WO2021036662A1 - 信号处理方法、装置及相关产品 - Google Patents

信号处理方法、装置及相关产品 Download PDF

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WO2021036662A1
WO2021036662A1 PCT/CN2020/105591 CN2020105591W WO2021036662A1 WO 2021036662 A1 WO2021036662 A1 WO 2021036662A1 CN 2020105591 W CN2020105591 W CN 2020105591W WO 2021036662 A1 WO2021036662 A1 WO 2021036662A1
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state change
signal
signals
cross
power spectrum
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PCT/CN2020/105591
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English (en)
French (fr)
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高风波
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深圳市豪视智能科技有限公司
深圳市广宁股份有限公司
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Publication of WO2021036662A1 publication Critical patent/WO2021036662A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/40—Scenes; Scene-specific elements in video content
    • G06V20/41—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items
    • G06V20/42—Higher-level, semantic clustering, classification or understanding of video scenes, e.g. detection, labelling or Markovian modelling of sport events or news items of sport video content
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/02—Preprocessing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F2218/00—Aspects of pattern recognition specially adapted for signal processing
    • G06F2218/02—Preprocessing
    • G06F2218/04—Denoising
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
    • Y02D30/00—Reducing energy consumption in communication networks
    • Y02D30/70—Reducing energy consumption in communication networks in wireless communication networks

Definitions

  • This application relates to the field of detection technology, and in particular to a signal processing method, device and related products.
  • vibration can reflect the operating conditions of certain mechanical structures. You can shoot a video of the vibration device when it is running, and then obtain the vibration of the device to be tested from the video. When extracting the vibration signal, you can first extract each pixel in the video. According to the state change signal of the point, the vibration condition of the device to be detected is obtained according to the state change signal. However, when the video is taken, various noise information will be introduced due to the influence of the environment. For example, the light will also cause the vibration of each pixel in the video. When the state changes, the obtained vibration condition of the device to be tested also contains noise information, which makes it impossible to accurately obtain the operating status of the device.
  • the embodiments of the present application provide a signal processing method, device, and related products.
  • an embodiment of the present application provides a signal processing method, including:
  • the analyzing the plurality of state change signals to obtain an aperiodic signal in the plurality of state change signals includes:
  • state change signals with inconsistent frequencies of the corresponding plurality of first state change signal segments are used as the non-periodic signal.
  • the analyzing the plurality of state change signals to obtain an aperiodic signal in the plurality of state change signals includes:
  • the state change signal that cannot pass the corresponding sliding window is used as the non-periodic signal.
  • the acquiring multiple state change signals corresponding to the cross cross power spectrum obtained by performing phase correlation calculation on the frame sequence corresponding to the detected video includes:
  • Inverse Fourier transform is performed on the cross cross power spectrum to obtain multiple state change signals.
  • the cross cross power spectrum obtained by performing phase correlation calculation on the frame sequence corresponding to the detection video is obtained
  • the corresponding multiple state change signals also include:
  • the performing inverse Fourier transform on the cross cross power spectrum to obtain multiple state change signals includes:
  • Inverse Fourier transform is performed on the cross cross power spectrum after filtering processing to obtain multiple state change signals.
  • the filtering strategy includes a filtering bandwidth, and the filtering corresponding to each correlation peak is determined according to the corresponding position of each correlation peak in the cross cross power spectrum in the detection video and the frequency band of the correlation peak.
  • Strategies include:
  • the product of the initial filter bandwidth corresponding to each correlation peak and the bandwidth adjustment coefficient is used as the filter bandwidth corresponding to each correlation peak.
  • an embodiment of the present application also provides a signal processing device, including:
  • a signal acquisition module configured to acquire a plurality of state change signals corresponding to the cross cross power spectrum obtained by performing phase correlation calculation on the frame sequence corresponding to the detected video, and the state change signal is a time domain signal;
  • An analysis module configured to analyze the plurality of state change signals to obtain non-periodic signals among the plurality of state change signals
  • the signal processing module is used to remove aperiodic signals from the plurality of state change signals.
  • an embodiment of the present application also provides an electronic device, including a processor, a memory, and an information recommendation program stored on the memory and executable by the processor, wherein the information recommendation program is When executed by the processor, instructions for implementing the steps in the signal processing method described in any one of the foregoing embodiments.
  • the present application also provides a computer-readable storage medium on which a signal processing program is stored.
  • the signal processing program is executed by a processor, the signal processing described in any of the above is implemented. method.
  • the present application also provides a detection device for detecting the operating status of the device to be detected.
  • the detection device includes the signal processing device of the foregoing embodiment or the electronic device of the foregoing embodiment.
