WO2020038389A1 - Procédé de reconnaissance de défaut négatif de joint de soudure - Google Patents

Procédé de reconnaissance de défaut négatif de joint de soudure Download PDF

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Publication number
WO2020038389A1
WO2020038389A1 PCT/CN2019/101716 CN2019101716W WO2020038389A1 WO 2020038389 A1 WO2020038389 A1 WO 2020038389A1 CN 2019101716 W CN2019101716 W CN 2019101716W WO 2020038389 A1 WO2020038389 A1 WO 2020038389A1
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Prior art keywords
weld
defect
image
negative
feature data
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PCT/CN2019/101716
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English (en)
Chinese (zh)
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董绍华
谢书懿
孙玄
张河苇
张来斌
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中国石油大学(北京)
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Publication of WO2020038389A1 publication Critical patent/WO2020038389A1/fr

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    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N23/00—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00
    • G01N23/02—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material
    • G01N23/06—Investigating or analysing materials by the use of wave or particle radiation, e.g. X-rays or neutrons, not covered by groups G01N3/00 – G01N17/00, G01N21/00 or G01N22/00 by transmitting the radiation through the material and measuring the absorption
    • G01N23/18—Investigating the presence of flaws defects or foreign matter

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  • the invention relates to the field of safety inspection of oil and gas pipelines, in particular to a method for identifying a defect of a weld negative.
  • X-ray digital real-time imaging detection technology has the characteristics of noisy images, low contrast, blurred weld edges and more textures. It is difficult to meet the required quality standards. In order to improve the quality of inspection objects, it is necessary to carry out subsequent computer image processing. And combined with the computer evaluation form to complete the automatic identification of defects in the weld.
  • due to many problems in the actual detection process such as motion-induced image capture blurring (edges and defects are blurred and rough), noise caused by various aspects is trapped in the sample to be detected, resulting in long recognition process and low accuracy. , which severely limits the application of image processing technology in this field; in addition, for the same workpiece, the angle of ray irradiation during detection is different, and the quality of the obtained image also varies greatly.
  • the purpose of the present invention is to provide a method for identifying defects in a weld film, which overcomes the lack of accuracy in amplitude and direction, and is superior in classification accuracy to the direct method of determining shape features and contours, and the traditional LTP, LBP, and CLBP texture feature methods. system.
  • the method for identifying a defect of a weld negative specifically includes: obtaining a weld negative of a weld by radiography; obtaining image texture feature data of a weld negative of a weld negative by using image recognition technology; The welding image texture feature data and the defect type of the weld backsheet are used to establish a recognition model by a machine learning algorithm; the weld image texture feature data of the weld backsheet to be inspected is obtained; and the weld image texture feature is based on the weld backsheet to be inspected The data and the identification model are used to calculate and obtain the defect category of the weld backsheet to be detected.
  • the use of image recognition technology to obtain the weld image texture feature data of the welding film includes: using the image recognition technology to obtain the size information, the symbol information, and the central region information of the welding film. , Obtaining the texture feature data of the weld image of the weld negative based on the size information, the symbol information, and the central area information.
  • obtaining the size information of the welding film by using the image recognition technology includes: obtaining the size information of the welding film by the following formula;
  • a and b are custom constants
  • TH is a predetermined threshold
  • p is the number of pixels.
  • obtaining the symbol information of the weld negative by using image recognition technology includes: obtaining the symbol information of the weld negative by calculating by the following formula;
  • CLTP_S * is the symbol information
  • TH is a predetermined threshold
  • p is the number of pixels.
  • obtaining the center region information of the welding film by using image recognition technology includes: obtaining the center region information of the welding film by the following formula;
  • CLTP_C * is the central area information
  • TH1 is the average value of the image pixels.
  • establishing a recognition model by a machine learning algorithm based on the texture image feature data of the welding seam and the defect category of the welding film further includes: obtaining the detection using a defect edge detection and tracking processing technology.
