Unmanned aerial vehicle detection and imaging device and method based on electromagnetic wave induced ultrasound

Abstract

The invention discloses an unmanned aerial vehicle detection and imaging device and method based on electromagnetic wave induced ultrasound, and belongs to the technical field of unmanned aerial vehicle detection. The device comprises an unmanned aerial vehicle body, an electromagnetic wave transmitting module, an ultrasonic receiving module, a flight control module and an image processing and analyzing module. The method comprises the following steps: controlling an unmanned aerial vehicle to fly to a target area and planning a scanning path; pulse electromagnetic waves are transmitted to a tested structure through an airborne electromagnetic wave transmitting device, and an internal electromagnetic absorbing material is excited to generate ultrasonic waves; synchronously receiving ultrasonic waves reflected or scattered by the internal defect through an airborne ultrasonic receiving device, and converting the ultrasonic waves into electric signals; and performing feature extraction on the electric signal to generate defect distribution image data. According to the invention, remote electromagnetic excitation and non-contact ultrasonic detection are integrated through the unmanned aerial vehicle platform, so that high-altitude, large-range and non-contact internal defect efficient detection and high-precision imaging of a large-scale structure are realized.

Description

Technical Field

[0001] This invention relates to the field of unmanned aerial vehicle (UAV) technology, and in particular to a UAV detection and imaging device and method based on electromagnetic wave-induced ultrasound. Background Technology

[0002] In the field of non-destructive testing of large engineering structures (such as high-rise buildings and bridges), traditional methods mainly rely on manual climbing or scaffolding for contact ultrasonic testing. This method first requires placing a coupling agent and an ultrasonic probe on the surface to be inspected. The probe emits and receives ultrasonic waves propagating within the material, and internal defects are determined based on the echo signals. However, this method has significant limitations: firstly, manual high-altitude work is inefficient, carries high safety risks, and is difficult to cover large areas or complex geometric regions; secondly, contact probes are easily affected by surface roughness, curvature, and coupling state, which can introduce detection errors and lead to decreased imaging accuracy. In recent years, although some research has attempted to mount visual sensors on drones for exterior inspection, these methods can only identify surface defects and cannot detect internal structural damage.

[0003] Therefore, existing technologies have not yet been able to effectively achieve a solution for large structures that combines non-contact, wide-area coverage, and high-precision internal defect detection capabilities. Summary of the Invention

[0004] This invention provides a drone detection and imaging device and method based on electromagnetic wave-induced ultrasound, which can achieve non-contact, high-efficiency, and high-precision detection and imaging of internal defects in large structures.

[0005] An embodiment of the present invention provides a drone detection and imaging device based on electromagnetic wave-induced ultrasound, comprising: The drone itself is used to carry various functional modules and fly to the target area of ​​the structure being tested. An electromagnetic wave transmitting module is installed on the UAV body and is used to transmit pulsed electromagnetic waves to the structure under test in order to induce ultrasonic waves in the electromagnetic absorbing material inside the structure under test. An ultrasonic receiving module, disposed on the UAV body, is used to receive ultrasonic waves reflected or scattered by internal defects of the structure under test, and convert them into ultrasonic electrical signals. The flight control module is used to control the UAV body to fly along a preset scanning path, so that the electromagnetic wave transmitting module and the ultrasonic receiving module synchronously detect the surface of the structure under test; and,

[0006] The image processing and analysis module is used to extract features from the ultrasonic electrical signal and generate image data characterizing the distribution of internal defects in the tested structure.

[0007] As an improvement to the above solution, the electromagnetic wave transmitting module includes a high-frequency generator and a directional antenna. The high-frequency generator is used to generate electromagnetic wave signals with a frequency range of 1MHz to 10THz and adjustable pulse width. The directional antenna is used to radiate the electromagnetic wave signals directionally to the structure under test. The output power and pulse parameters of the electromagnetic wave transmitting module are adaptively adjusted according to the material type and thickness of the structure under test.

[0008] As an improvement to the above solution, the ultrasonic receiving module is a non-contact microwave interferometer, non-contact laser interferometer, or air-coupled ultrasonic sensor, used to receive the ultrasonic waves with a frequency range of 20kHz to 10MHz and convert the ultrasonic waves into ultrasonic electrical signals. The ultrasonic receiving module and the electromagnetic wave transmitting module maintain a fixed spatial relative position on the UAV body to achieve synchronous detection.

