CN116642953A - Defect ultrasonic imaging method and system under strong noise background - Google Patents

Abstract

The application relates to the technical field of defect ultrasonic detection, and provides a defect ultrasonic imaging method and system under a strong noise background, wherein the method comprises the steps of performing traversal scanning ultrasonic detection on a defect sample to be detected to obtain a first initial signal point by point, and performing traversal scanning ultrasonic detection on a reference sample to obtain a second initial signal point by point; carrying out noise reduction pretreatment on the first initial signal to obtain a first noise reduction signal, and carrying out noise reduction pretreatment on the second initial signal to obtain a second noise reduction signal; performing signal matching operation on the first noise reduction signal and the second noise reduction signal, performing background difference subtraction computation to obtain a scattering characteristic signal, performing multi-direction adjacent wave difference subtraction computation on the scattering characteristic signal to obtain a defect characteristic signal, and drawing an image according to the defect characteristic signal; judging whether the defect sample to be detected has defects according to the images. The method and the system provided by the application can realize clear imaging of the defects, and improve the accuracy and the detection efficiency of online defect detection.

Description

Defect ultrasonic imaging method and system under strong noise background

Technical Field

The application relates to the technical field of defect detection, in particular to a defect ultrasonic imaging method and system under a strong noise background.

Background

With the development of intelligent manufacturing, online detection can discover product defects in time and automatically or manually intervene in the manufacturing process, so that the manufacturing quality of the product is improved. Therefore, in order to effectively detect product defects and improve product quality, online detection is widely used for detecting product quality. The on-line detection method mainly comprises a laser ultrasonic method, an infrared thermal imaging method, a CCD imaging method and the like, wherein the laser ultrasonic method is more applied.

However, due to the reasons of strong scattering of the microstructure of the material, strong environmental noise and the like, a strong noise background is formed in the defect detection process, and the signal to noise ratio of a laser ultrasonic detection signal is often low, so that the defect edge imaging is fuzzy, and the accuracy of online real-time detection is affected.

Disclosure of Invention

The application provides a defect ultrasonic imaging method and system under a strong noise background, which can realize clear imaging of defects and improve the precision and the detection efficiency of online detection.

In order to achieve the above object, in a first aspect, the present application provides a defect ultrasonic imaging method in a strong noise background, including:

performing traversal ultrasonic detection on a defect sample to be detected to obtain a first initial signal point by point, and performing traversal ultrasonic detection on a reference sample to obtain a second initial signal point by point;

Performing noise reduction pretreatment on the first initial signal to obtain a first noise reduction signal, and performing noise reduction pretreatment on the second initial signal to obtain a second noise reduction signal;

performing signal matching operation on the first noise reduction signal and the second noise reduction signal, and performing background difference subtraction operation to obtain a scattering characteristic signal;

carrying out multidirectional adjacent wave differential subtraction calculation on the scattering characteristic signals to obtain defect characteristic signals;

drawing an image according to the defect characteristic signals;

judging whether the defect sample to be detected has defects according to the image, and evaluating defect characteristics.

In a possible implementation manner, performing the scanning ultrasonic detection on the defect sample to be detected to obtain a first initial signal, and performing the scanning ultrasonic detection on the reference sample to obtain a second initial signal includes:

and when the traverse scanning ultrasonic detection is carried out on the to-be-detected defect sample, single-point multiple excitation is adopted to obtain a plurality of first initial signals, and when the traverse scanning ultrasonic detection is carried out on the reference sample, single-point multiple excitation is adopted to obtain a plurality of second initial signals.

In a possible implementation manner, the performing noise reduction pretreatment on the first initial signal to obtain a first noise reduction signal, and performing noise reduction pretreatment on the second initial signal to obtain a second noise reduction signal includes:

Performing average operation on a plurality of first initial signals to obtain first noise reduction signals;

and carrying out average operation on the plurality of second initial signals to obtain the second noise reduction signals.

In a possible implementation manner, the performing noise reduction pretreatment on the first initial signal to obtain a first noise reduction signal, and performing noise reduction pretreatment on the second initial signal to obtain a second noise reduction signal includes:

carrying out average operation on a plurality of first initial signals to obtain first average signals;

performing primary noise reduction and secondary noise reduction on the first average signal to obtain a first noise reduction signal;

carrying out average operation on a plurality of second initial signals to obtain second average signals;

and performing primary noise reduction and secondary noise reduction on the second average signal to obtain the second noise reduction signal.

In a possible implementation manner, the performing a signal matching operation on the first noise reduction signal and the second noise reduction signal, and then performing a background difference subtraction operation to obtain a scattering feature signal includes:

performing cross-correlation operation on the first noise reduction signal and the second noise reduction signal to obtain a first maximum cross-correlation coefficient and a corresponding first delay time;

Matching the second noise reduction signal with the first noise reduction signal according to the first delay time to obtain a matching signal;

judging the magnitudes of the first maximum cross-correlation coefficient and a set cross-correlation coefficient threshold, and if the first maximum cross-correlation coefficient is smaller than the set cross-correlation coefficient threshold, performing differential operation on the first noise reduction signal and the matching signal to obtain the scattering characteristic signal; and if the first maximum cross-correlation coefficient is larger than the set cross-correlation coefficient threshold value, marking the scattering characteristic signal as zero.

In a possible implementation manner, the performing a subtraction calculation on the scattered characteristic signal by using a multi-directional adjacent wave difference to obtain a defect characteristic signal includes:

acquiring a 0-degree adjacent wave signal of the scattering characteristic signal, matching the 0-degree adjacent wave signal with the scattering characteristic signal to obtain a 0-degree matching signal, and obtaining a 0-degree defect characteristic signal according to the scattering characteristic signal and the 0-degree matching signal;

acquiring a 45-degree adjacent wave signal of the scattering characteristic signal, matching the 45-degree adjacent wave signal with the scattering characteristic signal to obtain a 45-degree matching signal, and obtaining a 45-degree defect characteristic signal according to the scattering characteristic signal and the 45-degree matching signal;

And acquiring a 90-degree adjacent wave signal of the scattering characteristic signal, matching the 90-degree adjacent wave signal with the scattering characteristic signal to obtain a 90-degree matching signal, and obtaining a 90-degree defect characteristic signal according to the scattering characteristic signal and the 90-degree matching signal.