  • the characteristic parameters include: welding plate number, image length pixel, image width pixel, gray difference between the defect and the background, relative position of the defect, gray deviation of the defect itself, Defect aspect ratio, equivalent area, circularity, entropy, correlation, moment of inertia, energy.
  • establishing a recognition model by a machine learning algorithm based on the welding image texture feature data and the welding seam film defect type includes: according to the welding seam image texture feature data and the welding seam
  • the defect categories of the negatives are used to build an SVM model using an SVM (Support Vector Machine) classification algorithm.
  • the establishing the SVM model by the SVM classification algorithm further includes: obtaining a number of categories of the classifier in the SVM model by a global optimization method; and constructing each category according to a category difference exclusion method.
  • the classifier; the categories of the classifier include: cracks, circular slag inclusions, strip slag inclusions, air holes, not welded through, and not fused.
  • the present invention also provides an electronic device including a memory, a processor, and a computer program stored on the memory and executable on the processor.
  • the processor executes the computer program, the method for identifying the above-mentioned weld film defect identification method is implemented. step.
  • the present invention also provides a computer-readable storage medium on which a computer program is stored.
  • the computer program is executed by a processor, the steps of the above-mentioned method for identifying a defect in a weld film are implemented.
  • the method and system for identifying the defect of a weld negative film provided by the present invention effectively solve the problems of inaccurate texture accuracy and inaccurate judgment of the characteristics of the weld negative identification, and greatly improve the accuracy of the edge detection and defect tracking identification of the weld negative image. Laying the foundation for development.
  • FIG. 1 is a schematic flowchart of a method for identifying a defect of a weld negative film according to an embodiment of the present invention
  • CLTP complete local three-valued mode
  • FIG. 3 is a schematic structural diagram of an SVM multi-class classifier according to an embodiment of the present invention.
  • FIG. 4 is a schematic structural diagram of a welding film defect recognition system according to an embodiment of the present invention.
  • FIG. 5 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
  • the standardized image processing technology for weld negatives includes image preprocessing and segmentation of image weld areas. , Defect feature extraction, defect classification and recognition, and finally display the defect results. Therefore, a complete computer intelligent evaluation system is a very important research branch of radiographic inspection.
  • an embodiment of the present invention provides a welding film defect recognition method.
  • the method specifically includes: S101 is obtained by radiography technology Weld seam negatives of the weld seam; S102 uses image recognition technology to obtain the seam image texture feature data of the seam negative film; S103 establishes a recognition model through a machine learning algorithm based on the seam image texture feature data and the defect type of the seam negative film; S104 Obtaining the texture feature data of the weld image of the to-be-detected backsheet; S105 calculating and obtaining the defect category of the weld plate of the to-be-tested according to the texture feature data of the weld image of the to-be-detected backsheet and the recognition model.
  • a radiographic image of the weld seam is obtained by using radiographic imaging technology, and then a machine model is trained based on the known defect types of the weld seam and the texture feature data of the weld seam image identified according to image recognition technology.
  • the subsequent machine model obtains an identification model for identifying the type of the weld negative film defect; after obtaining the weld negative film to be inspected, the texture feature data of the weld image of the weld negative film to be inspected can be directly used with the recognition model. Determine the defect type of the weld; compared with the existing amplifier, this method not only has higher recognition accuracy, but also is more efficient.
  • the obtaining the weld image texture feature data of the weld negative by using image recognition technology includes: obtaining the size information, symbol information, and center area information of the weld negative by using image recognition technology.
  • the size information, the symbol information, and the central area information are used to obtain the texture feature data of the weld image of the weld negative.
  • the improved CTLP (Complete Local Local Ternary Patterns) algorithm proposed by the present invention is used to obtain the weld image texture feature data of the weld negative.
  • the extracted weld image texture feature method retains richer textures.
  • Structure information based on the reconstruction of the central descriptor, including the symbol descriptor and the size descriptor; it is mainly divided into three components: two components contain size information, symbol information, and the other component retains the center area of the weld image Information, to achieve the fusion of texture features and weld shape features, the characterization data is closer to the actual situation.