[0009] As an improvement to the above solution, the flight control module includes a GPS positioning unit, an inertial measurement unit, and a path planning unit; the GPS positioning unit and the inertial measurement unit are used to acquire the real-time position and attitude information of the UAV body; the path planning unit is used to generate a full-coverage scanning path based on the geometric features of the structure under test, and, in conjunction with the real-time position and attitude information, control the UAV body to perform autonomous flight and dynamic obstacle avoidance with centimeter-level accuracy according to the full-coverage scanning path.

[0010] As an improvement to the above scheme, the image processing and analysis module includes a signal preprocessing unit and a deep learning reconstruction unit. The signal preprocessing unit is used to filter and denoise the ultrasonic electrical signal. The deep learning reconstruction unit performs feature extraction and spatial mapping on the preprocessed ultrasonic electrical signal based on the U-Net neural network, generates a three-dimensional defect distribution image, and automatically labels the location coordinates, size parameters, and type attributes of the defects.

[0011] Another embodiment of the present invention provides a method for UAV detection and imaging based on electromagnetic wave-induced ultrasound, comprising the following steps: Control the drone to fly to the target area of ​​the structure under test, and plan a scanning path covering the surface to be inspected based on the geometric features of the structure under test; The electromagnetic wave emitting device installed on the UAV emits pulsed electromagnetic waves to the structure under test, causing the electromagnetic absorbing material inside the structure under test to absorb electromagnetic energy and induce the generation of ultrasonic waves. The ultrasonic electrical signal is obtained by synchronously receiving the ultrasonic waves reflected or scattered by the internal defects of the structure under test through the ultrasonic receiving device installed on the UAV. Feature extraction is performed on the ultrasonic electrical signal to generate image data characterizing the distribution of internal defects in the tested structure.

[0012] As an improvement to the above scheme, before transmitting the pulsed electromagnetic wave, the method further includes: Based on the material electromagnetic parameters and thickness information of the structure under test, the center frequency, output power and pulse width of the pulsed electromagnetic wave are configured so that the electromagnetic wave energy generates a depth-controllable thermoacoustic conversion effect in the electromagnetic absorbing material inside the structure under test, thereby inducing the generation of the ultrasonic wave of the corresponding frequency.

[0013] As an improvement to the above solution, the step of synchronously receiving the ultrasonic waves reflected or scattered by internal defects in the structure under test through an ultrasonic receiving device installed on the UAV to obtain ultrasonic electrical signals includes: The ultrasonic waves with a frequency range of 20 kHz to 10 MHz are received using non-contact microwave interferometry, non-contact laser interferometry, or air coupling. The ultrasonic waves are converted into ultrasonic electrical signals, and the ultrasonic electrical signals are digitally sampled through a multi-channel synchronous acquisition method, so that the ultrasonic electrical signals are associated with the spatial position information of the UAV in a time-space synchronous manner.

[0014] As an improvement to the above solution, the step of controlling the UAV to fly to the target area of ​​the structure under test and planning a scanning path covering the surface to be inspected based on the geometric features of the structure under test includes: Acquire the three-dimensional geometric data of the structure under test, and generate a full-coverage scanning path based on the three-dimensional geometric data and the detection range requirements; The UAV is located in real time using RTK positioning technology or SLAM algorithm, and controlled to perform autonomous flight and dynamic obstacle avoidance with centimeter-level accuracy according to the full-coverage scanning path.

[0015] As an improvement to the above scheme, the step of extracting features from the ultrasonic electrical signal to generate image data characterizing the distribution of internal defects in the tested structure includes: The ultrasonic electrical signal is subjected to bandpass filtering and wavelet denoising to obtain a purified time-domain signal; the purified time-domain signal is then spatiotemporally registered with the corresponding spatial location information. The registered data is input into a pre-trained U-Net neural network for end-to-end feature learning, and the three-dimensional spatial distribution data of the internal defects of the tested structure is output. Based on the three-dimensional spatial distribution data, a visual image containing defect boundary contours, depth coordinates, and classification labels is automatically generated.