In a possible implementation manner, the obtaining the 0 ° adjacent wave signal of the scattering feature signal, and matching the 0 ° adjacent wave signal with the scattering feature signal to obtain a 0 ° matching signal, and obtaining a 0 ° defect feature signal according to the scattering feature signal and the 0 ° matching signal includes:

selecting waveforms of the scattering characteristic signals adjacent to the scattering characteristic signals along the 0-degree direction as 0-degree adjacent wave signals, and performing cross-correlation operation on the 0-degree adjacent wave signals and the scattering characteristic signals to obtain a second maximum cross-correlation coefficient and corresponding second delay time;

matching the 0-degree adjacent wave signal with the scattering characteristic signal according to the second delay time to obtain the 0-degree matching signal;

and carrying out differential operation on the scattering characteristic signal and the 0-degree matching signal to obtain the 0-degree defect characteristic signal.

In a possible embodiment, the drawing the image according to the defect feature signal includes:

Setting a chromatographic interval and a chromatographic depth;

according to the chromatographic interval and the chromatographic depth, the defect characteristic signals are obtained into a two-dimensional chromatographic image based on a time-varying window energy mapping method;

performing binarization processing on the two-dimensional tomographic image;

according to the position information of each layer of image, mapping the pixel points in each layer of image after binarization processing into a three-dimensional space based on a spatial relationship to form discrete three-dimensional pixel points;

and obtaining a three-dimensional image according to the discrete three-dimensional pixel points.

In a possible implementation manner, the determining whether the defect sample to be detected has a defect according to the image, and performing defect feature evaluation include:

and judging whether a defect exists or not through an image recognition technology or comparing pixel values of the images, and confirming the position and the size of the defect.

In order to achieve the above object, in a second aspect, the present application provides a defect ultrasound imaging system in a strong noise background, including:

the scanning ultrasonic detection module is used for performing scanning ultrasonic detection on the defect sample to be detected to obtain an initial first initial signal, and performing scanning ultrasonic detection on the reference sample to obtain an initial second initial signal;

The signal processing module is used for carrying out noise reduction pretreatment on the first initial signal to obtain a first noise reduction signal, and carrying out noise reduction pretreatment on the second initial signal to obtain a second noise reduction signal; performing signal matching operation on the first noise reduction signal and the second noise reduction signal, and performing background difference subtraction operation to obtain a scattering characteristic signal; carrying out multidirectional adjacent wave differential subtraction calculation on the scattering characteristic signals to obtain defect characteristic signals; drawing an image according to the defect characteristic signals; judging whether the defect sample to be detected has defects according to the image, and evaluating defect characteristics.

The application has the beneficial effects that:

the application provides a defect ultrasonic imaging method and a system under a strong noise background, wherein firstly, scanning ultrasonic detection is carried out on a defect sample to be detected to obtain a first initial signal point by point, and traversing scanning ultrasonic detection is carried out on a reference sample to obtain a second initial signal point by point; performing noise reduction pretreatment on the first initial signal to obtain a first noise reduction signal, and performing noise reduction pretreatment on the second initial signal to obtain a second noise reduction signal; performing signal matching operation on the first noise reduction signal and the second noise reduction signal, and performing background difference subtraction operation to obtain a scattering characteristic signal; carrying out multidirectional adjacent wave differential subtraction calculation on the scattering characteristic signals to obtain defect characteristic signals; drawing an image according to the defect characteristic signals; judging whether the defect sample to be detected has defects according to the images, and evaluating the defect characteristics. According to the application, the background difference and the multidirectional adjacent wave difference method are fused, on one hand, the background difference is used for inhibiting the strong noise background of the microstructure of the material, the noise reduction treatment of the noise of the tissue structure of the material is realized, and meanwhile, the rapid extraction of the defect scattering characteristic signal is realized. On the other hand, the boundary detail characteristics of the micro defects are enhanced through the multidirectional adjacent wave difference, and the sensitivity to the directions of the micro defects such as cracks is improved, so that the problem of low defect imaging precision is effectively avoided while the online detection efficiency is improved, and the defect imaging quality is further improved.

Drawings

In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present application, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of a method of ultrasound imaging of defects in a noisy background according to some embodiments of the present application;

FIG. 2 is a point-by-point signal processing diagram of a method of ultrasound imaging of defects in a strongly noisy background according to some embodiments of the present application;

FIG. 3 is a flow chart of a method for obtaining a first noise reduction signal according to some embodiments of the present application;

FIG. 4 is a flowchart of another method for obtaining a first noise reduction signal according to some embodiments of the present application;

FIG. 5 is a flow chart of a method for obtaining a scatter signature in accordance with some embodiments of the present application;

FIG. 6 is a flow chart of a method for obtaining a defect signature for each direction in accordance with some embodiments of the present application;

FIG. 7 is a flowchart of a method for obtaining a 0 defect signature in accordance with some embodiments of the present application;

FIG. 8 is a flowchart of a method for rendering an image from defect signature in accordance with some embodiments of the present application;

FIG. 9 is a flow chart of a method of obtaining a two-dimensional tomographic image according to some embodiments of the present application;

FIG. 10 is a flow chart of a method for obtaining a three-dimensional image from a two-dimensional tomographic image according to some embodiments of the present application;

FIG. 11 is a block diagram of a defect ultrasound imaging system in a strong noise context in accordance with some embodiments of the present application.

Detailed Description

The technical solutions of the present application will be clearly and completely described in connection with the embodiments, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more of the described features. In the description of the present application, the meaning of "a plurality" is two or more, unless explicitly defined otherwise.

With the development of intelligent manufacturing, the additive manufacturing technology has the advantages of short period, high efficiency, material saving and the like compared with the traditional material reduction and equal material manufacturing technology, can realize the integrated macro-micro structure and the integrated material-design-manufacturing of complex components, and becomes one of key core technologies for improving the design and manufacturing capacity of the complex components in the high-precision fields such as aerospace and the like. However, because the forming process can be quickly cooled after being instantaneously heated and melted by a movable point heat source, and complex thermophysical phenomena such as solid-hot gas coupling and the like are involved, metallurgical defects are easily generated in the printing process, the forming mechanism is complex, the variety of the forming mechanism is various, and the defects including hot cracks, solidification cracks, metallurgical air holes, key holes, shrinkage cavities, unfused and the like are more characterized by span-scale interlayer distribution of tens of micrometers to several millimeters.