  • the technical solution of the embodiment of the present application obtains multiple state change signals corresponding to the cross cross power spectrum obtained by performing phase correlation calculation on the frame sequence corresponding to the detected video, and the state change signal is a time domain signal; analyzes the multiple state change signals to Obtain the non-periodic signals in the multiple state change signals; remove the aperiodic signals in the multiple state change signals.
  • the state change signal obtained from the detection video has more useful information, which can make the operating status of the device to be detected obtained from the detection video more accurate.
  • FIG. 1 is a schematic diagram of the hardware structure of an electronic device according to an embodiment of the application.
  • FIG. 2 is a schematic flowchart of a signal processing method according to an embodiment of the application
  • FIG. 3 is a schematic diagram of a process of amplifying a detection video involved when the signal processing method of an embodiment of the application is used to detect the operating status of the device to be detected;
  • FIG. 4 is a schematic diagram of another flow of a signal processing method according to an embodiment of the application.
  • FIG. 5 is a schematic flowchart of another signal processing method according to an embodiment of the application.
  • FIG. 6 is a schematic flowchart of still another signal processing method according to an embodiment of the application.
  • FIG. 7 is a schematic diagram of still another flow of the signal processing method according to an embodiment of the application.
  • FIG. 8 is a schematic diagram of still another flow of the signal processing method according to an embodiment of the application.
  • FIG. 1 is a schematic diagram of the hardware structure of an electronic device 100 provided by an embodiment of the present application.
  • the electronic device 100 includes a processor 101, a memory 102, an input/output interface 103, and one or more programs.
  • One or more programs are stored in the memory 102 and configured to be executed by the processor 101.
  • the programs include any of the following Instructions for the steps of the signal processing method of the embodiment.
  • the electronic device 100 may be a server device or a terminal device.
  • the memory 102 may be a high-speed RAM memory or a non-volatile memory (non-volatile memory), such as a disk memory.
  • the memory 102 may optionally be a storage device independent of the aforementioned processor 101.
  • the input/output interface 103 may optionally include a USB interface, a standard wired interface, and a wireless interface (such as a WI-FI interface).
  • FIG. 2 is a schematic flowchart of a signal processing method provided by an embodiment of the present application. This method may include but is not limited to the following steps:
  • the filtering method of the embodiment of the present application can be used but not limited to detecting the vibration of the device to be detected.
  • the vibration of the device to be tested can reflect the operating conditions of the device to be tested.
  • the detection video may be a video obtained by shooting the device to be detected by the imaging module.
  • the detection video can be analyzed to extract the state change information of the device to be detected from the detection video, obtain vibration information according to the state change information, and then analyze the vibration information to obtain the operating status of the device to be tested.
  • the detection video is a vibration magnified video obtained by using an Euler algorithm to amplify vibrations in the video obtained by shooting the imaging module with the device to be detected.
  • Devices to be tested include, but are not limited to, various mechanical equipment, building structures, etc.
  • the phase correlation calculation is performed on the frame sequence corresponding to the detection video to obtain the cross cross power spectrum, and the cross cross power spectrum includes one or more correlation peaks.
  • the cross cross power spectrum obtained in this way is a frequency domain signal, and the state change signal corresponding to each correlation peak can be obtained after the cross cross power spectrum is subjected to inverse Fourier transform.
  • Each state change signal can reflect the state change of a certain position in the detection video, and the phase correlation calculation of the frame sequence corresponding to the detection video can be understood as the cross power spectrum can be understood as extracting the state in the video picture from the detection video Change information.
  • the state change information includes vibration information and other noise information. For example, changes in illumination can also cause state changes in the video screen, and the vibration information can reflect the operating conditions of the device to be tested.
  • the state change signal corresponding to each correlation peak in the cross cross power spectrum can be processed through the filtering method of the embodiment of this application, so as to make the operating status of the device to be tested obtained from the detection video more accurate .
  • the vibration of the device to be detected is a periodic reciprocating motion, and the state change caused by the vibration is also periodic.
  • the state change caused by the noise is often not periodic, for example, in an outdoor scene, the state change caused by the light and dark changes of the outdoor optical fiber.
  • periodic vibrations can be used to reflect the operating conditions of the device to be tested, because non-periodic vibrations are often caused by the external environment, rather than by the external environment.
  • the non-periodic signal caused by the detection device itself cannot be used to analyze the operating status of the device to be detected. Then, noise signals that are not caused by self-vibration can be obtained by obtaining aperiodic signals in the state change signals.
  • this part of the aperiodic signals can be removed to Makes the state change signal obtained from the detection video more useful information.