[0016] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: By using a drone as a mobile platform, a collaborative system is constructed integrating an electromagnetic wave transmitting module, an ultrasonic receiving module, a flight control module, and an image processing and analysis module. The flight control module controls the drone to autonomously fly along a preset scanning path, achieving full coverage detection of the surface to be inspected on the structure under test, avoiding the risks of manual high-altitude operations. The electromagnetic wave transmitting module emits pulsed electromagnetic waves to the structure under test, inducing ultrasonic waves to be generated by electromagnetic absorption materials within the structure. This allows for ultrasonic signal excitation without contact, avoiding interference from surface roughness. The ultrasonic receiving module simultaneously receives ultrasonic waves reflected / scattered from defects and converts them into electrical signals. The image processing and analysis module extracts features to generate a defect distribution image, achieving high-precision imaging. The collaborative work of these modules significantly improves detection efficiency and coverage. This technical solution, through the synergistic integration of electromagnetic wave-induced ultrasound and drone detection, effectively solves the technical pain points of traditional detection methods, achieving efficient, wide-range, and high-precision non-contact detection of large structures. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the structure of a drone detection and imaging device based on electromagnetic wave-induced ultrasound according to an embodiment of the present invention; Figure 2 This is a flowchart illustrating a method for unmanned aerial vehicle (UAV) detection and imaging based on electromagnetic wave-induced ultrasound, provided by an embodiment of the present invention.

[0018] Figure 3 This is a diagram showing the measurement of acoustic pressure electrical signals according to the present invention. Detailed Implementation

[0019] 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, and 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.

[0020] See Figure 1This is a schematic diagram of a UAV detection and imaging device based on electromagnetic wave-induced ultrasound according to an embodiment of the present invention. The UAV detection and imaging device based on electromagnetic wave-induced ultrasound includes: a UAV body (not shown), an electromagnetic wave transmitting module 10, an ultrasonic receiving module 20, a flight control module 30, and an image processing and analysis module 40. The UAV body 10 is used to carry the various functional modules and fly to the target area of ​​the structure under test. The electromagnetic wave transmitting module 10 is disposed on the UAV body 10 and is used to transmit pulsed electromagnetic waves to the structure under test to induce ultrasonic waves in the electromagnetic absorbing material inside the structure under test. The ultrasonic receiving module 20 is disposed on the UAV body 10 and is used to receive the ultrasonic waves reflected or scattered by defects inside the structure under test and convert them into ultrasonic electrical signals. The flight control module 30 is used to control the UAV body 10 to fly according to a preset scanning path, so that the electromagnetic wave transmitting module 10 and the ultrasonic receiving module 20 synchronously detect the surface to be inspected of the structure under test. The image processing and analysis module 40 is used to extract features from the ultrasonic electrical signals and generate image data characterizing the distribution of defects inside the structure under test.

[0021] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: Using the UAV body 10 as a mobile platform, an electromagnetic wave transmitting module 10, an ultrasonic receiving module 20, a flight control module 30, and an image processing and analysis module 40 are integrated to construct a collaborative working system. The flight control module 30 controls the UAV to fly autonomously along a preset scanning path, achieving full coverage detection of the surface to be inspected on the structure under test, avoiding the risks of manual high-altitude operations. The electromagnetic wave transmitting module 10 emits pulsed electromagnetic waves to the structure under test, inducing ultrasonic waves to be generated by the electromagnetic absorption material inside the structure. Ultrasonic signals are excited without contact, avoiding interference from surface roughness. The ultrasonic receiving module 20 simultaneously receives ultrasonic waves reflected / scattered from defects and converts them into electrical signals. The image processing and analysis module 40 extracts features to generate a defect distribution image, achieving high-precision imaging. The collaborative work of these modules significantly improves detection efficiency and coverage. This technical solution, through the synergistic integration of electromagnetic wave-induced ultrasound and UAV detection, effectively solves the technical pain points of traditional detection methods, achieving efficient, wide-range, and high-precision non-contact detection of large structures.

[0022] As an improvement to the above solution, the electromagnetic wave transmitting module 10 includes a high-frequency generator and a directional antenna. The high-frequency generator is used to generate an electromagnetic wave signal with a frequency range of 1MHz to 10THz and an adjustable pulse width. The directional antenna is used to radiate the electromagnetic wave signal directionally to the structure under test. The output power and pulse parameters of the electromagnetic wave transmitting module 10 are adaptively adjusted according to the material type and thickness of the structure under test.

[0023] In this embodiment, an electromagnetic wave signal with a specific frequency and pulse width is first generated using a high-frequency generator to match the dielectric loss characteristics of the electromagnetic absorbing material in the structure under test. Then, the electromagnetic wave energy is focused and radiated onto the target area using a directional antenna, achieving localized deposition of electromagnetic energy within the material. Next, the output power and pulse parameters are dynamically adjusted according to the material type and thickness, improving the thermoacoustic conversion efficiency and controlling the ultrasonic excitation depth. Finally, ultrasonic waves with a specific frequency and sound pressure are generated within the electromagnetic absorbing material, ultimately enhancing the excitation sensitivity to defects of different depths. Therefore, this embodiment achieves depth-controllable ultrasonic excitation for structures of different materials and thicknesses through adaptive adjustment of electromagnetic wave parameters.