In order to realize the detection of the quality of the product produced by additive manufacturing, the common detection methods are off-line detection and on-line detection. As online detection can discover product defects in time and automatically or manually intervene in the manufacturing process, the manufacturing quality of the product is improved. In order to effectively detect product defects and improve product quality, online detection is widely used for detecting product quality. The on-line detection method mainly comprises a laser ultrasonic method, an infrared thermal imaging method, a CCD imaging method and the like, wherein the laser ultrasonic method is more applied.

However, additive manufacturing, which is a major cause of being able to provide a strong noise environment in a strong noise background, may result in low on-line detection resolution. The method specifically comprises the following steps: the complex online detection environments such as high temperature, high pressure, dust, vibration and the like in the additive manufacturing printing forming cavity are easy to introduce stronger environmental noise; the surface roughness of the additive package affects the penetration of the ultrasonic signal. These factors can result in the ultrasonic signal received by the ultrasonic signal receiver often being accompanied by a stronger noise signal. These noise signals tend to be stronger than the ultrasonic signals generated by the micro-defects, thereby submerging the defect signals in the noise signals, causing erroneous judgment and missed detection of the defects. Meanwhile, because the on-line detection is synchronously carried out in the manufacturing process, the manufacturing process of additive manufacturing can also influence the signal-to-noise ratio of ultrasonic signals and the detection of defects.

In addition, the main reasons for forming a strong noise background include not only the strong noise environment but also the characteristics of the material itself. For materials with strong scattering, such as a biphase material, the material microstructure non-uniformity and obvious anisotropy easily introduce material structure scattering noise; the internal and external factors are combined to cause low signal-to-noise ratio of the internal micro-defect ultrasonic scattering signal, and the characteristic signal is difficult to identify and extract. The material is manufactured by additive materials or products formed by the production process of subtractive material manufacturing, and because of the characteristic that the scattering generated by the material is strong, the internal microscopic structure scattering noise is similar to the frequency spectrum characteristic of the microdefect scattering characteristic signal, and the tissue structure noise of the strong scattering material can not be effectively restrained, so that the microdefect detection resolution is low and even difficult to detect.

Further, defects in the product often belong to micro defects, and because the amplitudes of photoacoustic scattering characteristic signals of the micro defects are weak, the imaging edges of the defects are often blurred, and the contrast is not high, so that the imaging quality and quantitative characterization accuracy of the defects are reduced. The conventional synthetic aperture focusing, full focusing and other defect characteristic signal enhancement methods are used for photoacoustic detection, and often require storage of full matrix data of traversing scanning points, so that the data storage capacity is large, and the real-time imaging efficiency is low.

In view of the foregoing, under the influence of the strong noise background and the self-characteristics of the microdefect, it is desirable to provide a defect imaging method capable of overcoming the influence of the strong noise background and realizing efficient clear imaging of the microdefect.

Therefore, the application provides a defect ultrasonic imaging method and a system under a strong noise background, wherein the method comprises the steps of carrying out scanning ultrasonic detection on a defect sample to be detected to obtain a first initial signal point by point, and carrying out traversing scanning ultrasonic detection on a reference sample to obtain a second initial signal point by point; carrying out noise reduction pretreatment on the first initial signal to obtain a first noise reduction signal, and carrying out noise reduction pretreatment on the second initial signal to obtain a second noise reduction signal; performing signal matching operation on the first noise reduction signal and the second noise reduction signal, and performing background difference subtraction operation to obtain a scattering characteristic signal; carrying out multi-direction adjacent wave differential subtraction calculation on the scattering characteristic signals to obtain defect characteristic signals; drawing an image according to the defect characteristic signals; judging whether the defect sample to be detected has defects according to the images, and evaluating the defect characteristics. The background difference and the multidirectional adjacent wave difference method are fused, so that on one hand, the background difference is used for inhibiting the strong noise background of the microstructure of the material, the noise reduction treatment of the tissue structure noise of the material is realized, and meanwhile, the rapid extraction of the defect scattering characteristic signals is realized. On the other hand, the boundary detail characteristics of the micro defects are enhanced through the multidirectional adjacent wave difference, and the sensitivity to the directions of the micro defects such as cracks is improved, so that the problem of low defect imaging precision is effectively avoided while the online detection efficiency is improved, and the defect imaging quality is further improved.

The following describes a defect ultrasonic imaging method and system under a strong noise background provided by the application with reference to the accompanying drawings and the detailed description.

Referring to fig. 1 and 2, an embodiment of the present application provides a method and a system for ultrasonic imaging of defects in a strong noise background, including:

step S1, performing traversal ultrasonic detection on a defect sample to be detected to obtain a first initial signal point by point, and performing traversal ultrasonic detection on a reference sample to obtain a second initial signal point by point.

In this embodiment, the defect sample to be measured is a sample of a product to be measured, which may be obtained by additive manufacturing or may be obtained by other manufacturing methods. Meanwhile, the reference sample is a final formed sample meeting the quality requirement of the target product, so that an ultrasonic signal realized by reference is used as a reference to prepare for subsequent background differential processing.

Further, the ultrasonic detection in the embodiment is laser ultrasonic detection, and laser ultrasonic is a non-contact, high-temperature-resistant and high-precision detection technology, compared with the traditional ultrasonic detection, the laser ultrasonic has the advantages of obviously improving the detection efficiency and the detection precision, and being non-contact and high-temperature-resistant, the method can meet the requirements of online detection of the additive parts and the detection of defects of pipelines and containers in dangerous environments.

The first initial signal and the second initial signal are ultrasonic signals generated by exciting ultrasonic waves on the surface of the sample. Meanwhile, a first initial signal and a second initial signal are obtained through traversing scanning, the first initial signal and the second initial signal are processed and calculated in a subsequent mode, the first initial signal and the second initial signal are calculated in a corresponding mode, the obtained signals are signals corresponding to the points, and the subsequent steps are omitted.

Specifically, step S1 may include:

when the traverse scanning ultrasonic detection is carried out on the defect sample to be detected, single-point multiple excitation is adopted to obtain a plurality of first initial signals point by point, and when the traverse scanning ultrasonic detection is carried out on the reference sample, single-point multiple excitation is adopted to obtain a plurality of second initial signals point by point.

In this embodiment, the first initial signals and the second initial signals can be acquired by performing single-point multiple excitation, so that noise reduction processing can be performed on obvious noise in the signals.