  • TH is a given fixed threshold
  • a and b are custom constants
  • CLTP_C * is equal to: Among them, TH1 is the average value of image pixels, and then use The modes are converted into encoded values, CLTP_S * CLTP_M * and CLTP_C * respectively corresponding to those shown in FIG. 2.
  • the CLTP algorithm mainly uses some amplitude information lost in the existing texture extraction algorithm to improve the recognition rate.
  • the symbol part can be re-assigned according to a given threshold, and finally modified CLTP_C * , CLTP_S * , CLTP_M * are used to represent the central area information, symbol information, and size information .
  • a represents a 3 ⁇ 3 sample block with a center pixel of 38, and the eight pixels in the neighborhood are [27,72,69,32,25,43,26,88];
  • CL CLTP_S * local
  • the three-valued encoding vector of the difference sign is [0, 2, 2, 0, 0, 1, 0, 2]; in d, the CLTP_M * operator value is [1,2,2,0,1,0,1 , 2];
  • a film defect library can also be established.
  • establishing a recognition model by a machine learning algorithm based on the texture image data of the weld image and the defect category of the weld film also includes: using defect edges
  • the detection and tracking processing technology obtains feature parameters of a predetermined category in the texture feature data of the weld image; establishes a defect feature database according to the feature parameters and the defect category of the weld negative; and establishes recognition through a machine learning algorithm according to the defect feature database model.
  • the characteristic parameters include: pad number, image length pixel, image width pixel, gray difference between defect and background, relative position of defect, gray deviation of defect itself, defect aspect ratio, equivalent area, circle Degree, entropy, correlation, moment of inertia, energy.
  • this embodiment mainly integrates the above shape features and texture features, and the corresponding film blackness is different to form different defect features to construct a defect feature database shown in Table 1 below.
  • the defect feature database contains parameters such as grayscale difference, equivalent area, circularity, entropy, correlation, etc., which facilitates the improvement of the recognition model by machine learning algorithms at the later stage.
  • the defect feature database further characterizes the characteristics of weld defects
  • the size, symbol information and central area information, the algorithm overcomes the lack of precision in amplitude and direction, and is superior to the simple shape feature contour direct judgment method and the traditional LTP, LBP, CLBP texture feature methods in classification accuracy.
  • establishing a recognition model by a machine learning algorithm based on the weld image texture feature data and the defect type of the weld backsheet includes: according to the weld image texture feature data and the defect of the weld backsheet
  • the SVM model is established by the SVM classification algorithm.
  • the establishment of the SVM model by the SVM classification algorithm further includes: obtaining the number of classes of the classifier in the SVM model through a global optimization method; constructing the classifiers of each class according to a class difference exclusion method; Categories include: cracks, round slag inclusions, strip slag inclusions, pores, not welded, and not fused.
  • M-SVM multi-classifier SVM model
  • a single class and the remaining classes can be used to determine the criteria for determining this classifier. Repeat the above process for all classes. Find out the value of each judgment function and compare these values. The largest category is the best classification structure of the sample.
  • M-SVM direct global optimization method
  • the discriminant function is: ST.y i (w T ⁇ (x i ) + b) ⁇ 1- ⁇ i
  • each classifier structure should preferentially adopt the method of class difference exclusion, that is, in the overall structure, firstly, the similarity is regarded as a category, and then based on the small changes between the similarities, the model algorithm is used to distinguish.
  • This classification has extremely high efficiency in classification accuracy and balance accuracy.
  • this paper constructs six types of defects, such as cracks, circular slag inclusions, strip slag inclusions, pores, unwelded, unfused, and other typical defects. As shown in Figure 3 below.
  • the calculation parameters include: image length pixels, image width pixels, the gray difference between the defect and the background ⁇ h, the relative position d of the defect, the gray deviation of the defect itself ⁇ , the defect aspect ratio, the equivalent area S / C, and the circularity e.
  • the SVM data classification technology based on the defect database is applied.