[0024] In this embodiment, the operation of the electromagnetic wave transmitting module 10 begins with parameter configuration. The operator inputs basic information about the structure under test via a ground control station, including material type (e.g., reinforced concrete, composite material, or metal) and average thickness. The module's embedded controller automatically calculates and sets the center frequency, pulse width, and peak power of the pulsed electromagnetic wave based on a built-in material electromagnetic parameter database and a thickness-frequency correspondence model. For example, for a 50 cm thick concrete wall, the system selects a relatively low frequency (e.g., 200 MHz) and a long pulse width (e.g., 1 microsecond) to ensure that the electromagnetic wave energy can fully penetrate and excite the electromagnetic absorbing materials (e.g., aggregate or moisture) in the deeper areas; while for a thin metal skin, a higher frequency (e.g., 2 GHz) and a short pulse (e.g., 0.1 microsecond) are selected to improve the excitation resolution and accuracy of near-surface defects. After parameter setting, the high-frequency generator produces a corresponding radio frequency pulse sequence, which is boosted to the required power level by a power amplifier and finally formed into a focused beam by a directional antenna (e.g., a pyramidal horn antenna), radiating vertically towards the structural surface directly below the UAV's hovering point. The entire launch process is synchronized with the drone's hovering state, ensuring that the antenna maintains the optimal radiation distance and angle with the target surface during each firing. Furthermore, the system supports dynamic adjustment of these parameters during scanning based on preset programs or real-time feedback to accommodate potential thickness or material variations in different parts of the structure.

[0025] As an improvement to the above solution, the ultrasonic receiving module 20 is a non-contact microwave interferometer, non-contact laser interferometer, or air-coupled ultrasonic sensor, used to receive the ultrasonic waves with a frequency range of 20kHz to 10MHz and convert the ultrasonic waves into ultrasonic electrical signals. The ultrasonic receiving module 20 and the electromagnetic wave transmitting module 10 maintain a fixed spatial relative position on the UAV body 10 to achieve synchronous detection.

[0026] In this embodiment, non-contact laser interferometry or air coupling is first used to receive ultrasonic waves, avoiding acoustic coupling loss caused by physical contact between the probe and the surface being measured. Then, the electromagnetic wave transmitting module 10 and the ultrasonic receiving module 20 are configured in a fixed spatial relative position to achieve precise synchronization and spatial consistency of the excitation-reception timing. Next, ultrasonic echo signals in the 20kHz–10MHz frequency band are received, improving the ability to capture scattered signals from minute defects. Finally, the ultrasonic vibrations are converted into electrical signals for output, ultimately improving the signal-to-noise ratio and defect detection sensitivity. Therefore, this embodiment achieves high-fidelity ultrasonic signal acquisition unaffected by surface condition interference through non-contact reception and spatial synchronization.

[0027] The operation of the ultrasonic receiving module 20 is strictly synchronized with the electromagnetic wave emission. When the ultrasonic waves generated by the pulsed electromagnetic waves propagate inside the structure and encounter defects (such as cracks or holes), reflection and scattering occur, and some of the ultrasonic waves eventually propagate to the surface of the structure, causing micron-level vibrations. If a laser ultrasonic probe is used, it emits a continuous-wave laser beam towards the same detection point on the surface. After being reflected by the surface, the optical path is modulated by the surface vibration. The reflected light and the reference light interfere in the interferometer, and the change in light intensity is captured by the photodetector and converted into an analog voltage signal that precisely corresponds to the ultrasonic waveform. If an air-coupled ultrasonic sensor is used, its piezoelectric crystal is coupled to the air through an acoustic impedance matching layer, directly sensing the sound pressure wave radiated from the surface vibration into the air and converting it into an electrical signal. The analog front-end circuit of the receiving module performs preliminary amplification and impedance matching on this weak signal. To ensure signal integrity, the receiving module and the electromagnetic wave transmitting antenna are rigidly connected on the UAV payload platform, and their central axes are mechanically aligned parallel, so that the sound field center of the laser spot or the air-coupled sensor basically coincides with the electromagnetic wave radiation area in terms of detection distance. This fixed spatial relationship ensures that after each excitation, the receiving module captures ultrasonic echo signals from the same excitation-detection volume, laying the geometric foundation for subsequent accurate imaging.

