Low-frequency ultrasonic imaging method and device, computer equipment and storage medium

Abstract

The invention relates to the technical field of concrete detection, and discloses a low-frequency ultrasonic imaging method and device, computer equipment and a storage medium. Ultrasonic pulse echo signals are collected through an array type transducer; carrying out filtering processing on the collected ultrasonic pulse echo signal; identifying the arrival time of a direct wave based on a crest difference mutation criterion, inverting the propagation velocity of a transverse wave, and generating a single-region scanning image in combination with a synthetic aperture focusing technology; and finally, performing normalization, binaryzation and communicated region decomposition and matching on a plurality of scanning images to realize automatic splicing and fusion of cross-measurement-region images so as to form a two-dimensional ultrasonic imaging graph covering a long-distance structure. The method can effectively inhibit noise interference, improve wave velocity inversion and defect identification precision, realize continuous and complete imaging of a long-distance concrete structure, and is suitable for internal defect detection of subsea tunnels and other projects.

Description

Technical Field

[0001] This invention relates to the field of concrete testing technology, specifically to a low-frequency ultrasonic imaging method, apparatus, computer equipment, and storage medium. Background Technology

[0002] In major concrete projects such as undersea tunnels and integrated utility tunnels, the quality of secondary lining pouring directly affects the long-term safety and durability of the structure. If hidden defects such as non-compactness, voids, cracks, or misaligned reinforcement are not detected in time during the construction phase, these hidden dangers may further expand under the influence of water pressure, loads, and chemical corrosion during the operation period, even leading to serious consequences and causing huge safety risks and economic losses. The detection of internal defects in concrete structures mainly relies on ultrasonic technology; however, in complex applications such as tunnel lining, traditional ultrasonic testing faces several practical difficulties: First, in the signal preprocessing stage, commonly used filtering methods are prone to phase distortion or fail to effectively extract weak defect signal features in strong noise, affecting the accuracy of the analysis. Second, the automatic identification of direct wave arrival time mostly adopts fixed thresholds or simple envelope judgment, which has poor adaptability in the complex signal environment of concrete and is easily interfered with, resulting in inaccurate wave velocity inversion. Wave velocity deviation will directly cause the synthetic aperture focusing imaging to "go out of focus" and reduce resolution. Finally, the imaging results are usually limited to a small area, and there is a lack of efficient and reliable means to stitch together B-scan images (two-dimensional profiles) of multiple independent areas into a continuous overall profile, which is difficult to meet the actual needs of continuous detection of long-distance tunnel lining.

[0003] Therefore, there is an urgent need for a complete ultrasonic testing method that can adapt to harsh field conditions and extend from high-precision signal processing to continuous panoramic imaging, thereby improving the detection capability and reliability of hidden defects inside concrete structures. Summary of the Invention

[0004] In view of this, the present invention provides a low-frequency ultrasonic imaging method, apparatus, computer equipment and storage medium to solve the problems of poor signal quality, low imaging resolution caused by inaccurate direct wave identification and wave velocity inversion in existing concrete ultrasonic testing methods under strong noise and complex media environments, and difficulty in achieving continuous seamless imaging over long test areas.

[0005] In a first aspect, the present invention provides a low-frequency ultrasound imaging method, comprising: An array transducer is used to acquire ultrasonic pulse echo signals from multiple measurement locations. The signal acquisition adopts a sequential array element transmission and subsequent array element reception mode: when the i-th array element transmits an ultrasonic pulse, the (i+1)-8th array elements thereafter act as receiving array elements to synchronously acquire the echo signal; the control unit amplifies and converts the signal from analog to digital, and then stores it as a time-domain signal data matrix.

[0006] The acquired ultrasonic pulse echo signal is filtered using a bidirectional zero-phase filtering technique: the original signal is first filtered in the forward direction, the result is time-reversed and then filtered a second time, and the second filtering result is time-reversed again to obtain a filtered signal without phase distortion, which effectively suppresses out-of-band noise and preserves the main frequency energy.

[0007] Identifying the arrival time of direct waves in filtered signals based on peak difference abrupt change criteria includes: Peak extraction: Model the filtered signal, extract peaks through local maximum detection, and record the amplitude and corresponding time to form a peak sequence; Peak detection can be formalized as a set of local maxima: ; in This represents the amplitude of the k-th peak. The corresponding time.