And S2, performing noise reduction pretreatment on the first initial signal to obtain a first noise reduction signal, and performing noise reduction pretreatment on the second initial signal to obtain a second noise reduction signal.

In this embodiment, since the detection signal is affected by factors such as the environment, the generator, and the receiver during the generation and reception process, obvious noise such as high-frequency electrical noise and low-frequency oscillation signal may be generated, so when the first initial signal and the second initial signal are obtained, noise reduction pretreatment needs to be performed on the first initial signal and the second initial signal to remove the obvious noise included in the first initial signal and the second initial signal, and the first noise reduction signal and the second noise reduction signal are correspondingly obtained, so as to improve efficiency and precision of subsequent signal processing.

Referring to fig. 3, in one example, step S2 may be implemented by the following method:

step S211, carrying out average operation on a plurality of first initial signals to obtain first noise reduction signals;

step S212, an average operation is performed on the plurality of second initial signals to obtain a second noise reduction signal.

In this embodiment, when the noise reduction pretreatment is performed on the first initial signal and the second initial signal, a single-point multiple excitation averaging manner may be adopted to weaken the influence of a signal having obvious noise at one time by using the average value of a plurality of first initial signals obtained by multiple excitation of a single point, so that the noise reduction pretreatment on the first initial signal can be implemented. Similarly, a mode of single-point multiple excitation to calculate the average value can be adopted, so that the average value of a plurality of second initial signals obtained through multiple excitation is used for weakening the influence of a signal with obvious noise at one time, and noise reduction pretreatment of the second initial signals can be realized.

It should be noted that, the order of obtaining the first noise reduction signal and the second noise reduction signal may be adjusted by itself, the first noise reduction signal may be obtained first, the second noise reduction signal may be obtained second, and the first noise reduction signal may be obtained first, or the first noise reduction signals may be obtained simultaneously, that is, the order of steps S211 to S212 may be adjusted at will, which is not limited in this embodiment.

Referring to fig. 4, in another example, the above step S2 may be implemented by the following method:

step S221, carrying out average operation on a plurality of first initial signals to obtain first average signals;

step S222, performing primary noise reduction and secondary noise reduction on the first average signal to obtain a first noise reduction signal;

step S223, carrying out average operation on a plurality of second initial signals to obtain second average signals;

and step 224, performing primary noise reduction and secondary noise reduction on the second average signal to obtain a second noise reduction signal.

In this embodiment, after the average operation is performed on the plurality of first initial signals and the plurality of second initial signals, the first average signal and the second average signal may be obtained, and in order to improve the noise reduction effect, the noise reduction process may be further performed on the first average signal and the second average signal again, so as to improve the noise reduction efficiency and the noise reduction effect, so that the ideal first noise reduction signal and second noise reduction signal may be obtained.

Specifically, the above-described primary noise reduction and secondary noise reduction may be noise reduction methods such as wavelet noise reduction, hilbert Huang Jiangzao processing, and deep learning self-encoding noise reduction, and the present embodiment is not limited thereto. The primary noise reduction and the secondary noise reduction can be different, so that the noise reduction effect can be further improved, and the influence of obvious noise on the imaging precision of the subsequent defects is reduced.

It should be noted that, the order of obtaining the first noise reduction signal and the second noise reduction signal may be adjusted by itself, the first noise reduction signal may be obtained first, the second noise reduction signal may be obtained second, and the first noise reduction signal may be obtained first, or the first noise reduction signals may be obtained simultaneously, that is, the order of steps S221 to S224 may be adjusted at will, which is not limited in this embodiment.

And S3, performing signal matching operation on the first noise reduction signal and the second noise reduction signal, and performing background difference subtraction operation to obtain a scattering characteristic signal.

In this embodiment, the background difference method is mainly based on the principle of reciprocity difference, and the basic idea is to measure the disturbance degree of the scattered wave field caused by medium non-uniformity by comparing the difference between the non-uniform medium ultrasonic wave field containing scattering sources such as grains or defects and the uniform reference medium ultrasonic wave field, so as to obtain the amplitude of the scattered signal.

Since the first noise reduction signal is a signal with a defect of a defect sample to be detected, and the second noise reduction signal is a signal with no defect or a negligible defect of the reference sample, in order to determine whether the first noise reduction signal includes defect information, the first noise reduction signal and the second noise reduction signal can be subjected to differential subtraction, that is, background signal features in the first noise reduction signal are subtracted, only defect signal features in the first noise reduction signal are left, so that a scattering feature signal of the defect is obtained, and the detection and imaging operation of the subsequent defect are facilitated. The scattering characteristic signal is a scattering characteristic signal wave emitted from a secondary sound source to various directions by taking a crystal grain or a defect as the secondary sound source due to the change of acoustic impedance between the crystal grain and the crystal grain in the ultrasonic wave propagation process. In this embodiment, the point-by-point scattering characteristic signal of the defect sample to be detected can be obtained by the method, so that subsequent processing is facilitated.

With reference to fig. 5, further, the present embodiment further provides a method capable of implementing step S3, including:

and S31, performing cross-correlation operation on the first noise reduction signal and the second noise reduction signal to obtain a first maximum cross-correlation coefficient and a corresponding first delay time.

In this embodiment, since there may be a certain delay between the first noise reduction signal and the second noise reduction signal, there is a phase difference between the first noise reduction signal and the second noise reduction signal, and if the difference is directly subtracted at this time, the obtained scattering feature signal is inaccurate. Therefore, the first noise reduction signal and the second noise reduction signal need to be subjected to cross-correlation operation to obtain the first maximum cross-correlation coefficient and the corresponding first delay time, so that the phase difference between the first noise reduction signal and the second noise reduction signal can be adjusted subsequently.

Step S32, the second noise reduction signal is matched with the first noise reduction signal according to the first delay time, and a matched signal is obtained.

In this embodiment, after the first delay time is obtained, the second noise reduction signal may be made to match with the first noise reduction signal according to the first delay time, so as to eliminate a phase difference between the first noise reduction signal and the second noise reduction signal, and make the second noise reduction signal align with the first noise reduction signal, so as to obtain a matching signal.

Step S33, judging the magnitude of the first maximum cross-correlation coefficient and a set cross-correlation coefficient threshold, and if the first maximum cross-correlation coefficient is smaller than the set cross-correlation coefficient threshold, performing differential operation on the first noise reduction signal and the matching signal to obtain a scattering characteristic signal; if the first maximum cross-correlation coefficient is greater than the set cross-correlation coefficient threshold, the scattering feature signal is marked as zero.