  • the signal processing method of the embodiment of the present application obtains multiple state change signals corresponding to the cross cross power spectrum obtained by performing phase correlation calculation on the frame sequence corresponding to the detected video, and the state change signal is a time domain signal; analyzes the multiple state change signals, In order to obtain the non-periodic signals in the multiple state change signals; remove the non-periodic signals in the multiple state change signals.
  • the state change signal obtained from the detection video has more useful information, which can make the operating status of the device to be detected obtained from the detection video more accurate.
  • the vibration information can be reflected by state change information.
  • the frame sequence in the detection video can be converted from the RGB color space to the YIQ color space, and the brightness information and chroma information of the video frames can be separated.
  • the conversion relationship between RGB and YIQ is:
  • the Euler motion amplification algorithm to amplify the video data line. Specifically, it includes: firstly decompose the Y-channel image after FFT transformation in the spatial domain of complex manipulable gold towers to obtain a pyramidal structure composed of multiple sub-images with different spatial resolutions; for each of the multiple sub-images in the pyramid-shaped structure The image is processed by time-domain band-pass filtering to obtain the transformed signal corresponding to the target frequency band. It can be understood that in the video picture, the vibration can be reflected by the brightness of the video frame sequence, then the vibration information in the detection video can be obtained by analyzing the Y channel in the detection video.
  • phase correlation algorithm is used to calculate the cross power spectrum between the frame sequences on the frame sequence after the video motion amplification processing.
  • the phase correlation algorithm uses the following formula to calculate the cross cross power spectrum.
  • Fa is the Fourier transform of a frame image
  • the cross cross power spectrum obtained in this way is a frequency domain signal
  • the cross cross power spectrum is subjected to inverse Fourier transform and phase-by-phase comparison to obtain multiple state change signals, which can reflect the state change information of various locations in the detected video.
  • the multiple state change signals are processed to remove the non-periodic signals, and the signal-to-noise ratio of the useful signal is improved, so that the detection video that contains the image during the operation of the detection device is extracted Information, useful signals have a higher signal-to-noise ratio.
  • a pyramid structure is obtained.
  • the pyramid structure includes multiple layers of sub-images, and the image resolution from top to bottom is sequentially reduced, and the spatial frequency is sequentially reduced.
  • the time-domain band-pass filtering of each layer of image is to obtain the target frequency band, so that the sub-image resolution of the target frequency band can clearly express the motion characteristics of the image, and at the same time, it will not cause too much calculation due to the high resolution. Therefore, according to the pyramid structure, the frequency bands of each sub-image at different spatial frequencies are obtained from the bottom to the top, and the acquisition sequence is compared with the standard frequency band.
  • the frequency band acquisition and matching of the spatial frequency of one or more sub-images above the sub-image of this layer improves the efficiency of time-domain band-pass filtering.
  • the maximum number of pyramid decomposition levels is determined: log 2 (min(xres,yres)), where xres is the width pixel value of the image, and yres is the height pixel value of the image.
  • first group includes sub-images corresponding to 2n+1 layers
  • second group includes sub-images corresponding to 2(n+1) layers, where n is an integer greater than or equal to 0 , N ⁇ max(2(n+1),2n+1);
  • the first group of sub-images are processed by the first processor to perform temporal band-pass filtering according to the number of layers from small to large
  • the second group of sub-images are processed by the second processor to perform temporal band-pass filtering according to the number of layers from small to large.
  • the time-domain band-pass filtering process is to match the frequency bands of different spatial frequencies corresponding to the sub-images with the standard frequency band, and the first processor and the second processor are independently operating processors;
  • the first processor or the second processor determines that the frequency band matches the standard frequency band successfully, determining that the frequency band is the target frequency band;
  • the first target sub-image and the second target sub-image are transformed signals corresponding to the target frequency band.
  • the base array uses the first processor to perform time-domain band-pass filtering in the order of 1,3,5...2n+1, and the even array uses the second processor. Perform the time-domain band-pass filter processing in the order of 2,4,6,...2(n+1).
  • the two processors can start running at the same time or have a certain processing time interval.
  • the obtaining method may be to match the frequency bands of different spatial frequencies corresponding to the sub-images with the standard frequency bands.
  • the first processor or the second processor determines that the frequency band is successfully matched with the standard frequency band, it is determined that the frequency band is the target frequency band.
  • the target frequency band represents the spatial frequency of the lowest resolution sub-image that can reflect the vibration information of the image
  • the sub-image corresponding to the target frequency band is determined as the first target sub-image, and then the sub-image corresponding to the target frequency band in the upper layer of the sub-image is obtained as the second
  • the target sub-image, the first target sub-image and the second target sub-image are transformed signals corresponding to the target frequency band, and subsequent amplification and transformation are performed.