[0008] Differential sequence construction: Calculate the amplitude difference between adjacent peaks to form an amplitude difference sequence that reflects the rate of energy change; For peak sequences Construct adjacent difference sequences:

[0009]

[0010] Arrival time determination: When the total number of peaks is ≥2, the time of the peak with the larger amplitude corresponding to the peak with the largest amplitude difference is taken; when there is only 1 peak, the time of that peak is taken directly; if there is no peak, the signal is marked as invalid.

[0011] The transverse wave propagation velocity is inverted based on the direct wave arrival time at all measurement locations, including: Data preprocessing: Collect the coordinates of the measuring points and the arrival time of the direct wave at the valid measurement locations, and remove invalid data and constant values ​​with a standard deviation of 3 times;

[0012] in This indicates the location of the measuring point.

[0013] Model building: Constructing the mapping relationship between arrival time and spatial location; Assuming the sound wave travels at a speed of For approximately linear propagation, the following conditions are met:

[0014] in For launch delay, This is the residual term.

[0015] Fitting calculation: The model is linearly fitted using the least squares method, and the reciprocal of the slope of the fitted line is taken as the transverse wave propagation speed.

[0016]

[0017] in, This is the average value for all measurement points. This represents the average value at arrival times.

[0018] Based on the arrival time of the direct wave, the time interval in which the main energy of the direct wave is located is determined; the signal amplitude within this interval is set to zero, and the amplitude of the remaining signal is calibrated to obtain the reflected signal containing reflection information such as defects and reinforcing bars.

[0019] Based on the shear wave propagation velocity, synthetic aperture focusing technology is used to image the reflected signal, generating a scanned image, including: Grid division: Set the imaging range, and divide the horizontal and depth directions into two-dimensional grid points according to the preset resolution; Delay time calculation: For each grid point, calculate the propagation distance of the transmit-receive array element pair to that point, and combine it with the shear wave velocity to obtain the signal delay time; Signal superposition: Extract the amplitude of the reflected signal of each array element at the corresponding delay time, and superimpose them according to the weighting factor (based on the propagation distance and array element sensitivity); Image generation: The superimposed amplitude is mapped to pixel values ​​to form a two-dimensional scanned image, and the pixel intensity reflects the distribution of the internal reflective interface.

[0020] By stitching together the scanned images generated from multiple measurement areas, a two-dimensional ultrasound image is obtained, including: Normalization: Eliminates differences in brightness and contrast between different areas by standardizing to the maximum value; Binarization: Otsu's method adaptive threshold segmentation algorithm is used to perform adaptive threshold segmentation, extracting regions with reflectance intensity higher than the threshold to obtain a binarized image; Connected component analysis: Perform connected component decomposition on the binarized image and calculate the centroid coordinates and area of ​​each connected component; Connected region matching: Calculate the ratio of depth difference to area difference of corresponding connected regions within overlapping regions of adjacent images. If the ratio meets the threshold, the region is determined to be a matching region. To quantitatively determine whether corresponding regions in two images can be considered the same physical target (such as the same steel bar reflector), two similarity indices are introduced: depth difference and area difference ratio.

[0021] The depth difference between the points of greatest energy in two images is defined as: Area difference ratio: Defined as the proportion of the area difference between two regions to the total area. When both the distance d and the area error err are sufficiently small (i.e., meeting the set threshold conditions), image A and image B are considered to have matching connected regions. During image stitching, the corresponding positions of these two connected regions can be aligned and stitched together.

[0022] Fusion optimization: Align the image based on the centroid of the matching region, and fuse the pixels in the overlapping region by taking the larger value. Non-overlapping areas retain their original information, and after edge smoothing, continuous two-dimensional ultrasound images are output.

[0023] In a second aspect, the present invention provides a low-frequency ultrasound imaging device, comprising: The signal acquisition module is used to acquire ultrasonic pulse echo signals; A signal processing module is used to filter the signal; The direct wave processing module is used to identify the arrival time of the direct wave and remove the direct wave component from the filtered signal; The parameter inversion module is used to invert the shear wave propagation velocity based on the arrival time of the direct wave and its corresponding measurement location. The imaging module is used to generate a scanned image from the reflected signal after removing the direct wave; The stitching module is used to stitch together the scanned images to obtain a two-dimensional ultrasound image.

[0024] Thirdly, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the computer instructions to perform a low-frequency ultrasound imaging method according to the first aspect or any corresponding embodiment described above.

[0025] Fourthly, the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to perform a low-frequency ultrasound imaging method according to the first aspect or any corresponding embodiment described above.

