CN110490874A - Weaving cloth surface flaw detecting method based on YOLO neural network - Google Patents

Weaving cloth surface flaw detecting method based on YOLO neural network Download PDF

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CN110490874A
CN110490874A CN201910832559.3A CN201910832559A CN110490874A CN 110490874 A CN110490874 A CN 110490874A CN 201910832559 A CN201910832559 A CN 201910832559A CN 110490874 A CN110490874 A CN 110490874A
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flaw
neural network
training
yolo
picture
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刘淼
单鸣雷
纪世豪
张鹏
金振洲
汤一彬
韩庆邦
高远
姚澄
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Changzhou Campus of Hohai University
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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
    • G—PHYSICS
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    • G—PHYSICS
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    • 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
    • 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
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30108—Industrial image inspection
    • G06T2207/30124—Fabrics; Textile; Paper
    • 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
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30168—Image quality inspection

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Abstract

The present invention discloses a kind of weaving cloth surface flaw detecting method, comprising: the cloth exterior view piece of acquisition weaving in real time；Flaw identification is carried out to the weaving cloth exterior view piece that acquires in real time using the YOLO neural network detection model pre-established, obtain include flaw type and flaw location recognition result；And output flaw recognition result data.The building of YOLO neural network detection model includes flaw picture collection, processing, forming label and sample training process, using detecting and identify flaw based on the method for the supervised learning of big data, region where enabling established YOLO neural network detection model quickly to select flaw simultaneously detects corresponding type, the detection scheme of intelligent Fabric Defect can be provided for textile garment manufacturing enterprise, solve the problems such as artificial perching recall rate is low, speed is slow, personnel cost is high.

Description

Weaving cloth surface flaw detecting method based on YOLO neural network
Technical field
The present invention relates to textile surface Defect Detection technical fields, especially a kind of to be based on YOLO (You Only Look Once only has a look at) the weaving cloth surface flaw detecting method of neural network.
Background technique
It stands at present in the cloth inspector that textile garment manufacturing enterprise mainly passes through profession and passes through naked eyes before perching equipment It was found that fabric defect carries out the label of fault again.With continuing to optimize for Computer Vision Detection Technique, intelligent fabric inspecting system The labor intensity of artificial detection will be mitigated significantly, improves the efficiency and precision of the monitoring of production process fabric quality.
With the continuous development of China's textile industryization level, the requirement to weaving cloth surface quality is also higher and higher, How rapidly and accurately to detect that flaw becomes a link very crucial in cloth manufacturing process.The cloth of mainstream detects skill Art is the machine vision technique based on image procossing, is divided into two steps substantially:
(1) first the image obtained by industrial camera is handled, then extracts feature, the method for this step mainly has statistics Four class such as method, Structure Method, Spectrum Method, modelling, wherein using it is relatively broad be statistic law and Spectrum Method；
(2) by trained classifier, the feature extracted to (1) is classified, and has BP refreshing using more classifier at present Through network, SVM etc..
But these methods are both limited by the low contrast and noise and fine defects between flaw and non-defect areas Similitude exists and detects the problems such as speed is slow and accuracy of identification is low, is unable to satisfy the accuracy and real-time of caused by spinning industrial production It is required that.
The continuous development of deep learning is that we provide better tools.Wherein, YOLO (You Only Look Once only has a look at) be the target detection deep neural network based on convolutional network, because its on small target deteection table The high frame rate and high-accuracy that reveal and be employed in many real-time detecting systems.
Summary of the invention
The technical problem to be solved in the present invention is to be examined automatically using the surface blemish of YOLO neural fusion weaving cloth It surveys, promotes the accuracy and real-time of Defect Detection in textile industry cloth production process.
The technical scheme adopted by the invention is as follows: a kind of weaving cloth surface flaw detecting method, comprising:
The cloth exterior view piece of acquisition weaving in real time；
Flaw knowledge is carried out to the weaving cloth exterior view piece acquired in real time using the YOLO neural network detection model pre-established , do not obtain include flaw type and flaw location recognition result；
Export flaw recognition result data.