In this embodiment, when the first maximum cross-correlation coefficient is smaller than the set cross-correlation coefficient threshold, it is indicated that a defect exists at the position of the point, and differential operation needs to be performed on the first noise reduction signal and the matching signal to obtain a scattering characteristic signal, so that imaging of a subsequent defect is facilitated; when the first maximum cross-correlation coefficient is larger than the set cross-correlation coefficient threshold, the difference result of the point can be directly recorded as 0, namely the point is regarded as not having defects, so that subsequent operation is not needed, and the detection efficiency is improved.

Wherein, preferably, the cross-correlation coefficient threshold value can be used for separating a defective area and a non-defective area based on a machine learning clustering algorithm, and the first maximum cross-correlation coefficient in the defective area is defined as the cross-correlation coefficient threshold value.

And S4, performing multi-direction adjacent wave differential subtraction calculation on the scattering characteristic signals to obtain defect characteristic signals.

In this embodiment, the scattering characteristic signal of the micro defect is weak, which causes blurring of the defect imaging edge and affects the imaging resolution. The fundamental principle of the multidirectional adjacent wave differential method is to utilize the difference of ultrasonic scattering characteristic signals at defective and non-defective positions to differential the time domain waveforms of adjacent detection points in different directions, amplify defect scattering abnormal waves, inhibit incident signals and grain scattering signals, and play the purpose of enhancing defect characteristic signals, so that the signal intensity of micro defects can be improved, and clear imaging of the defects is convenient to realize. The defect characteristic signal is obtained by performing multi-direction adjacent wave differential calculation on the ultrasonic scattering characteristic signal.

With reference to fig. 6, further, the present embodiment further provides a method capable of implementing step S4, including:

and S41, acquiring a 0-degree adjacent wave signal of the scattering characteristic signal, matching the 0-degree adjacent wave signal with the scattering characteristic signal to obtain a 0-degree matching signal, and obtaining a 0-degree defect characteristic signal according to the scattering characteristic signal and the 0-degree matching signal.

In this embodiment, in order to compare the time domain waveforms of adjacent points in different directions to enhance the signal strength at the edge of the defect, the adjacent waves in the 0 ° direction may be first selected for comparison. The 0-degree adjacent wave signal and the scattering characteristic signal are matched to obtain a 0-degree matching signal, so that errors caused by phase differences between the two signals are reduced, and the accuracy of the 0-degree defect characteristic signal is improved.

It should be noted that, the above-mentioned 0 ° adjacent wave signal may be a signal of an adjacent point found to the right along the 0 ° direction from the point, or may be a signal of an adjacent point found to the left, or may be a signal of an adjacent wave found to the left along two directions, or may be a signal of an adjacent wave found to the 0 ° along other specified directions, which is not limited in this embodiment.

Referring to fig. 7, for example, the above step S41 may be implemented by the following method, including:

step S411, selecting waveforms of the scattering feature signals adjacent to the scattering feature signals along the 0-degree direction as 0-degree adjacent wave signals, and performing cross-correlation operation on the 0-degree adjacent wave signals and the scattering feature signals to obtain a second maximum cross-correlation coefficient and a corresponding second delay time.

In this embodiment, since there may be a certain delay between the scattering feature signal and the 0 ° adjacent wave signal, there is a phase difference between the scattering feature signal and the 0 ° adjacent wave signal, and if the difference is directly subtracted at this time, the obtained scattering feature signal is inaccurate. Therefore, the cross-correlation operation is performed on the scattering feature signal and the 0 ° adjacent wave signal to obtain the second maximum cross-correlation coefficient and the corresponding second delay time, so as to adjust the phase difference between the scattering feature signal and the 0 ° adjacent wave signal.

Step S412, matching the 0-degree adjacent wave signal with the scattering characteristic signal according to the second delay time to obtain a 0-degree matching signal;

in this embodiment, after the second delay time is obtained, the 0 ° adjacent wave signal may be made to match with the scattering feature signal according to the second delay time, so as to eliminate a phase difference between the scattering feature signal and the 0 ° adjacent wave signal, and make the 0 ° adjacent wave signal align with the first noise reduction signal, so as to obtain a 0 ° matching signal.

And S413, carrying out differential operation on the scattering characteristic signal and the 0-degree matching signal to obtain a 0-degree defect characteristic signal.

In this embodiment, by performing differential operation on the scattering feature signal and the 0 ° matching signal, the difference between the scattering feature signal and the adjacent wave in the 0 ° direction can be amplified, and the 0 ° defect feature signal can be obtained, so that the defect feature of the scattering feature signal in the direction can be highlighted, and the subsequent defect can be clearly imaged.

Step S42, acquiring a 45-degree adjacent wave signal of the scattering characteristic signal, matching the 45-degree adjacent wave signal with the scattering characteristic signal to obtain a 45-degree matching signal, and obtaining a 45-degree defect characteristic signal according to the scattering characteristic signal and the 45-degree matching signal.

In this embodiment, by observing the mesh data, the points adjacent to the scattering feature signal also include the points adjacent to the 45 ° direction thereof, so that the adjacent waves in the 45 ° direction can be selected for comparison. The 45-degree adjacent wave signal and the scattering characteristic signal are matched to obtain a 45-degree matching signal, so that errors caused by phase differences between the two signals are reduced, and the accuracy of the 45-degree defect characteristic signal is improved.

It can be understood that the search direction of the 45 ° adjacent wave signal is the same as the search direction of the 0 ° adjacent wave signal, and will not be described herein. Meanwhile, the specific process of acquiring the 45 ° defect characteristic signal may refer to the process of acquiring the 0 ° defect characteristic signal, which is not described herein.

And S43, acquiring a 90-degree adjacent wave signal of the scattering characteristic signal, matching the 90-degree adjacent wave signal with the scattering characteristic signal to obtain a 90-degree matching signal, and obtaining a 90-degree defect characteristic signal according to the scattering characteristic signal and the 90-degree matching signal.