[0026] Fifthly, the present invention provides a computer program product, including computer instructions for causing a computer to execute a low-frequency ultrasound imaging method according to the first aspect or any corresponding embodiment described above.

[0027] This invention employs an identification method based on abrupt peak difference criteria, overcoming the subjectivity and instability of traditional methods that rely on manual experience to set thresholds. It achieves adaptive and high-precision extraction of the arrival time of direct waves. By utilizing the direct wave time data automatically acquired from various measurement locations to invert the transverse wave propagation velocity, it effectively improves the accuracy and consistency of medium acoustic parameter estimation, thereby ensuring the positioning accuracy and resolution of synthetic aperture focusing imaging. Through normalization, binarization, connected component decomposition, and matching of high-resolution scanned images of a single region, it achieves two-dimensional image fusion across multiple measurement regions, overcoming the limitations of traditional point-based or localized detection in covering long-distance structures. This enables continuous, panoramic internal visualization of linear engineering projects such as tunnels and utility tunnels. This invention can rapidly complete large-area scanning and imaging without damaging the structure, clearly presenting the spatial morphology and location of internal steel reinforcement distribution and defects such as voids, cracks, and non-compacted areas. It provides an efficient and reliable technical means for construction quality control and structural safety assessment during the operational phase. Attached Figure Description

[0028] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the specific embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0029] Figure 1 This is a flowchart illustrating a low-frequency ultrasound imaging method according to an embodiment of the present invention; Figure 2 This is a schematic diagram of a low-frequency ultrasound imaging device according to an embodiment of the present invention; Figure 3 This is a flowchart of direct wave identification and wave velocity inversion for a low-frequency ultrasound imaging method according to an embodiment of the present invention; Figure 4 This is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. Detailed Implementation

[0030] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0031] During the construction phase of an undersea tunnel, the concrete secondary lining, as a critical load-bearing and seepage-proof structure, is prone to potential defects such as incomplete compaction, rebar misalignment, interface voids, and early cracks due to improper construction techniques and material ratio deviations. If these defects are not detected in time, they will continue to develop under the influence of water pressure, seawater erosion, and traffic loads during long-term service, leading to a decrease in structural load-bearing capacity, an increased risk of leakage, and even safety accidents. Therefore, high-precision and continuous testing of the internal structure of the secondary lining concrete is crucial during the construction phase.

[0032] Among existing non-destructive testing technologies for concrete, ground-penetrating radar (GPR) is easily affected by the electromagnetic shielding of reinforcing steel bars, limiting its detection accuracy; while X-ray CT technology offers high resolution, its equipment is bulky and expensive, making it unsuitable for on-site testing of large tunnel structures. Ultrasonic testing technology, due to its portability, moderate cost, and ability to reflect the mechanical properties of materials, has become the mainstream choice for concrete testing, with the synthetic aperture focusing (SAFT) technique combined with pulse-echo method being widely used.

[0033] However, existing ultrasonic testing technologies still have many shortcomings: First, the heterogeneity and anisotropy of concrete materials cause severe attenuation and scattering of ultrasonic signals during propagation. The original signal is easily interfered with by low-frequency structural vibrations and high-frequency electronic noise, and traditional filtering methods are difficult to achieve efficient noise reduction while retaining the effective signal. Second, the identification of the arrival time of direct waves depends on manually setting thresholds or empirical parameters, which is highly subjective and has low accuracy, directly affecting the accuracy of the inversion of shear wave propagation speed, and thus leading to imaging deviations. Third, signal acquisition often uses single transducer or traditional array modes, which has low acquisition efficiency and makes it difficult to effectively stitch together B-scan images when detecting across test areas, making it impossible to form continuous and complete two-dimensional imaging results, affecting the comprehensive detection of long-distance tunnel structures. Fourth, traditional SAFT imaging is easily interfered with by direct wave components, producing imaging artifacts and reducing the accuracy of defect identification.

[0034] In summary, existing ultrasonic imaging technologies for concrete structure inspection suffer from problems such as poor noise reduction, low accuracy of direct wave identification, inaccurate wave velocity inversion, and difficulty in continuous imaging across test areas, making it difficult to meet the needs of high-precision inspection scenarios such as the secondary lining of submarine tunnels.