Optionally, the method for building up of the YOLO neural network detection model includes:
Acquire multiple Fabric Defect pictures；
Fabric Defect picture is pre-processed；
The calibration that defect areas and type are carried out to pretreated Fabric Defect picture, obtains the flaw of each Fabric Defect picture Label；
Each Fabric Defect picture and its flaw label are divided into training set sample and test set sample；
Using training set sample and test machine sample, using computer graphics processor GPU to the YOLO depth mind built in advance It is trained through network, obtains YOLO neural network detection model.
When establishing YOLO neural network detection model, should shooting Fabric Defect picture as much as possible, cover as far as possible Various known flaw types, the target detection model recall rate and accuracy rate for this training being obtained are all higher.
Optionally, the pretreatment includes that Fabric Defect picture is normalized: the cloth after equal proportion is scaled In the blank picture of flaw picture filling presetted pixel size, the then pixel by blank sheet on piece in addition to Fabric Defect picture It is stuffed entirely with as setpoint color.
Optionally, the pixel size of the blank picture is 416*416, and blank picture can cover the cloth flaw after scaling Defect picture；The setpoint color is grey.
The above normalized processing of picture can keep the original textural characteristics of picture as far as possible, so that training obtained YOLO model inspection accuracy is higher.
Optionally, the pretreatment further includes carrying out data enhancing to the Fabric Defect picture obtained after normalized. The strategies such as existing turnover translation moving can be used in data enhancing.
Optionally, the present invention selects mode to carry out defect areas and kind to pretreated Fabric Defect picture by artificial frame The flaw type of the upper left angle point in each calibration region and bottom right angular coordinate and corresponding region is stored as label by the calibration of class.
Optionally, the flaw less for elongated, pixel (such as wrong flower and latitude), using multiple continuous small frames into Rower is fixed.Flaw proportion in rectangle frame can be improved.
Optionally, the training set sample and test machine sample random division, division proportion 9:1.
Optionally, described be trained to the YOLO neural network built in advance includes:
S1 is arranged training parameter: setting 10000 for iterative steps epochs, and learning rate optimizer optimizer is arranged For ' adam', 64, and the size of 9 priori frame anchor box of setting are set by crowd number of training batch_size；
Training YOLO neural network: training set sample data and each sample corresponding flaw type data are input to YOLO by S2 Model training is carried out in neural network；
Model measurement: S3 every time after training, is tested, if model using the model that test set sample obtains training To the flaw recall rate in test set sample be more than 95% and Detection accuracy is not less than 95%, then obtains last time training Model as final YOLO neural network detection model；Otherwise model last time training obtained is as currently wait train YOLO neural network, step S2-S3 is repeated, until obtain final YOLO neural network detection model.
Optionally, the size of the priori frame is arranged by k-means clustering method, comprising: randomly selects 9 flaw squares Cluster centre of the shape frame object as training set distributes each object according to the distance between each object and each cluster centre To apart from nearest cluster centre, one sample of every distribution, cluster centre is recalculated once, duplicate allocation process, until not having There is cluster centre to change again, the size of the value i.e. 9 priori frame of current 9 cluster centres.Pass through k-means clustering method The size for determining and being arranged 9 priori frame anchor box, can make model obtain more accurately target sizes in the training process And location expression.
Beneficial effect
(1) present invention utilizes the target detection model of YOLO neural network Fabric Defect, using it on small target deteection The high frame rate and high-accuracy that show ensure inspection, it can be achieved that for the real-time detection of surface blemish in cloth production process Survey the accuracy of result；
(2) when establishing YOLO neural network detection model, the present invention is adopted by manually demarcating to defect areas and type With the supervised learning detection algorithm based on big data, therefore it can be good at the study from data and improved to effectively expressing The accuracy rate of detection；
(2) the target detection recognition methods based on deep learning that the present invention provides is by feature construction and fusion for classification at one A entirety, i.e. input are initial data, direct output category result, do not need artificial constructed feature, are more suitable for solving complicated The automatic detection identification problem of Fabric Defect under grain background；
(3) classification method of the present invention uses YOLO deep neural network frame, which will test task as regression problem It handles, directly obtains bounding box coordinates and confidence level comprising object by all pixels of entire image and classification is general Rate is significantly better than R-CNN, Fast R-CNN even depth learning network frame in detection speed.And it is clustered using k-means Algorithm determines the size of priori frame anchor box, and YOLO model is made to obtain more accurately target sizes and position in the training process Description is set, Defect Detection accuracy rate is improved.