[0028] As an improvement to the above solution, the flight control module 30 includes a GPS positioning unit, an inertial measurement unit, and a path planning unit; the GPS positioning unit and the inertial measurement unit are used to acquire the real-time position and attitude information of the UAV body 10; the path planning unit is used to generate a full-coverage scanning path based on the geometric features of the structure under test, and, in conjunction with the real-time position and attitude information, control the UAV body 10 to perform autonomous flight and dynamic obstacle avoidance with centimeter-level accuracy according to the full-coverage scanning path.

[0029] In this embodiment, high-precision pose information of the UAV is first acquired through a combination of GPS and IMU, providing a reliable navigation reference for scanning path tracking. Then, a full-coverage scanning path is generated based on the three-dimensional geometric data of the structure under test, achieving complete coverage of the surface to be inspected. Next, real-time pose information is fused for path tracking control, improving the consistency between the flight trajectory and the preset path. Finally, obstacle perception is combined to achieve dynamic obstacle avoidance, ultimately ensuring safe and continuous detection in complex environments. Therefore, this embodiment achieves centimeter-level precision full-coverage scanning of the surface of the structure under test through the synergy of high-precision positioning and intelligent path planning.

[0030] Specifically, the working process of this embodiment is as follows: Before the mission begins, a 3D digital model (such as a CAD model or laser scan point cloud) of the structure under test is imported into the ground control software. The path planning unit automatically generates a "bow"-shaped or spiral flight path covering all surfaces to be inspected, based on the model's geometric features, a preset scanning resolution (such as a 5-centimeter grid), and the effective detection range of the sensors, and decomposes it into a series of ordered waypoints. After the UAV takes off, the core flight controller of the flight control module 30 begins operation. It reads the centimeter-level global position coordinates provided by the RTK-GPS module in real time, as well as the high-frequency attitude and angular velocity data provided by the inertial measurement unit (IMU, containing a three-axis gyroscope and accelerometer). Through sensor fusion algorithms (such as an extended Kalman filter), it calculates the UAV's high-refresh-rate, high-precision real-time position, velocity, attitude, and heading. The controller compares this real-time state with the current target waypoint and, through PID or more advanced model predictive control algorithms, calculates the thrust commands for each motor, driving the UAV to smoothly and accurately reach each waypoint and achieve stable hovering. During hovering, electromagnetic wave transmission and ultrasonic reception are triggered. Meanwhile, the onboard forward-looking binocular vision sensor or lidar continuously scans the environment ahead of the drone, constructing a local obstacle map. Once an unmodeled obstacle (such as a temporary structure or tree) is detected on the original path, the dynamic obstacle avoidance unit will immediately replan a safe detour path within the local area, guiding the drone to automatically return to the original scanning path after avoiding the obstacle, ensuring the continuity and safety of the detection mission.

[0031] As an improvement to the above scheme, the image processing and analysis module 40 includes a signal preprocessing unit and a deep learning reconstruction unit. The signal preprocessing unit is used to filter and denoise the ultrasonic electrical signal. The deep learning reconstruction unit performs feature extraction and spatial mapping on the preprocessed ultrasonic electrical signal based on the U-Net neural network, generates a three-dimensional defect distribution image, and automatically labels the location coordinates, size parameters, and type attributes of the defects.

[0032] In this embodiment, bandpass filtering and wavelet denoising are first used to suppress environmental noise and system interference, thereby improving the signal-to-noise ratio of the ultrasonic signal. Next, the purified signal and spatial location information are spatiotemporally registered to achieve a precise correspondence between the ultrasonic echo and physical coordinates. Then, the signal is input into a pre-trained U-Net network for end-to-end feature learning, which improves the ability to identify weak defect scattering signals. Finally, a three-dimensional defect distribution is generated based on the network output, and attributes are automatically labeled, ultimately achieving precise defect localization and qualitative analysis. Therefore, this embodiment achieves high-precision, automated three-dimensional defect imaging and identification through cascaded signal preprocessing and deep learning reconstruction.