In the present embodiment, the points adjacent to the scattering feature signal include 90 ° directions in addition to the above-described 0 ° directions and 45 ° directions. Therefore, it is also necessary to select the adjacent waves in the 90 ° direction for comparison. The 90-degree adjacent wave signal and the scattering characteristic signal are matched to obtain a 90-degree matching signal, so that errors caused by phase differences between the two signals are reduced, and the accuracy of the 90-degree defect characteristic signal is improved.

It can be understood that the search direction of the 90 ° adjacent wave signal is the same as the search direction of the 0 ° adjacent wave signal, and will not be described herein. Meanwhile, the specific process of acquiring the 90 ° defect characteristic signal may refer to the process of acquiring the 0 ° defect characteristic signal, which is not described herein.

In this embodiment, after obtaining the defect characteristic signal, in some embodiments, it may be first determined whether the point has a defect according to the defect characteristic signal, and a simple determination may be performed.

The defect characteristic signals include a 0-degree defect characteristic signal, a 45-degree defect characteristic signal and a 90-degree defect characteristic signal, so that when judging whether defects exist, the defect characteristic signals in three different directions need to be comprehensively judged, meanwhile, the defect characteristic signals generated by different defect characteristics are different, particularly, the defect characteristic signals in different directions are obviously different, and therefore the main form of the defects can be further represented through the defect characteristic signals in all directions, and the defect types are obtained. Specifically, in order to improve the judging efficiency of defect characteristics, after obtaining a 0-degree defect characteristic signal, a 45-degree defect characteristic signal and a 90-degree defect characteristic signal, the defect characteristic signals in three directions can be summed up to obtain a total defect characteristic signal, when judging whether the point has defects, the judging can be firstly carried out according to the total defect characteristic signal obtained by summation, if the total defect characteristic signal does not have defects, the point is indicated to have no defects; if the total defect characteristic signal has defects, the defect exists at the point, and further judging the defect characteristic signals in all directions, so that the direction and the defect type of the defects are obtained. In this embodiment, the total defect characteristic signal is determined first, and then the defect characteristic signals in all directions are determined, so that the defect determination efficiency can be effectively improved. Thereby further improving defect detection efficiency.

And S5, drawing an image according to the defect characteristic signals.

In this embodiment, when the defect sample to be tested has a defect, the defect image is drawn, and if the defect sample does not have a defect, drawing is not needed.

Referring to fig. 8, for example, the present embodiment further provides a method for implementing step S5, including:

s51, drawing a two-dimensional tomographic image according to the defect characteristic signals;

in the present embodiment, when an image is drawn, a two-dimensional tomographic image may be drawn first, so that an image of a defect on each layer can be obtained, facilitating the subsequent acquisition of a three-dimensional image. The two-dimensional tomographic image is an image formed by arranging a plurality of layers of two-dimensional images in order according to the positional information of each layer of images.

Referring to fig. 9, specifically, the step S51 includes:

step S511, setting a chromatographic interval and a chromatographic depth.

In this embodiment, when a two-dimensional tomographic image is drawn, first, a tomographic interval and a tomographic depth are set according to a desired resolution to obtain a two-dimensional image of a corresponding layer number.

And S512, acquiring a two-dimensional tomographic image by using the defect characteristic signal based on a time-varying window energy mapping method according to the tomographic interval and the tomographic depth.

In this embodiment, the variable time window energy mapping method is to calculate a time window starting time according to a chromatographic depth, determine a time window length according to a chromatographic interval, sum squares of defect characteristic signal amplitudes in a detection point time window to obtain defect energy values at corresponding depths of the detection points, obtain defect energy values of all detection points in the same depth, and draw a two-dimensional image of the corresponding depth defects according to a mapping relation between the defect energy values of the detection points and positions of the detection points. And arranging the two-dimensional images of the defects of each layer according to the depth positions of the two-dimensional images, so as to obtain two-dimensional tomographic images of the defects.

And S52, performing three-dimensional reconstruction according to the two-dimensional tomographic image to obtain a three-dimensional image.

In this embodiment, after the two-dimensional tomographic image is obtained, three-dimensional reconstruction may be performed from the two-dimensional tomographic image, thereby obtaining a three-dimensional image. Wherein the three-dimensional image is an image of a defect having a three-dimensional spatial structure.

Referring to fig. 10, specifically, the step S52 includes:

step S521, binarizing the two-dimensional tomographic image.

In the present embodiment, since the obtained two-dimensional tomographic image is a color image, it includes a plurality of layers of two-dimensional color images. In order to improve the efficiency of the subsequent three-dimensional reconstruction, the color image may be subjected to binarization processing, so that each layer of two-dimensional image is converted from color into only two colors of black and white.

For example, since the edges of the defect and the edges of the non-defective positions of the color two-dimensional image are clear, the color value of the pixel point at the edge of the color image can be selected as a demarcation threshold value, the defective part is changed to white, and the non-defective part is changed to black; or the defect part is changed into black, the defect-free part is changed into white, and at the moment, the point on the two-dimensional image is not black, namely white, and no gray value is included; the two-dimensional image subjected to binarization processing only comprises two colors of black and white, so that the data processing pressure of subsequent three-dimensional reconstruction is reduced, and the imaging efficiency is improved.

Step 522, mapping the binarized pixel points in each layer of image into a three-dimensional space based on a spatial relationship according to the position information of each layer of image, so as to form discrete three-dimensional pixel points.

In this embodiment, after the two-dimensional image is binarized, the two-dimensional image is mapped into a three-dimensional space based on a spatial relationship according to the position information of each point of each layer of image and the color information of the pixel point after the binarization processing, so as to form a discrete three-dimensional body pixel point.

It will be appreciated that the points in the two-dimensional image of each layer contain position coordinate information of the plane in which they lie, together with depth information of the layer in which they lie, forming three-dimensional position information of the respective points. In addition, after binarization processing, each point also contains black or white pixel information, and the information is mapped in the three-dimensional space together, so that a discrete three-dimensional body with only black and white in the three-dimensional space can be formed.

Step S523, obtaining a three-dimensional image according to the discrete three-dimensional pixel points.

In this embodiment, after the discrete three-dimensional volume pixels are formed, they can be converted into a continuous three-dimensional image.

And S6, judging whether the defect sample to be detected has defects according to the image, and evaluating the defect characteristics.

In this embodiment, after the image is obtained, whether the defect exists in the to-be-detected defect sample can be determined according to the scanned image, and after the defect is confirmed, the position, the size and other characteristics of the defect need to be evaluated.