Detailed description of the invention
Fig. 1 show model foundation of the present invention to a kind of specific embodiment method flow schematic diagram of real-time detection process；
Fig. 2 is picture normalization processes result figure provided by the present invention；
Fig. 3 is two different picture flaw mark modes provided by the present invention；
Fig. 4 is YOLO deep neural network structural schematic diagram provided by the present invention；
Fig. 5 is the operation principle schematic diagram of YOLO network provided by the present invention；
Fig. 6 is detection effect illustrated example of the present invention.
Specific embodiment
It is further described below in conjunction with the drawings and specific embodiments.
With reference to Fig. 1, a kind of weaving cloth surface flaw detecting method of the present invention, comprising:
The cloth exterior view piece of acquisition weaving in real time；
Flaw knowledge is carried out to the weaving cloth exterior view piece acquired in real time using the YOLO neural network detection model pre-established , do not obtain include flaw type and flaw location recognition result；
Export flaw recognition result data.
Wherein, the method for building up of the YOLO neural network detection model includes:
Acquire multiple Fabric Defect pictures；
Fabric Defect picture is pre-processed；
The calibration that defect areas and type are carried out to pretreated Fabric Defect picture, obtains the flaw of each Fabric Defect picture Label；
Each Fabric Defect picture and its flaw label are divided into training set sample and test set sample；
Using training set sample and test machine sample, using computer graphics processor GPU to the YOLO depth mind built in advance It is trained through network, obtains YOLO neural network detection model.
Embodiment
Embodiment as shown in Figure 1, when establishing YOLO neural network detection model, data acquisition phase should be utilized and be taken the photograph The shooting Fabric Defect picture as much as possible such as camera, to cover various known flaw types, so that the target inspection that training obtains It surveys model recall rate and accuracy rate is all higher.
Flaw picture is normalized in data preprocessing phase, which is normalization process by picture Original length-width ratio zooms to correspondingly sized, is filled among 416*416 blank picture, other vacant pixels are all filled out as grey, To keep the original textural characteristics of picture as far as possible, as shown in Figure 2；And by overturning, translation strategy to the flaw figure after normalization Piece carries out data enhancing.
In picture calibration phase, the production of flaw label comprising steps of
(1) marker software can be used, defect areas and flaw type are manually demarcated with rectangle frame, wherein for elongated, pixel Such as wrong flower of less flaw and latitude, are demarcated with multiple continuous small frames, so as to improve flaw in rectangle frame institute's accounting Example, as shown in Figure 3；
(2) transverse and longitudinal coordinate of the upper left angle point of rectangle frame and bottom right angle point is stored in txt file, while the rectangle frame is wrapped The flaw type contained is also recorded in file.
Existing graphic plotting modification software, such as labelImg software can be used in the marker software, only for convenience of in people The choosing of work frame can automatically or artificially obtain corresponding coordinate data.
Training set sample and test machine sample random division, division proportion are about 9:1, can be floated up and down.
The network structure for the YOLO neural network that the present embodiment is built in advance can refer to shown in Fig. 4, to YOLO neural network It is trained and includes:
S1 is arranged training parameter: setting 10000 for iterative steps epochs, and learning rate optimizer optimizer is arranged For ' adam', i.e., the learning rate of each network node parameter is dynamically adjusted, sets 64 for crowd number of training batch_size, Determining by k-means clustering method and 9 priori frame anchor box of setting size；
Training YOLO neural network: training set sample data and each sample corresponding flaw type data are input to YOLO by S2 In neural network, according to the parameter of setting, with computer graphics processor GPU training objective detection model；；
Model measurement: S3 every time after training, is tested, if model using the model that test set sample obtains training To the flaw recall rate in test set sample be more than 95% and Detection accuracy is not less than 95%, then obtains last time training Model as final YOLO neural network detection model；Otherwise model last time training obtained is as currently wait train YOLO neural network, step S2-S3 is repeated, until obtain final YOLO neural network detection model.
The subsequent real-time detection that Fabric Defect is carried out using final YOLO neural network detection model: camera is utilized Weaving cloth surface image to be detected is acquired in real time, and according to trained YOLO network, whether real-time detection cloth surface has Flaw, and the flaw detected is classified and positioned.