  • the frame image corresponding to the target vibration video can be enlarged more accurately, and more accurate motion information can be obtained, and at the same time, the amount of calculation that needs to be increased for amplifying a higher resolution image is avoided.
  • the sub-images corresponding to the pyramid structure are grouped, and then the sub-images of different groups are subjected to time-domain band-pass filtering through two independently operating processors, which can improve the efficiency of filtering processing, and at the same time obtain
  • the sub-image of the layer corresponding to the target frequency band is used as the first target sub-image
  • the sub-image of the upper layer is obtained as the second target sub-image
  • the first target sub-image and the second target sub-image are transformed signals corresponding to the target frequency band
  • step 02 includes but is not limited to the following steps:
  • the state change signal is a time-domain signal, and the first state change signal segments of different periods can be extracted from each state change signal, and then the frequency of each first state change signal segment can be obtained through a mathematical method. Extracting multiple first state change signal segments from each state change signal can be understood as extracting at least two first state change signal segments from each state change signal, and then obtain the frequency of each first state change signal segment .
  • the number can be 2, 3, 4, or any other number greater than 4.
  • the state change signals with inconsistent frequencies of the corresponding plurality of first state change signal segments are used as non-periodic signals.
  • the frequencies of multiple first state change signal segments corresponding to one state change signal can be compared to determine whether the frequencies of the multiple first state change signal segments are consistent. If the frequencies of multiple first state change signal segments corresponding to a state change signal are inconsistent, for example, four first state change signal segments are extracted from one state change signal, and among these four first state change signal segments, There are two first state change signal segments with a frequency of a, and two first state change signal segments with a frequency of b. Then the frequency of the first state change signal segment corresponding to the state change signal segment is inconsistent, indicating the state change If the signal has at least two frequency bands with different frequency values, the state change signal can be used as a non-periodic signal.
  • step 02 includes but is not limited to the following steps:
  • each state change signal When judging whether each state change signal is a periodic signal, first extract the target state change signal segment of a preset length, obtain the target frequency of the target change signal segment, and then compare the frequencies of other parts in the state change signal with the target frequency. If the frequency of other parts of the state change signal is inconsistent with the target frequency, the state change signal can be regarded as a non-periodic signal.
  • the preset length can be set by a user to a certain value, or it can be self-adapted according to the length of the signal during signal processing. For example, the preset length can be set to 1/10 of the length of the state change signal.
  • the window size of the sliding window can be set according to the target frequency.
  • the window size of the sliding window can be set to be consistent with the target frequency, so that only signals with the same frequency as the target frequency can pass through the sliding window. However, signals that are inconsistent with the target frequency cannot pass through the sliding window.
  • the state change signal cannot pass through the corresponding sliding window, it means that there is a signal segment whose frequency is inconsistent with the target frequency in the state change signal, that is, the state change signal is a non-periodic signal.
  • a sliding window is used to determine whether the frequencies of other parts of the state change signal are consistent with the target frequency, so that conclusions can be obtained quickly and conveniently, and the amount of calculation is smaller.
  • step 01 includes:
  • the frequency domain signal is converted into a time domain signal, which is convenient for identifying non-periodic signals and removing non-periodic signals, which improves the signal-to-noise ratio of useful signals, so that it can be extracted from the detection video containing the image of the detection device when it is running.
  • the information output, the signal-to-noise ratio of the useful signal is higher.
  • step 01 also includes:
  • Step 013 is executed after step 011 and before step 012.
  • Each correlation peak can be analyzed to obtain the corresponding position of each correlation peak in the detection video, and the corresponding position of the correlation peak in the detection video can be understood as the position in the detection video corresponding to the state change reflected by the correlation peak.
  • the brightness of a location in the detection video changes periodically with time, and one or more correlation peaks obtained by performing phase correlation calculations on the corresponding frame sequence of the detection video may include at least one correlation peak. The brightness of the location changes.
  • each correlation peak in the detection video can be used as a basis.
  • Determine the filtering strategy for each correlation peak The frequency range (ie, frequency band) corresponding to each correlation peak is different, and the filtering strategy of each correlation peak is determined according to the frequency band corresponding to the correlation peak, so that the filtering strategy is more adapted to each correlation peak, thereby helping to determine the filtering strategy from each correlation peak.
  • the filtering strategy may include, but is not limited to, the bandwidth of the filter, the type of the filter, and the like, for example.