Further, the step S6 includes:

and judging whether a defect exists or not through an image recognition technology or comparing pixel values of the images, and confirming the position and the size of the defect.

In this embodiment, whether the image has a defect or not may be obtained by the result of identifying the image by the image identification technology, and defect characteristics such as the position and size of the defect may be obtained. Or the pixel value of the contrast image is firstly read, the pixel values of all points of the image are compared, whether the image has defects or not is obtained according to the comparison result, and defect characteristics such as the position, the size and the like of the defects are obtained.

The above-mentioned process of recognizing the image or the process of reading the pixel value of the image may be implemented by a computer, so as to further improve the detection efficiency and detection accuracy of the defect. It will be appreciated that the above-described process may also be implemented manually, and is not limited in this embodiment.

Due to the reasons of strong scattering of the microstructure of the material, environmental noise and the like, a strong noise background is formed in the defect detection process, and the signal to noise ratio of a laser ultrasonic detection signal is often low, so that the defect edge imaging is fuzzy, and the accuracy of online real-time detection is affected. Meanwhile, the existing laser ultrasonic online detection needs to adopt point-by-point scanning, and the detection efficiency is seriously affected. The present embodiment provides a method and a system for imaging a defect in a strong noise background, where the strong noise background includes a case of a strong noise signal due to a characteristic of a material itself and a case of an environment with strong noise due to an environmental characteristic, and may include only one or both of the above cases.

Scanning ultrasonic detection is carried out on a defect sample to be detected to obtain a first initial signal point by point, and traversing scanning ultrasonic detection is carried out on a reference sample to obtain a second initial signal point by point; carrying out noise reduction pretreatment on the first initial signal to obtain a first noise reduction signal, and carrying out noise reduction pretreatment on the second initial signal to obtain a second noise reduction signal; performing signal matching operation on the first noise reduction signal and the second noise reduction signal, and performing background difference subtraction operation to obtain a scattering characteristic signal; carrying out multi-direction adjacent wave differential subtraction calculation on the scattering characteristic signals to obtain defect characteristic signals; drawing an image according to the defect characteristic signals; judging whether the defect sample to be detected has defects according to the images, and evaluating the defect characteristics.

The background difference and the multidirectional adjacent wave difference method are fused, so that the strong noise background of the microstructure of the material is restrained through the background difference. The method has the advantages that the medium internal full wave field is not needed to be considered, only the differential is carried out on the non-uniform medium and the reference medium detection surface scattering wave field, the Berne approximation and the single scattering source assumption are not introduced, the noise reduction treatment on the material tissue structure noise is realized, and meanwhile, the rapid extraction on the defect scattering characteristic signals is realized. On the other hand, the boundary detail characteristics of the micro defects are enhanced through the multidirectional adjacent wave difference, and the sensitivity to the directions of the micro defects such as cracks is improved, so that the problem of low defect imaging precision is effectively avoided while the online detection efficiency is improved, and the defect imaging quality is further improved.

In other words, the method not only maintains the advantage of high speed and high efficiency of a background differential method defect scattering characteristic signal extraction algorithm, but also further combines a multi-direction adjacent wave differential method to perform differential calculation after matching detection waveform signals of two adjacent points in different directions, and fully plays the advantages that the method can realize defect boundary detail characteristic enhancement and is sensitive to crack defect directions. The method has the advantages that the method can effectively avoid low defect characterization precision caused by improper background selection, reduces crack defect detection shadow areas, does not need to collect full matrix scanning data, does not need to carry out spectrum analysis on the data, can realize real-time storage, time domain analysis and real-time imaging of detection data, and greatly reduces the data storage capacity of large-scale components for detection, thereby remarkably improving the detection efficiency. Finally, high-precision photoacoustic tomography and three-dimensional reconstruction of the micro-defect characteristics inside the metal material are realized.

Referring to fig. 11, in a second aspect of the present embodiment, a defect ultrasonic imaging system 200 under a strong noise background is further provided, where the system includes a scanning ultrasonic detection module 201 and a signal processing module 202, the scanning ultrasonic detection module 201 is in communication connection with the signal processing module 202, the scanning ultrasonic detection module 201 is configured to perform scanning ultrasonic detection on a defect sample to be detected to obtain a first initial signal with a point by point, perform traversing scanning ultrasonic detection on a reference sample to obtain a second initial signal with a point by point, and the signal processing module 202 is configured to perform noise reduction pretreatment on the first initial signal to obtain a first noise reduction signal, and perform noise reduction pretreatment on the second initial signal to obtain a second noise reduction signal; performing signal matching operation on the first noise reduction signal and the second noise reduction signal, and performing background difference subtraction operation to obtain a scattering characteristic signal; carrying out multi-direction adjacent wave differential subtraction calculation on the scattering characteristic signals to obtain defect characteristic signals; drawing an image according to the defect characteristic signals; judging whether the defect sample to be detected has defects according to the images, and evaluating the defect characteristics.

According to the system, the background difference and the multidirectional adjacent wave difference method are fused, on one hand, the background difference is used for inhibiting the strong noise background of the microstructure of the material, the noise reduction treatment of the noise of the tissue structure of the material is realized, and meanwhile, the rapid extraction of the defect scattering characteristic signals is realized. On the other hand, the boundary detail characteristics of the micro defects are enhanced through the multidirectional adjacent wave difference, and the sensitivity to the directions of the micro defects such as cracks is improved, so that the problem of low defect imaging precision is effectively avoided while the online detection efficiency is improved, and the defect imaging quality is further improved.

Finally, it should be noted that: the above embodiments are only for illustrating the technical solution of the present invention, and not for limiting the same; although the invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical scheme described in the foregoing embodiments can be modified or some or all of the technical features thereof can be replaced by equivalents; such modifications and substitutions do not depart from the spirit of the invention.

Claims (10)

1. The defect ultrasonic imaging method under the strong noise background is characterized by comprising the following steps of:

performing traversal ultrasonic detection on a defect sample to be detected to obtain a first initial signal point by point, and performing traversal ultrasonic detection on a reference sample to obtain a second initial signal point by point;

performing noise reduction pretreatment on the first initial signal to obtain a first noise reduction signal, and performing noise reduction pretreatment on the second initial signal to obtain a second noise reduction signal;

performing signal matching operation on the first noise reduction signal and the second noise reduction signal, and performing background difference subtraction operation to obtain a scattering characteristic signal;

Carrying out multidirectional adjacent wave differential subtraction calculation on the scattering characteristic signals to obtain defect characteristic signals;

drawing an image according to the defect characteristic signals;

judging whether the defect sample to be detected has defects according to the image, and evaluating defect characteristics.