[0035] This invention provides a low-frequency ultrasonic imaging method that combines elliptical bandpass filtering and an adaptive direct wave identification algorithm based on abrupt peak difference criterion to achieve concrete shear wave velocity inversion and high signal-to-noise ratio data acquisition. Simultaneously, through binarized connected component decomposition and matching methods, it enables continuous B-scan image stitching across different survey areas. This method can accurately and continuously reveal the distribution of reinforcing steel and potential defects within the concrete secondary lining during the construction phase, and its practicality and reliability have been verified in engineering applications.

[0036] According to an embodiment of the present invention, a low-frequency ultrasound imaging method embodiment is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here. Example

[0037] This embodiment provides a low-frequency ultrasonic imaging method applied to the secondary lining inspection section of a submarine tunnel. The inspection object is the concrete lining structure and its internal steel reinforcement distribution. The inspection area is divided into three adjacent regions, denoted as regions A, B, and C.

[0038] During the first measurement, the antenna array system covered areas A and B, obtaining the corresponding B-scan image; during the second measurement, the array system moved along the tunnel direction, covering areas B and C, obtaining the second B-scan image.

[0039] To ensure the continuity of spatial data and the feasibility of subsequent stitching, an overlap region of approximately 50% (i.e., region B) was designed between two adjacent measurements. By comparing and matching the common connected regions within this overlap region, spatial alignment of the images can be achieved, ultimately generating a continuous and complete two-dimensional ultrasound image.

[0040] Step S201: Use an array transducer to acquire ultrasonic pulse echo signals at multiple measurement locations within a single measurement area.

[0041] The array system consists of N =Composed of 8 transducer elements. Signal acquisition is performed by transmitting element by element and receiving element by element. When the first i When each array element transmits a signal ( i = 1, 2, …, 7), and the subsequent array elements (i.e., the 1st, 2nd, …, 7th) i +1, i +2, …, 8 array elements) are sequentially used as receiving array elements to collect echo signals. Therefore, in the first… i The number of received signals that can be obtained in this transmission is The total number of data channels for a single complete measurement by the system is the sum of all transmit-receive combinations, i.e.:

[0042] The eight transducer elements of the array system are labeled as follows: , No. n The positions of each array element are denoted as ( x n , z 0), here we set the array in z = z On the 0 plane. The point to be imaged (target point) is denoted as...P ( x , z ),in x It is a horizontal coordinate. z The depth is defined as positive from the array plane downwards. The speed of sound propagation in the medium is denoted as a constant. (m / s). The... n The time-domain signal received by each array element is S n ( t The sampling interval is Δ. t sampling vector The number of sampling points is K Then from the first... n From individual elements to imaging point P ( x , z The one-way geometric distance of ) can be expressed as:

[0043] The propagation speed of ultrasonic transverse waves within the tested concrete is: (m / s), assuming T If 1 is transmitted, then the echo delay time received by each reflector is... t n Represented as:

[0044] For spatial points P ( x , z By delaying the signals collected by different sensors and superimposing them to highlight abnormal reflections, the depth and shape of defects can be reconstructed. This is the basic principle of SAFT.

[0045] in, Weights (used for attenuation compensation, window functions, gain correction, etc.); Indicates the first n The echo signal received by each array element.

[0046] Step S202: Filter the acquired ultrasonic pulse echo signal.

[0047] In filter design, a narrow transition band, strong stopband attenuation, and controllable passband ripple are typically required. Elliptic filters, with their ability to achieve the narrowest transition band with the lowest order while meeting specified passband ripple and stopband attenuation requirements, are more suitable for bandpass filtering of ultrasonic signals than Butterworth and Chebyshev filters. By combining an elliptic bandpass filter with a bidirectional zero-phase filter, not only can efficient bandpass denoising be achieved, but the influence of phase distortion on signal timing information can also be avoided, thus ensuring the accurate preservation of the time and frequency domain characteristics of the ultrasonic signal.

[0048] The amplitude-frequency response of an ideal elliptic filter can be expressed as:

[0049] in: The passband ripple factor; for n Rational functions of an elliptic of order; Normalized frequency; This is the elliptic modulus of the filter, and its value depends on the ratio of the passband to the stopband boundary frequency. This is the center frequency of the passband.

[0050] Elliptic functions Defined as:

[0051] in It is a complete elliptic integral of the first kind. It is a Jacobian elliptic function.

[0052] Step S203: Identify the arrival time of the direct wave in the filtered ultrasonic pulse echo signal based on the peak difference abrupt change criterion.