[0033] In this embodiment, the image processing and analysis module 40 intelligently processes the acquired raw data to generate the final defect image. First, the signal preprocessing unit standardizes the multi-channel digital signal transmitted from the ultrasonic receiving module 20. It first applies a digital bandpass filter that matches the frequency band of the excitation ultrasonic wave (for example, for concrete detection, the passband may be set to 100kHz to 1MHz) to retain the main frequency components of the defect echo and filter out low-frequency mechanical vibrations and high-frequency electronic noise. Subsequently, a wavelet transform threshold denoising algorithm is used to further suppress random noise in the signal and improve the signal-to-noise ratio. Each time-domain waveform signal after preprocessing is strictly bound (spatiotemporal registration) to the precise three-dimensional spatial coordinates and attitude angles of the UAV recorded by the flight control module 30 at the time of signal acquisition, forming a complete data point containing spatial location information and time series information. All data points are aggregated to form a three-dimensional data volume (two horizontal spatial dimensions and one time / depth dimension). Subsequently, the deep learning reconstruction unit is activated. The core of this unit is a U-Net convolutional neural network model that has been pre-trained on a large amount of simulated and measured data. The 3D data volume is input into the network. Through multiple convolutional and downsampling operations in the encoder section, abstract feature representations are gradually extracted. Then, through upsampling and skip connections with corresponding layers in the decoder section, spatial resolution is gradually restored, ultimately outputting a 3D probability map with the same spatial size as the input data volume. The value of each voxel represents the probability of a defect existing at that spatial location. Binarizing this probability map with a threshold (e.g., 0.7) yields a clear binary image of the 3D spatial distribution of defects. The post-processing algorithm automatically identifies connected regions (i.e., individual defects) in the image, calculates their 3D centroid coordinates as the defect location, calculates their volume or circumscribed cuboid size as quantization parameters, and automatically determines the defect type (e.g., cracks, peeling, voids) using a lightweight classifier (e.g., support vector machine) based on the average amplitude and spectral characteristics of the signal in that region. All analysis results are automatically integrated to generate a final inspection report containing a 3D visualization rendering and a structured data table. It is understood that the relevant algorithms can utilize existing technologies.

[0034] See Figure 2 This is a flowchart illustrating a method for UAV detection and imaging based on electromagnetic wave-induced ultrasound according to an embodiment of the present invention. The method includes the following steps: S10, control the UAV to fly to the target area of ​​the structure under test, and plan a scanning path covering the surface to be inspected according to the geometric features of the structure under test; S11, pulsed electromagnetic waves are emitted to the structure under test by an electromagnetic wave emitting device installed on the UAV, causing the electromagnetic absorbing material inside the structure under test to absorb electromagnetic energy and induce the generation of ultrasonic waves. S12, the ultrasonic waves reflected or scattered by the internal defects of the structure under test are synchronously received by the ultrasonic receiving device installed on the UAV, and an ultrasonic electrical signal is obtained. S13, extract features from the ultrasonic electrical signal to generate image data characterizing the distribution of internal defects in the tested structure.

[0035] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: By using a drone as a mobile platform, a collaborative system is constructed integrating an electromagnetic wave transmitting module, an ultrasonic receiving module, a flight control module, and an image processing and analysis module. The flight control module controls the drone to autonomously fly along a preset scanning path, achieving full coverage detection of the surface to be inspected on the structure under test, avoiding the risks of manual high-altitude operations. The electromagnetic wave transmitting module emits pulsed electromagnetic waves to the structure under test, inducing ultrasonic waves to be generated by electromagnetic absorption materials within the structure. This allows for ultrasonic signal excitation without contact, avoiding interference from surface roughness. The ultrasonic receiving module simultaneously receives ultrasonic waves reflected / scattered from defects and converts them into electrical signals. The image processing and analysis module extracts features to generate a defect distribution image, achieving high-precision imaging. The collaborative work of these modules significantly improves detection efficiency and coverage. This technical solution, through the synergistic integration of electromagnetic wave-induced ultrasound and drone detection, effectively solves the technical pain points of traditional detection methods, achieving efficient, wide-range, and high-precision non-contact detection of large structures.

[0036] As an improvement to the above scheme, before transmitting the pulsed electromagnetic wave, the method further includes: Based on the material electromagnetic parameters and thickness information of the structure under test, the center frequency, output power and pulse width of the pulsed electromagnetic wave are configured so that the electromagnetic wave energy generates a depth-controllable thermoacoustic conversion effect in the electromagnetic absorbing material inside the structure under test, thereby inducing the generation of the ultrasonic wave of the corresponding frequency.