  • the CTLP mode greatly improves the accuracy of edge detection and defect tracking identification of welded negatives.
  • the SVM defect classification model enables automatic recognition and judgment of image defects. The accuracy has been greatly improved, and has basically reached the level of industrial application.
  • the above method was used to verify that the X-ray film of the weld at the 7.28 accident section of the West-East China Gas Pipeline shows the unfused characteristic at the lower left of position 38, and its computer system judgment is completely consistent with the result of manual evaluation.
  • the above method was used to verify that the second-line welded negative film of Shaanxi-Beijing in China showed the characteristics of incomplete penetration, and the main defects were located between the position points 6-7. This shows that the method has better accuracy and accuracy.
  • an embodiment of the present invention further provides a welding film defect recognition system.
  • the system includes a data acquisition unit, a model construction unit, and an identification unit.
  • the data acquisition unit is used for radiography technology.
  • the model construction unit is configured to pass the machine according to the weld image texture feature data and the defect type of the weld seam film
  • a learning algorithm establishes a recognition model;
  • the recognition unit is configured to obtain texture feature data of a weld image of a negative plate of the weld to be tested; and calculate and obtain the texture feature data of the weld image of the negative plate of the weld to be tested and the recognition model Defect category of the negative to be tested.
  • the identification and description of the texture features and shape features of pipeline defects, and the accurate judgment of defects, are the problems of automatic computer image recognition and the main problems facing intelligent welding.
  • the method and system for identifying weld film defects in the present invention solve the problem of inaccurate texture accuracy and feature judgment of weld film identification.
  • An improved CTLP algorithm is provided, and the central descriptor is reconstructed, including the symbol descriptor.
  • the defect shape features are obtained by classification, and cracks, slag inclusions, pores, unwelded, unfused, Defect characteristics such as strip defects.
  • the present invention uses the fusion processing technology of the improved CTLP algorithm and the SVM classification algorithm to make the weld defect feature description more accurate, and overcomes the shortcomings of the previous algorithms in terms of amplitude, directional accuracy, and easily confused defect features.
  • the contour feature direct judgment method is superior to the shape feature in the accuracy rate and the traditional LTP, LBP, CLBP texture feature description method; the pipe weld seam defect SVM method based on CLTP texture & shape feature has better accuracy, which makes the welding negative image
  • the accuracy of edge detection and defect tracking recognition has been greatly improved, laying a foundation for the development of weld feature recognition technology.
  • the present invention also provides an electronic device including a memory, a processor, and a computer program stored on the memory and executable on the processor.
  • the processor executes the computer program, the method for identifying the above-mentioned weld film defect identification method is implemented. step.
  • the present invention also provides a computer-readable storage medium on which a computer program is stored.
  • the computer program is executed by a processor, the steps of the above-mentioned method for identifying a defect in a weld film are implemented.
  • the electronic device 600 may further include a communication module 110, an input unit 120, an audio processing unit 130, a display 160, and a power source 170. It is worth noting that the electronic device 600 does not necessarily need to include all the components shown in FIG. 5; in addition, the electronic device 600 may also include components not shown in FIG. 5, and reference may be made to the prior art.
  • the central processing unit 100 is sometimes referred to as a controller or an operation control, and may include a microprocessor or other processor devices and / or logic devices.
  • the central processing unit 100 receives input and controls each of the electronic devices 600. Operation of parts.
  • the memory 140 may be, for example, one or more of a buffer, a flash memory, a hard drive, a removable medium, a volatile memory, a non-volatile memory, or other suitable devices.
  • the above-mentioned failure-related information can be stored, and a program for executing the related information can also be stored.
  • the central processing unit 100 may execute the program stored in the memory 140 to implement information storage or processing.
  • the input unit 120 provides input to the central processing unit 100.
  • the input unit 120 is, for example, a key or a touch input device.
  • the power source 170 is used to provide power to the electronic device 600.
  • the display 160 is used for displaying display objects such as images and characters.
  • the display may be, for example, an LCD display, but is not limited thereto.