[0053] An adaptive direct wave arrival time identification algorithm based on a peak difference abrupt change criterion is adopted. The algorithm first performs peak detection on the filtered signal at each measurement point, extracting the amplitude and position of each peak. Then, it calculates the amplitude difference between adjacent peaks and selects the pair of peaks with the most significant increase in difference as the criterion point for energy transition. This position physically corresponds to the first occurrence of the sound wave energy increasing from weak to strong, i.e., the arrival time of the main wave train. By automatically selecting the abrupt peak, adaptive identification of the direct wave arrival time can be achieved without manually setting thresholds or empirical windows.

[0054] After obtaining the arrival times of all measuring points, the surface wave propagation velocity can be obtained by linearly fitting the relationship between "arrival time and spatial location," and the reciprocal of the slope is the wave velocity value. Compared with traditional envelope detection or threshold discrimination methods, this method does not require prior assumptions, has strong noise resistance, fast calculation speed, and high adaptability. It is especially suitable for real-time acoustic velocity inversion and automated monitoring in field environments with high temperature, high humidity, or significant noise interference.

[0055] High signal-to-noise ratio ultrasound signal after filtering (No. i To achieve high-precision automatic identification of the arrival time of direct waves at multiple measurement points, an adaptive arrival identification algorithm based on the peak difference abrupt change criterion is proposed. The core idea of ​​the algorithm is to utilize the abrupt change characteristics of the local energy of the signal and determine the moment of the first significant jump in sound wave energy through amplitude difference analysis of the peak sequence.

[0056] The filtered high signal-to-noise ratio ultrasonic signal matrix:

[0057] in, , n Indicates the sensor channel number (or measurement point number). For the first m For each discrete sampling time, an adaptive direct wave arrival recognition algorithm based on the peak difference abrupt change criterion is proposed to automatically extract the first arrival time of the sound wave.

[0058] S2031 Signal Modeling and Peak Extraction For each channel, define its filtered one-dimensional signal:

[0059] Peak detection can be formalized as a set of local maxima:

[0060] in Indicates the first k The amplitude of each peak, The corresponding time.

[0061] S2032 Construction of adjacent peak difference sequence For peak sequences Construct adjacent difference sequences:

[0062] Its physical meaning is the rate of change of local wave crest energy, representing the energy transition trend of the wave train. The arrival point of the sound wave usually shows a significant positive transition in this difference sequence.

[0063] S2033 Criteria for Mutation Points and Determination of Arrival Time Define the extremum index of the difference sequence:

[0064] The index The pair of peaks that correspond to the most obvious energy change between adjacent peaks.

[0065] Further define the candidate peak set:

[0066] Select the one with the larger amplitude from this set as the point of arrival of the direct wave:

[0067] in The amplitude of the direct wave arrival. This corresponds to the arrival time. If the signal contains only one peak, then it is defined as:

[0068] If no valid peak is detected, then .

[0069] Step S204: Based on the arrival time of the direct wave at the measurement location, the propagation velocity of the concrete transverse wave is obtained by inversion.

[0070] For all measuring points Collect the set of arrival times of direct waves:

[0071] in Let's define the location of the measuring point. Assume the sound wave travels at a speed of... For approximately linear propagation, the following conditions are met:

[0072] in For launch delay, This is the residual term. The velocity of sound can be estimated using the least squares method:

[0073] in, This is the average value for all measurement points. This represents the average value at arrival times.

[0074] Step S205: Based on the arrival time of the direct wave, the direct wave component is removed from the filtered ultrasonic pulse echo signal to obtain the reflected signal.

[0075] Step S206: Based on the transverse wave propagation velocity, the reflected signal is imaged using synthetic aperture focusing technology to generate B-scan images corresponding to each measurement position.

[0076] Step S207: The B-scan images generated from multiple continuous measurement areas are stitched together to obtain a continuous two-dimensional ultrasound image across the measurement areas.

[0077] Because of slight differences in the gain, coupling state, and external environmental conditions of the array system during different measurement processes, directly stitching together the original B-scan images can lead to significant inconsistencies in brightness and contrast, thus affecting the accuracy of feature recognition. To eliminate these unstructured differences, it is necessary to normalize the signals at each measurement point. The maximum value of each channel signal is normalized to ensure that the energy across different channels is on the same order of magnitude. This normalization operation ensures that subsequent matching and stitching primarily reflect the true differences in the internal structure of the medium, rather than instrument response or signal intensity changes, thereby significantly improving image contrast and matching stability.

[0078] The normalized image is segmented using Otsu adaptive thresholding to identify salient regions with high reflectivity (such as steel bars or hollow reflective objects). The Otsu algorithm automatically selects the threshold by minimizing the intra-class variance, maximizing the separation of the foreground and background in grayscale distribution, thus obtaining a stable binarization result.