Realize k-means clustering algorithm the specific steps are randomly select 9 flaw rectangle frame objects as training set Then cluster centre calculates the distance between each object and each cluster centre, each object is distributed to apart from it recently Cluster centre, one sample of every distribution, cluster centre can be recalculated according to object existing in cluster, repeat above-mentioned point With process, until not having cluster centre to change again, the value of 9 cluster centres obtains the size of 9 priori frames.
Weaving cloth surface flaw detecting method based on YOLO network in order to better understand, here to YOLO network Working principle is briefly described, as shown in Figure 5:
The image averaging of input is divided into S × S cell (as shown in Fig. 5 (a)) by a.YOLO network；
B. each cell can predict B bounding box (Bounding Box), provide these bounding boxes in vector form Information.The information of bounding box includes location information (rectangle frame center point coordinate, wide and high), confidence level (Confidence) with And the classification information of prediction object (shown in such as Fig. 5 (b) (c)).
C. for training data, after picture and label input, by five parameters of cell output, (rectangle frame central point is sat Mark, wide and high, confidence level) substituting into loss function, (loss function is used to calculate five parameters and mark that YOLO network query function goes out Five parameters between gap) calculate, weight is adjusted by backpropagation, increases the confidence level of correct flaw type, The confidence level of incorrect flaw type reduces；Data for acquiring in real time can also calculate five ginsengs after inputting YOLO network Number (rectangle frame center point coordinate, wide and high, confidence level) can obtain making to damage after calculating this five data by loss function The minimum bounding box of functional value, that is, the classfying frame finally needed, i.e. testing result are lost, (shown in such as Fig. 5 (d)).
In example shown in Fig. 6, it is shown that after detecting the cloth picture of four high-definition camera shootings as a result, figure In flaw is outlined automatically, while the upper left corner gives the type of flaw.
To sum up, the present invention realizes the real-time detection of Fabric Defect using YOLO deep learning method, can quickly frame select Region where flaw simultaneously detects corresponding type, and flaw recall rate is up to 95%, and Detection accuracy is up to 90%.Because having used depth Study has robustness high in this way, and accuracy rate is high, and fireballing advantage, for textile garment, manufacturing enterprise provides intelligence The detection scheme of Fabric Defect solves the problems such as artificial perching recall rate is low, speed is slow, personnel cost is high.
It should be understood by those skilled in the art that, embodiments herein can provide as method, system or computer program Product.Therefore, complete hardware embodiment, complete software embodiment or reality combining software and hardware aspects can be used in the application Apply the form of example.Moreover, it wherein includes the computer of computer usable program code that the application, which can be used in one or more, The computer program implemented in usable storage medium (including but not limited to magnetic disk storage, CD-ROM, optical memory etc.) produces The form of product.
The application is referring to method, the process of equipment (system) and computer program product according to the embodiment of the present application Figure and/or block diagram describe.It should be understood that every one stream in flowchart and/or the block diagram can be realized by computer program instructions The combination of process and/or box in journey and/or box and flowchart and/or the block diagram.It can provide these computer programs Instruct the processor of general purpose computer, special purpose computer, Embedded Processor or other programmable data processing devices to produce A raw machine, so that being generated by the instruction that computer or the processor of other programmable data processing devices execute for real The device for the function of being specified in present one or more flows of the flowchart and/or one or more blocks of the block diagram.
These computer program instructions, which may also be stored in, is able to guide computer or other programmable data processing devices with spy Determine in the computer-readable memory that mode works, so that it includes referring to that instruction stored in the computer readable memory, which generates, Enable the manufacture of device, the command device realize in one box of one or more flows of the flowchart and/or block diagram or The function of being specified in multiple boxes.
These computer program instructions also can be loaded onto a computer or other programmable data processing device, so that counting Series of operation steps are executed on calculation machine or other programmable devices to generate computer implemented processing, thus in computer or The instruction executed on other programmable devices is provided for realizing in one or more flows of the flowchart and/or block diagram one The step of function of being specified in a box or multiple boxes.
The embodiment of the present invention is described in conjunction with attached drawing above, but the invention is not limited to above-mentioned specific Embodiment, the above mentioned embodiment is only schematical, rather than restrictive, those skilled in the art Under the inspiration of the present invention, without breaking away from the scope protected by the purposes and claims of the present invention, it can also make very much Form, all of these belong to the protection of the present invention.