2. The method for ultrasonic imaging of defects in a noisy background according to claim 1, wherein performing a scanning ultrasonic test on the defect sample to be detected to obtain a first initial signal, and performing a scanning ultrasonic test on the reference sample to obtain a second initial signal comprises:

and when the traverse scanning ultrasonic detection is carried out on the to-be-detected defect sample, single-point multiple excitation is adopted to obtain a plurality of first initial signals, and when the traverse scanning ultrasonic detection is carried out on the reference sample, single-point multiple excitation is adopted to obtain a plurality of second initial signals.

3. The method of claim 2, wherein the performing noise reduction preprocessing on the first initial signal to obtain a first noise reduction signal, and performing noise reduction preprocessing on the second initial signal to obtain a second noise reduction signal comprises:

performing average operation on a plurality of first initial signals to obtain first noise reduction signals;

And carrying out average operation on the plurality of second initial signals to obtain the second noise reduction signals.

4. The method of claim 2, wherein the performing noise reduction preprocessing on the first initial signal to obtain a first noise reduction signal, and performing noise reduction preprocessing on the second initial signal to obtain a second noise reduction signal comprises:

carrying out average operation on a plurality of first initial signals to obtain first average signals;

performing primary noise reduction and secondary noise reduction on the first average signal to obtain a first noise reduction signal;

carrying out average operation on a plurality of second initial signals to obtain second average signals;

and performing primary noise reduction and secondary noise reduction on the second average signal to obtain the second noise reduction signal.

5. The method of claim 1, wherein performing a signal matching operation on the first noise reduction signal and the second noise reduction signal, and performing a background difference subtraction operation to obtain a scattering feature signal comprises:

performing cross-correlation operation on the first noise reduction signal and the second noise reduction signal to obtain a first maximum cross-correlation coefficient and a corresponding first delay time;

Matching the second noise reduction signal with the first noise reduction signal according to the first delay time to obtain a matching signal; judging the magnitudes of the first maximum cross-correlation coefficient and a set cross-correlation coefficient threshold, and if the first maximum cross-correlation coefficient is smaller than the set cross-correlation coefficient threshold, performing differential operation on the first noise reduction signal and the matching signal to obtain the scattering characteristic signal; and if the first maximum cross-correlation coefficient is larger than the set cross-correlation coefficient threshold value, marking the scattering characteristic signal as zero.

6. The method for ultrasonic imaging of defects in a strong noise background according to claim 1, wherein the step of performing a multi-directional adjacent wave differential subtraction on the scattered feature signal to obtain the defect feature signal comprises:

acquiring a 0-degree adjacent wave signal of the scattering characteristic signal, matching the 0-degree adjacent wave signal with the scattering characteristic signal to obtain a 0-degree matching signal, and obtaining a 0-degree defect characteristic signal according to the scattering characteristic signal and the 0-degree matching signal;

acquiring a 45-degree adjacent wave signal of the scattering characteristic signal, matching the 45-degree adjacent wave signal with the scattering characteristic signal to obtain a 45-degree matching signal, and obtaining a 45-degree defect characteristic signal according to the scattering characteristic signal and the 45-degree matching signal;

And acquiring a 90-degree adjacent wave signal of the scattering characteristic signal, matching the 90-degree adjacent wave signal with the scattering characteristic signal to obtain a 90-degree matching signal, and obtaining a 90-degree defect characteristic signal according to the scattering characteristic signal and the 90-degree matching signal.

7. The method of claim 6, wherein the obtaining the 0 ° ortho-wave signal of the scattering feature signal, and the matching the 0 ° ortho-wave signal with the scattering feature signal to obtain a 0 ° matching signal, and the obtaining the 0 ° defect feature signal according to the scattering feature signal and the 0 ° matching signal comprises:

selecting waveforms of the scattering characteristic signals adjacent to the scattering characteristic signals along the 0-degree direction as 0-degree adjacent wave signals, and performing cross-correlation operation on the 0-degree adjacent wave signals and the scattering characteristic signals to obtain a second maximum cross-correlation coefficient and corresponding second delay time;

matching the 0-degree adjacent wave signal with the scattering characteristic signal according to the second delay time to obtain the 0-degree matching signal;

and carrying out differential operation on the scattering characteristic signal and the 0-degree matching signal to obtain the 0-degree defect characteristic signal.

8. The method of ultrasound imaging of defects in a noisy background according to any of claims 1 to 7, wherein said rendering an image from said defect signature comprises:

Setting a chromatographic interval and a chromatographic depth;

according to the chromatographic interval and the chromatographic depth, the defect characteristic signals are obtained into a two-dimensional chromatographic image based on a time-varying window energy mapping method;

performing binarization processing on the two-dimensional tomographic image;

according to the position information of each layer of image, mapping the pixel points in each layer of image after binarization processing into a three-dimensional space based on a spatial relationship to form discrete three-dimensional pixel points;

and obtaining a three-dimensional image according to the discrete three-dimensional pixel points.

9. The method for ultrasonic imaging of defects in a noisy background according to any one of claims 1 to 7, wherein said determining whether the defect sample to be tested has defects according to the image and performing defect feature evaluation comprises:

and judging whether a defect exists or not through an image recognition technology or comparing pixel values of the images, and confirming the position and the size of the defect.

10. A defect ultrasound imaging system in a noisy background, comprising:

the scanning ultrasonic detection module is used for performing traversal ultrasonic detection on the defect sample to be detected to obtain a first initial signal point by point, and performing traversal ultrasonic detection on the reference sample to obtain a second initial signal point by point;

The signal processing module is used for carrying out noise reduction pretreatment on the first initial signal to obtain a first noise reduction signal, and carrying out noise reduction pretreatment on the second initial signal to obtain a second noise reduction signal; performing signal matching operation on the first noise reduction signal and the second noise reduction signal, and performing background difference subtraction operation to obtain a scattering characteristic signal; carrying out multidirectional adjacent wave differential subtraction calculation on the scattering characteristic signals to obtain defect characteristic signals; drawing an image according to the defect characteristic signals; judging whether the defect sample to be detected has defects according to the image, and evaluating defect characteristics.