[0079] Find the point with the strongest energy in the corresponding B region in the first and second B-scan images, and denote their coordinates as: ( )and( ).

[0080] Its mathematical essence is defined on a set of discrete pixel grid points Marking function on (pixel coordinate geometry) L Perform connected component decomposition on (x, y):

[0081] in Indicates the first in the image k A connected region, K This represents the total number of regions. The first step in region partitioning is to scan the binary image based on the adjacency relationship of pixels (in this embodiment, the 4-connectivity criterion is used), and determine all sets of pixels that satisfy the connectivity condition using a set union-find algorithm or a breadth-first search (BFS) algorithm. Each region's pixel set is uniquely numbered, thus forming a region labeling matrix. L ( x , y After obtaining the regional divisions, for each region... Calculate a series of geometric and statistical properties. Find the relevant properties in the binary map of region B from the first measurement and the binary map of region B from the second measurement. )and( The binary region containing the point ( ) is a reinforced concrete structure. Calculate the area of ​​the two corresponding regions. )and( The area of ​​the region where ) is located is denoted as: and .

[0082] In a binary image, the centroid describes the geometric center of a connected region and is an important parameter reflecting the spatial distribution characteristics of the target shape. For a binary region consisting of a set of pixels R, its centroid coordinates ( This can be calculated using the average coordinates of all pixels within the region. The mathematical expression is as follows:

[0083] in,( () represents the pixel coordinates belonging to region R. S Let be the area of ​​the region, i.e., the total number of pixels within the region. The centroids of the regions containing the reinforcing bars in the two images are calculated as follows: and .

[0084] To quantitatively determine whether corresponding regions in two images can be considered the same physical target (such as the same steel bar reflector), this study introduces two similarity indicators: depth difference and area difference ratio.

[0085] The depth difference between the points of greatest energy in two images is defined as:

[0086] Area difference ratio: Defined as the proportion of the area difference between two regions to the total area:

[0087] In this embodiment, the feature region refers to the connected region extracted from the B-scan image after the image processing process, which corresponds to the internal reflector (such as steel bar or cavity) and serves as the matching reference.

[0088] Matching region determination: When both the distance d and the area error err are sufficiently small (i.e., meeting the set threshold conditions), image A and image B are considered to have similar feature regions. During image stitching, the corresponding positions of these two feature regions can be aligned and stitched together.

[0089] In this measurement , . , .d =3; err =4.6%.

[0090] The presence of highly consistent reflection characteristics within region B in both measurements indicates that these regions can be considered matching areas corresponding to the same rebar location. Based on successful feature region matching, images 1 and 2 are spatially aligned and overlapped, with the pixel values ​​of the overlapping region taking the larger amplitude of the corresponding positions in both images to preserve key information about the reflected energy. This method yields continuous and complete two-dimensional synthetic aperture imaging results, effectively achieving spatial information fusion across measurement areas.

[0091] This embodiment can accurately identify direct waves and invert wave velocities, ensuring continuous imaging across survey areas, clearly presenting the distribution of reinforcing bars and defects, and taking into account the efficiency and accuracy of conventional tunnel secondary lining detection, providing a reliable solution for defect investigation during the construction period.

[0092] Example 2 The difference between this embodiment and Embodiment 1 is that the 8-connectivity criterion is adopted. This criterion is more conducive to identifying irregularly shaped and tortuous defects (such as meandering cracks and irregular cavities) as complete connected regions, avoiding them from being fragmented into multiple small regions, thereby more accurately reflecting the actual shape of the defects.

[0093] Accordingly, since the feature regions extracted by the 8-connectivity criterion are more complete, a more stringent similarity threshold is applied in the subsequent feature matching steps. For example, the allowable threshold for area difference ratio is set to 3%, and the allowable threshold for depth difference is set to 2mm. This ensures that only features with consistent spatial location and size are judged as matches, thereby significantly reducing splicing misalignment and making the transition of defect edges in the final continuous imaging image smoother and more natural.