Claims (10)

1. a kind of weaving cloth surface flaw detecting method, characterized in that include:
The cloth exterior view piece of acquisition weaving in real time；
Flaw knowledge is carried out to the weaving cloth exterior view piece acquired in real time using the YOLO neural network detection model pre-established , do not obtain include flaw type and flaw location recognition result；
Export flaw recognition result data.
2. according to the method described in claim 1, it is characterized in that, the method for building up packet of the YOLO neural network detection model It includes:
Acquire multiple Fabric Defect pictures；
Fabric Defect picture is pre-processed；
The calibration that defect areas and type are carried out to pretreated Fabric Defect picture, obtains the flaw of each Fabric Defect picture Label；
Each Fabric Defect picture and its flaw label are divided into training set sample and test set sample；
Using training set sample and test machine sample, using computer graphics processor GPU to the YOLO depth mind built in advance It is trained through network, obtains YOLO neural network detection model.
3. according to the method described in claim 2, it is characterized in that, the pretreatment includes that Fabric Defect picture is normalized Processing: in the blank picture of the Fabric Defect picture filling presetted pixel size after equal proportion is scaled, then by blank picture On pixel in addition to Fabric Defect picture be stuffed entirely with as setpoint color.
4. according to the method described in claim 3, it is characterized in that, the pixel size of the blank picture is 416*416, blank sheet Piece can cover the Fabric Defect picture after scaling；The setpoint color is grey.
5. according to the method described in claim 2, it is characterized in that, the pretreatment further includes to the cloth obtained after normalized Flaw picture carries out data enhancing.
6. according to the method described in claim 2, it is characterized in that, select mode to pretreated Fabric Defect figure by artificial frame Piece carries out the calibration of defect areas and type, by the flaw of the upper left angle point in each calibration region and bottom right angular coordinate and corresponding region Defect type is stored as flaw label.
7. according to the method described in claim 6, it is characterized in that, the flaw less for elongated, pixel is (such as wrong flower and latitude Deng), it is demarcated using multiple continuous small frames.
8. according to the method described in claim 2, it is characterized in that, the training set sample and test machine sample random division, draw Dividing ratio is 9:1.
9. according to the method described in claim 2, it is characterized in that, it is described that packet is trained to the YOLO neural network built in advance It includes:
S1 is arranged training parameter: setting 10000 for iterative steps epochs, and learning rate optimizer optimizer is arranged For ' adam', 64, and the size of 9 priori frame anchor box of setting are set by crowd number of training batch_size；
Training YOLO neural network: training set sample data and each sample corresponding flaw type data are input to YOLO by S2 Model training is carried out in neural network；
Model measurement: S3 every time after training, is tested, if model using the model that test set sample obtains training To the flaw recall rate in test set sample be more than 95% and Detection accuracy is not less than 95%, then obtains last time training Model as final YOLO neural network detection model；Otherwise model last time training obtained is as currently wait train YOLO neural network, step S2-S3 is repeated, until obtain final YOLO neural network detection model.
10. according to the method described in claim 9, it is characterized in that, the size of the priori frame is set by k-means clustering method It sets, comprising: cluster centre of 9 flaw rectangle frame objects as training set is randomly selected, according in each object and each cluster The distance between heart distributes to each object apart from nearest cluster centre, one sample of every distribution, and cluster centre recalculates Once, duplicate allocation process, until there is no cluster centre to change again, the value i.e. 9 priori frame of current 9 cluster centres Size.
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CN111260614A (en) * 2020-01-13 2020-06-09 华南理工大学 Convolutional neural network cloth flaw detection method based on extreme learning machine
CN111340802A (en) * 2020-03-25 2020-06-26 嘉兴学院 Intelligent cloth inspecting machine capable of automatically inspecting cloth by adopting artificial intelligence technology
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CN111553898A (en) * 2020-04-27 2020-08-18 东华大学 Fabric defect detection method based on convolutional neural network
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CN111681229A (en) * 2020-06-10 2020-09-18 创新奇智(上海)科技有限公司 Deep learning model training method, wearable clothes flaw identification method and wearable clothes flaw identification device
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