[0037] As an improvement to the above solution, the step of synchronously receiving the ultrasonic waves reflected or scattered by internal defects in the structure under test through an ultrasonic receiving device installed on the UAV to obtain ultrasonic electrical signals includes: The ultrasonic waves with a frequency range of 20 kHz to 10 MHz are received using non-contact microwave interferometry, non-contact laser interferometry, or air coupling. The ultrasonic waves are converted into ultrasonic electrical signals, and the ultrasonic electrical signals are digitally sampled through a multi-channel synchronous acquisition method, so that the ultrasonic electrical signals are associated with the spatial position information of the UAV in a time-space synchronous manner.

[0038] As an improvement to the above solution, the step of controlling the UAV to fly to the target area of ​​the structure under test and planning a scanning path covering the surface to be inspected based on the geometric features of the structure under test includes: Acquire the three-dimensional geometric data of the structure under test, and generate a full-coverage scanning path based on the three-dimensional geometric data and the detection range requirements; The UAV is located in real time using RTK positioning technology or SLAM algorithm, and controlled to perform autonomous flight and dynamic obstacle avoidance with centimeter-level accuracy according to the full-coverage scanning path.

[0039] As an improvement to the above scheme, the step of extracting features from the ultrasonic electrical signal to generate image data characterizing the distribution of internal defects in the tested structure includes: The ultrasonic electrical signal is subjected to bandpass filtering and wavelet denoising to obtain a purified time-domain signal; the purified time-domain signal is then spatiotemporally registered with the corresponding spatial location information. The registered data is input into a pre-trained U-Net neural network for end-to-end feature learning, and the three-dimensional spatial distribution data of the internal defects of the tested structure is output. Based on the three-dimensional spatial distribution data, a visual image containing defect boundary contours, depth coordinates, and classification labels is automatically generated.

[0040] It is understood that the embodiments of the UAV detection and imaging method based on electromagnetic wave induced ultrasound can be referred to the embodiments of the UAV detection and imaging device based on electromagnetic wave induced ultrasound described above, and will not be repeated here.

[0041] It should be noted that the device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Furthermore, in the accompanying drawings of the device embodiments provided by this invention, the connection relationships between modules indicate that they have communication connections, which can be specifically implemented as one or more communication buses or signal lines. Those skilled in the art can understand and implement this without any creative effort.

[0042] The above description represents the preferred embodiments of the present invention. It should be noted that those skilled in the art can make various improvements and modifications without departing from the principles of the present invention, and these improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A UAV detection and imaging device based on electromagnetic wave-induced ultrasound, characterized in that, include: The drone itself is used to carry various functional modules and fly to the target area of ​​the structure being tested. An electromagnetic wave transmitting module is installed on the UAV body and is used to transmit pulsed electromagnetic waves to the structure under test in order to induce ultrasonic waves in the electromagnetic absorbing material inside the structure under test. An ultrasonic receiving module, disposed on the UAV body, is used to receive ultrasonic waves reflected or scattered by internal defects of the structure under test, and convert them into ultrasonic electrical signals. The flight control module is used to control the UAV body to fly along a preset scanning path, so that the electromagnetic wave transmitting module and the ultrasonic receiving module synchronously detect the surface of the structure under test; and, The image processing and analysis module is used to extract features from the ultrasonic electrical signal and generate image data characterizing the distribution of internal defects in the tested structure.

2. The UAV detection and imaging device based on electromagnetic wave-induced ultrasound as described in claim 1, characterized in that, The electromagnetic wave transmitting module includes a high-frequency generator and a directional antenna. The high-frequency generator is used to generate electromagnetic wave signals with a frequency range of 1MHz to 10THz and an adjustable pulse width. The directional antenna is used to radiate the electromagnetic wave signals directionally to the structure under test. The output power and pulse parameters of the electromagnetic wave transmitting module are adaptively adjusted according to the material type and thickness of the structure under test.

3. The UAV detection and imaging device based on electromagnetic wave-induced ultrasound as described in claim 1, characterized in that, The ultrasonic receiving module is a non-contact microwave interferometer, non-contact laser interferometer, or air-coupled ultrasonic sensor, used to receive ultrasonic waves with a frequency range of 20kHz to 10MHz and convert the ultrasonic waves into ultrasonic electrical signals. The ultrasonic receiving module and the electromagnetic wave transmitting module maintain a fixed spatial relative position on the UAV body to achieve synchronous detection.