[0094] Example 3 The difference between this embodiment and Embodiment 1 lies in the optimization of the array transducer's operating mode in step S201 (signal acquisition). Instead of strictly adhering to the "element 1 to 7 sequential transmission" method, a grouped alternating transmission mode is used. The eight elements are divided into odd-numbered groups (1, 3, 5, 7) and even-numbered groups (2, 4, 6, 8). All elements in the odd-numbered groups are excited first (and their signals are acquired), followed by the even-numbered groups. This mode reduces crosstalk between signals emitted by adjacent elements and may shorten the overall data acquisition time through optimized control logic, making it suitable for fast scanning scenarios with higher detection efficiency requirements. The subsequent signal processing and imaging procedures are exactly the same as in Embodiment 1.

[0095] This embodiment provides a low-frequency ultrasound imaging device, such as... Figure 2 As shown, it includes: The signal acquisition module is used to acquire ultrasonic pulse echo signals; A signal processing module is used to filter the signal; The direct wave processing module is used to identify the arrival time of the direct wave and remove the direct wave component from the filtered signal; The parameter inversion module is used to invert the shear wave propagation velocity based on the arrival time of the direct wave and its corresponding measurement location. The imaging module is used to generate a scanned image from the reflected signal after removing the direct wave; The stitching module is used to stitch together the scanned images to obtain a two-dimensional ultrasound image.

[0096] Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0097] In this embodiment, a low-frequency ultrasound imaging device is presented in the form of a functional unit. Here, a unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that execute one or more software or fixed programs, and / or other devices that can provide the above-mentioned functions.

[0098] This invention also provides a computer device having the above-described features. Figure 4 The image shows a low-frequency ultrasound imaging device.

[0099] Please see Figure 4 , Figure 4 This is a schematic diagram of the structure of a computer device provided in an optional embodiment of the present invention, such as... Figure 4 As shown, the computer device includes one or more processors 10, memory 20, and interfaces for connecting the components, including high-speed interfaces and low-speed interfaces. The components communicate with each other via different buses and can be mounted on a common motherboard or otherwise installed as needed. The processors can process instructions executed within the computer device, including instructions stored in or on memory to display graphical information of a GUI on external input / output devices (such as display devices coupled to the interfaces). In some alternative implementations, multiple processors and / or multiple buses can be used with multiple memories and multiple memory modules, if desired. Similarly, multiple computer devices can be connected, each providing some of the necessary operations (e.g., as a server array, a group of blade servers, or a multiprocessor system). Figure 4 Take a processor 10 as an example.

[0100] Processor 10 may be a central processing unit, a network processor, or a combination thereof. Processor 10 may further include a hardware chip. The hardware chip may be an application-specific integrated circuit (ASIC), a programmable logic device (PLD), or a combination thereof. The programmable logic device may be a complex programmable logic device (CAMP), a field-programmable gate array (FPGA), a general-purpose array logic (GPA), or any combination thereof.

[0101] The memory 20 stores instructions executable by at least one processor 10 to cause the at least one processor 10 to perform the method shown in the above embodiments.

[0102] The memory 20 may include a program storage area and a data storage area. The program storage area may store the operating system and applications required for at least one function; the data storage area may store data created based on the use of the computer device. Furthermore, the memory 20 may include high-speed random access memory and may also include non-transitory memory, such as at least one disk storage device, flash memory device, or other non-transitory solid-state storage device. In some alternative embodiments, the memory 20 may optionally include memory remotely located relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of such networks include, but are not limited to, the Internet, intranets, local area networks, mobile communication networks, and combinations thereof.

[0103] The memory 20 may include volatile memory, such as random access memory; the memory may also include non-volatile memory, such as flash memory, hard disk or solid-state drive; the memory 20 may also include a combination of the above types of memory.

[0104] The computer device also includes an input device 30 and an output device 40. The processor 10, memory 20, input device 30, and output device 20 can be connected via a bus or other means. Figure 4 Taking the example of a connection between China and Israel via a bus.

[0105] Input device 30 can receive input numerical or character information, and generate key signal inputs related to user settings and function control of the computer device, such as a touchscreen, keypad, mouse, trackpad, touchpad, joystick, one or more mouse buttons, trackball, joystick, etc. Output device 40 may include display devices, auxiliary lighting devices (e.g., LEDs), and haptic feedback devices (e.g., vibration motors). The aforementioned display devices include, but are not limited to, liquid crystal displays, light-emitting diodes, displays, and plasma displays. In some alternative embodiments, the display device may be a touchscreen.

[0106] The computer device also includes a communication interface 50 for communicating with other devices or communication networks.

[0107] This invention also provides a computer-readable storage medium. The methods described above according to embodiments of the invention can be implemented in hardware or firmware, or implemented as computer code that can be recorded on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and then stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code, which, when accessed and executed by the computer, processor, or hardware, implements the methods shown in the above embodiments.