  • the memory 140 may be a solid-state memory, such as a read-only memory (ROM), a random access memory (RAM), a SIM card, and the like. It may also be a memory that holds information even when power is off, can be selectively erased and is provided with more data, and an example of the memory is sometimes called EPROM or the like.
  • the memory 140 may also be some other type of device.
  • the memory 140 includes a buffer memory 141 (sometimes referred to as a buffer).
  • the memory 140 may include an application / function storage section 142 for storing application programs and function programs or a flow for performing operations of the electronic device 600 through the central processing unit 100.
  • the memory 140 may further include a data storage section 143 for storing data, such as contacts, digital data, pictures, sounds, and / or any other data used by the electronic device.
  • the driver storage section 144 of the memory 140 may include various drivers for the electronic device for communication functions and / or for performing other functions of the electronic device (such as a messaging application, an address book application, etc.).
  • the communication module 110 is a transmitter / receiver 110 that transmits and receives signals via the antenna 111.
  • a communication module (transmitter / receiver) 110 is coupled to the central processing unit 100 to provide input signals and receive output signals, which may be the same as the case of a conventional mobile communication terminal.
  • multiple communication modules 110 may be provided in the same electronic device, such as a cellular network module, a Bluetooth module, and / or a wireless local area network module.
  • the communication module (transmitter / receiver) 110 is also coupled to the speaker 131 and the microphone 132 via the audio processor 130 to provide audio output via the speaker 131 and to receive audio input from the microphone 132, thereby realizing general telecommunication functions.
  • the audio processor 130 may include any suitable buffers, decoders, amplifiers, and the like.
  • the audio processor 130 is also coupled to the central processing unit 100, so that the microphone 132 can be used to record on the unit, and the speaker 131 can be used to play the sound stored on the unit.
  • the embodiments of the present invention may be provided as a method, a system, or a computer program product. Therefore, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
  • computer-usable storage media including, but not limited to, disk storage, CD-ROM, optical storage, etc.
  • These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to work in a particular manner such that the instructions stored in the computer-readable memory produce a manufactured article including an instruction device, the instructions
  • the device implements the functions specified in one or more flowcharts and / or one or more blocks of the block diagram.
  • These computer program instructions can also be loaded on a computer or other programmable data processing device, so that a series of steps can be performed on the computer or other programmable device to produce a computer-implemented process, which can be executed on the computer or other programmable device.
  • the instructions provide steps for implementing the functions specified in one or more flowcharts and / or one or more blocks of the block diagrams.

Abstract

La présente invention concerne un procédé de reconnaissance de défaut négatif de joint de soudure, comprenant spécifiquement : l'acquisition d'un négatif de joint de soudure d'un joint de soudure au moyen d'une radiographie (S101) ; l'acquisition de données caractéristiques de texture d'image de joint de soudure du négatif de joint de soudure au moyen d'une technique de reconnaissance d'image (S102) ; l'établissement d'un modèle de reconnaissance par l'intermédiaire d'un algorithme d'apprentissage automatique sur la base des données caractéristiques de texture d'image de joint de soudure et de types de défaut négatif de joint de soudure (S103) ; l'acquisition des données caractéristiques de texture d'image de joint de soudure d'un négatif de joint de soudure à détecter (S104) ; et le calcul, sur la base des données caractéristiques de texture d'image de joint de soudure du négatif de joint de soudure à détecter et du modèle de reconnaissance pour acquérir le type de défaut du négatif de joint de soudure à détecter (S105). Cela permet de résoudre efficacement le problème d'imprécision de la précision de texture de reconnaissance de négatif de soudure et de la détermination de caractéristiques, de façon à augmenter considérablement la précision de détection de bord d'image négative de soudure et de suivi et de reconnaissance de défaut, et d'établir une base pour le développement d'une technique de reconnaissance de caractéristiques de joint de soudure.
PCT/CN2019/101716 2018-08-23 2019-08-21 Procédé de reconnaissance de défaut négatif de joint de soudure WO2020038389A1 (fr)

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