4. The UAV detection and imaging device based on electromagnetic wave-induced ultrasound as described in claim 1, characterized in that, The flight control module includes a GPS positioning unit, an inertial measurement unit, and a path planning unit. The GPS positioning unit and the inertial measurement unit are used to acquire the real-time position and attitude information of the UAV body. The path planning unit is used to generate a full-coverage scanning path based on the geometric features of the structure under test, and, in conjunction with the real-time position and attitude information, control the UAV body to perform autonomous flight and dynamic obstacle avoidance with centimeter-level accuracy according to the full-coverage scanning path.

5. The UAV detection and imaging device based on electromagnetic wave-induced ultrasound as described in claim 1, characterized in that, The image processing and analysis module includes a signal preprocessing unit and a deep learning reconstruction unit. The signal preprocessing unit is used to filter and denoise the ultrasonic electrical signal. The deep learning reconstruction unit performs feature extraction and spatial mapping on the preprocessed ultrasonic electrical signal based on the U-Net neural network, generates a three-dimensional defect distribution image, and automatically labels the location coordinates, size parameters, and type attributes of the defects.

6. A method for unmanned aerial vehicle (UAV) detection and imaging based on electromagnetic wave-induced ultrasound, characterized in that, Includes the following steps: Control the drone to fly to the target area of ​​the structure under test, and plan a scanning path covering the surface to be inspected based on the geometric features of the structure under test; The electromagnetic wave emitting device installed on the UAV emits pulsed electromagnetic waves to the structure under test, causing the electromagnetic absorbing material inside the structure under test to absorb electromagnetic energy and induce the generation of ultrasonic waves. The ultrasonic electrical signal is obtained by synchronously receiving the ultrasonic waves reflected or scattered by the internal defects of the structure under test through the ultrasonic receiving device installed on the UAV. Feature extraction is performed on the ultrasonic electrical signal to generate image data characterizing the distribution of internal defects in the tested structure.

7. The UAV detection and imaging method based on electromagnetic wave-induced ultrasound as described in claim 6, characterized in that, Before transmitting the pulsed electromagnetic wave, the method further includes: Based on the material electromagnetic parameters and thickness information of the structure under test, the center frequency, output power and pulse width of the pulsed electromagnetic wave are configured so that the electromagnetic wave energy generates a depth-controllable thermoacoustic conversion effect in the electromagnetic absorbing material inside the structure under test, thereby inducing the generation of the ultrasonic wave of the corresponding frequency.

8. The UAV detection and imaging method based on electromagnetic wave-induced ultrasound as described in claim 6, characterized in that, The step of synchronously receiving ultrasonic waves reflected or scattered by internal defects in the structure under test through an ultrasonic receiving device installed on the UAV to obtain ultrasonic electrical signals includes: The ultrasonic waves with a frequency range of 20 kHz to 10 MHz are received using non-contact microwave interferometry, non-contact laser interferometry, or air coupling. The ultrasonic waves are converted into ultrasonic electrical signals, and the ultrasonic electrical signals are digitally sampled through a multi-channel synchronous acquisition method, so that the ultrasonic electrical signals are associated with the spatial position information of the UAV in a time-space synchronous manner.

9. The UAV detection and imaging method based on electromagnetic wave-induced ultrasound as described in claim 6, characterized in that, The process of controlling the UAV to fly to the target area of ​​the structure under test and planning a scanning path covering the surface to be inspected based on the geometric features of the structure under test includes: Acquire the three-dimensional geometric data of the structure under test, and generate a full-coverage scanning path based on the three-dimensional geometric data and the detection range requirements; The UAV is located in real time using RTK positioning technology or SLAM algorithm, and the UAV is controlled to perform autonomous flight and dynamic obstacle avoidance with centimeter-level accuracy according to the full-coverage scanning path.

10. The UAV detection and imaging method based on electromagnetic wave-induced ultrasound as described in claim 6, characterized in that, The step of extracting features from the ultrasonic electrical signal to generate image data characterizing the distribution of internal defects in the tested structure includes: The ultrasonic electrical signal is subjected to bandpass filtering and wavelet denoising to obtain a purified time-domain signal; the purified time-domain signal is then spatiotemporally registered with the corresponding spatial location information. The registered data is input into a pre-trained U-Net neural network for end-to-end feature learning, and the three-dimensional spatial distribution data of the internal defects of the tested structure is output. Based on the three-dimensional spatial distribution data, a visual image containing defect boundary contours, depth coordinates, and classification labels is automatically generated.

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