima suppression.The gradient magnitude ridge in refinement m (x, y) is carried out by the amplitude of all non-ridge values on suppression gradient line.This process can be refined into wide for m (x, y) ridge band the image n (x, y) only having a pixel wide.Dual threshold value algorithm acts on dual threshold τ to non-maxima suppression image n (x, y)
1and τ
2, the mean pixel due to the background area around ring image is 170, and the mean pixel of white fiber area is 195, and demand fulfillment τ
1≈ 2 τ
2, therefore select τ in dual threshold
2be 236, τ
1value 119, can obtain two threshold skirt image t
1(x, y) and t
2(x, y).Due to image t
2(x, y) obtains with high threshold, and therefore it contains little false edge, but t
2(x, y) may have interruption on profile.Dual threshold algorithm will at t
2in (x, y), edge conjunction is become profile, when reaching the end points of profile, this algorithm is at t
1edge is collected, until by t in (x, y)
2till recessed bond ing all in (x, y) gets up.
(6) carry out Fault Pattern Recognition: image after opposing connection ring section edge track detection calculates Harr feature, mainly comprise horizontal properties, vertical features, to corner characteristics and point patterns.In ring image, Harr feature dissimilar for all different sizes is all being extracted and is being strengthened by weight coefficient.Meanwhile, the Harr feature utilizing the algorithm opposing connection ring image of support vector regression (SVR) to be formed determines whether there is fault and fault mode around ring.Fault mode comprises superposition and space.
Specifically comprise the steps:
(a) to the every width imagery exploitation Harr feature calculation in fault pattern base around the position of circling point, edge and direction character to determine the Harr proper vector of every width image; The algorithm of support vector regression (SVR) is utilized to set up classifying face model around ring quality according to the Harr proper vector of every width image and fault mode type;
B the image after () opposing connection ring section edge track detection calculates Harr proper vector, and the described classifying face model around ring quality of input utilizes the algorithm of support vector regression (SVR) to determine whether there is fault and fault mode around ring.
Utilize support vector regression algorithm the edge pixel that cannot process after utilizing Harr algorithm accurately can be divided, and the generalization ability of algorithm own is very strong, utilizes adjustment algorithm parameter just can reach the object of control algolithm degree of accuracy.
To have in traditional neural network pattern-recognition that network structure is difficult to determine, crosses study, speed of convergence slowly, is easily absorbed in local minimum etc. not enough and do not consider the difference problem of each sample importance in standard support vector regression, optical fiber winding machine image information mostly is nonlinear images, utilize support vector regression algorithm by the sample image Nonlinear Mapping of input to higher dimensional space, carry out utilizing linear model following regression function at higher dimensional space, the kernel function K (x of support vector regression (SVR)
ix) gaussian kernel function conventional in technology is chosen as, because support vector regression algorithm attempts each optimum configurations by successive ignition, the use of gaussian kernel function can realize the Fast Convergent to each parameter estimation, thus reaches the estimation to regression equation fast.
The present invention adopts Harr algorithm to obtain around circling point image key points eigenvector information.As shown in Figure 5, the fritter near unique point calculates the value of the Harr proper vector of each fritter, to obtain the proper vector of a key point.Fig. 6 a is 8 direction character vector-valued images of the optical fiber winding gap fault after the binarization segmentation of black and white conversion; Fig. 6 b is 8 direction character vector-valued images of the optical fiber winding superposition fault after the binarization segmentation of black and white conversion.
The content be not described in detail in instructions of the present invention belongs to the known technology of those skilled in the art.
Claims (3)
1. an automatic testing method for optical fiber winding quality, is characterized in that, comprises the steps:
(1) Real-time Collection optical fiber winding image in optical fiber winding process, adopts the first median filtering algorithm to carry out filtering and noise reduction to described optical fiber winding image;
(2) Threshold Segmentation Algorithm is adopted to obtain optical fiber winding region and territory, background display area to the optical fiber winding image after filtering and noise reduction;
(3) adopt Second Intermediate Value filtering algorithm to carry out image smoothing to optical fiber winding region and obtain the optical fiber winding region after image smoothing;
(4) adopt laplacian spectral radius process to carry out image sharpening to the optical fiber winding region after image smoothing and obtain the optical fiber winding region after image sharpening;
(5) dual threshold algorithm is adopted to carry out obtaining the image after ring section edge track detection around ring section edge track detection to the optical fiber winding region after image sharpening;
(6) image after opposing connection ring section edge track detection calculates Harr proper vector, utilizes support vector regression algorithm to determine whether there is fault and fault mode around ring according to Harr proper vector.
2. method according to claim 1, is characterized in that: described step specifically comprises the steps: in (6)
(a) to the every width imagery exploitation Harr feature calculation in fault pattern base around the position of circling point, edge and direction character to determine the Harr proper vector of every width image; The algorithm of support vector regression is utilized to set up classifying face surface model around ring quality according to the Harr proper vector of every width image and fault mode type; ;
B the image after () opposing connection ring section edge track detection calculates Harr proper vector, and the described classifying face model around ring quality of input utilizes the algorithm of support vector regression to determine whether there is fault and fault mode around ring.
3. method according to claim 2, is characterized in that: the kernel function of support vector regression algorithm is gaussian kernel function.
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