[0108] A portion of this application can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to this application through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0109] Although embodiments of the invention have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of the invention, and such modifications and variations all fall within the scope defined by the appended claims.

Claims

1. A low-frequency ultrasound imaging method, characterized in that, include: An array transducer is used to acquire ultrasonic pulse echo signals at multiple measurement locations within a single measurement area; The acquired ultrasonic pulse echo signals are filtered. The arrival time of the direct wave in the filtered ultrasonic pulse echo signal is identified based on the peak difference abrupt change criterion. The transverse wave propagation velocity is obtained by inversion based on the arrival time of the direct wave at the measured location; Based on the arrival time of the direct wave, the direct wave component is removed from the filtered ultrasonic pulse echo signal to obtain the reflected signal; Based on the shear wave propagation velocity, the reflected signal is imaged using synthetic aperture focusing technology to generate a scanned image corresponding to the measurement area; The scanned images generated from multiple measurement areas are stitched together to obtain a two-dimensional ultrasound image.

2. The method according to claim 1, characterized in that, Identifying the arrival time of the direct wave in the filtered ultrasonic pulse echo signal based on abrupt changes in peak difference includes: Peak detection is performed on the filtered ultrasonic pulse echo signal to extract the amplitude and corresponding time of each peak and form a peak sequence. Calculate the amplitude difference between adjacent peaks in the peak sequence to obtain the amplitude difference sequence; If the wave peak sequence contains multiple wave peaks, then the wave peak time with the larger amplitude corresponding to the maximum value in the amplitude difference sequence is determined as the arrival time of the direct wave. If the wave crest sequence contains only one wave crest, then the time corresponding to that wave crest shall be taken as the arrival time of the direct wave. If no peak is detected, the ultrasound pulse echo signal is marked as invalid data.

3. The method according to claim 1, characterized in that, The inversion yields the transverse wave propagation velocity, including: Collect the arrival times of direct waves at all the measurement locations and the corresponding measurement point locations; Establish a mapping relationship between arrival time and spatial location; The relationship model was linearly fitted using the least squares method. The reciprocal of the slope of the fitting result is the transverse wave propagation speed.

4. The method according to claim 1, characterized in that, The process of stitching together scanned images generated from multiple measurement areas includes: Normalize each scanned image; The normalized image is binarized, and the regions with reflectance intensity higher than the threshold are extracted to obtain the binarized image. Perform connected component decomposition on the binarized image and calculate the centroid coordinates and area of ​​each connected component; Matching is performed by comparing the depth similarity and area similarity of corresponding connected regions between adjacent images; Based on the matching results, spatial alignment and pixel fusion are performed on multiple scanned images.

5. The method according to claim 2, characterized in that, The calculation of the amplitude difference between adjacent peaks in the peak sequence includes: The amplitude difference between adjacent peaks is calculated using the following formula: ； ； in This represents the amplitude of the k-th peak. This represents the amplitude of the (k+1)th peak. This corresponds to the amplitude difference.

6. The method according to claim 4, characterized in that, The binarized image is decomposed into connected components, and the centroid coordinates and area of ​​each connected component are calculated, including: The binarized image is scanned based on the adjacency of pixels; Pixels that meet the connectivity condition are divided into the same connected region, and each connected region is assigned a unique number to form a region label matrix; Based on the region labeling matrix, the total number of pixels contained in each connected region is determined, which is used as the area of ​​that region; Based on the region label matrix and the coordinates of each pixel, the centroid coordinates of each connected region are calculated.

7. A low-frequency ultrasound imaging device, characterized in that, The device includes: The signal acquisition module is used to acquire ultrasonic pulse echo signals; A signal processing module is used to filter the signal; The direct wave processing module is used to identify the arrival time of the direct wave and remove the direct wave component from the filtered signal; The parameter inversion module is used to invert the shear wave propagation velocity based on the arrival time of the direct wave and its corresponding measurement location. The imaging module is used to generate a scanned image from the reflected signal after removing the direct wave; The stitching module is used to stitch together the scanned images to obtain a two-dimensional ultrasound image.

8. A computer device, characterized in that, include: A memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, the processor executing the computer instructions to perform the method of any one of claims 1 to 6.

9. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing a computer to perform the method of any one of claims 1 to 6.

10. A computer program product, characterized in that, Includes computer instructions for causing a computer to perform the method of any one of claims 1 to 6.

Images