  • each correlation peak After obtaining the filtering strategy of each correlation peak, filter processing is performed on each correlation peak according to the corresponding filtering strategy, so that the signal-to-noise ratio of the signal obtained from the detection video is higher.
  • each correlation peak can be filtered by interpolation filtering according to the filtering strategy corresponding to each correlation peak, so that the filtering effect is better.
  • Step 012 includes:
  • the cross cross power spectrum is filtered first, which can make the subsequent process of identifying aperiodic signals less computationally expensive.
  • a de-drying is performed first to reduce the noise information in each correlation peak, and further make the detection video
  • the signal-to-noise ratio of the state change signal obtained in the test video is higher, so that the operating status of the device to be tested obtained from the test video is more accurate.
  • the principal component decomposition method can also be used to determine the filtering strategy for each correlation peak.
  • Correlation peaks are subjected to dimensionality reduction processing, so in step 013, the correlation peaks after the dimensionality reduction processing are calculated to obtain the filtering strategy corresponding to each correlation peak, which can reduce the calculation amount of the filtering process and improve the signal processing efficiency.
  • Principal component decomposition is used to reduce the dimensionality of the correlation peak data in the two main directions of vibration detection.
  • the correlation peaks are screened, the correlation peaks whose peaks are not within the preset range are removed, and the correlation peaks whose peaks are within the preset peak range are reduced in dimensionality. Processing and filtering the correlation peaks that are not within the preset peak range.
  • the signal-to-noise ratio of the state change signal obtained from the detection video can be improved, and there are more useful signals.
  • the removal of part of the noise signal it can Reduce the amount of subsequent filtration calculations and improve filtration efficiency.
  • the filtering strategy includes filtering bandwidth
  • step 013 includes:
  • 0131 Determine the initial filter bandwidth corresponding to each correlation peak according to the frequency band corresponding to each correlation peak in the cross cross power spectrum;
  • the correlation peaks obtained by performing phase correlation calculation on the frame sequence corresponding to the detection video include multiple similar state change information of multiple pixels in similar positions in the screen corresponding to the detection video, so that multiple pixels can be reflected. The status of the location of the point changes. Then, in the screen corresponding to the detection video, the state changes at different positions are different, and the frequency distribution of the corresponding correlation peaks is also different. If the same filter bandwidth is used for filtering, then in order to avoid useful signals being filtered, the filter bandwidth needs to be set to a larger value, but this will cause the noise signal to be unable to be filtered.
  • the initial filter bandwidth corresponding to each correlation peak is determined according to the frequency band corresponding to each correlation peak, and the frequency band corresponding to the correlation peak is positively correlated with the initial filter bandwidth.
  • the larger the frequency band corresponding to the correlation peak, the corresponding initial filter bandwidth The wider it is, the better the filter bandwidth can be adapted to the correlation peaks, which can effectively filter noise signals and also help prevent useful signals from being filtered, thereby making the filtering effect better.
  • B*(1-a) can be used as the initial filter bandwidth
  • B is the frequency band corresponding to each correlation peak
  • a can be determined according to actual needs.
  • a can be set to be larger
  • more useful signals need to be obtained a can be set to a smaller value.
  • the setting method of the initial filter bandwidth is not limited to the above example, and is not limited here.
  • 0132 Determine the bandwidth adjustment coefficient corresponding to the correlation peak according to the corresponding position of each correlation peak in the detection video;
  • Vibration at different locations in the detection video has different effects on detecting the operating status of the device to be detected. For example, compared with the vibration at the center of the screen, the vibration at the edge of the screen has a smaller effect on detecting the operating status of the device to be detected. Then, the bandwidth adjustment coefficient corresponding to the correlation peak can be determined according to the corresponding position of each correlation peak in the detection video. The bandwidth adjustment coefficient is used to adjust the initial filter bandwidth to make the filter bandwidth more reasonable.
  • the screen corresponding to the detection video can be divided into multiple areas according to the impact of the vibration of the corresponding position of the correlation peak in the detection video on the operating status of the device to be detected, and a preset bandwidth can be preset for each area Adjustment coefficient, so that when the bandwidth adjustment coefficient is determined in step 022, the preset bandwidth adjustment coefficient corresponding to the area where the corresponding position of each correlation peak is located in the detection video can be directly obtained, and the preset bandwidth adjustment coefficient is used as the correlation peak corresponding Bandwidth adjustment factor. In this way, the bandwidth adjustment coefficient can be obtained quickly and accurately.
  • the user can set bandwidth weighting coefficients for each area in the screen corresponding to the detection video according to the structure of the device to be detected, and then use the product of the preset bandwidth adjustment coefficient and the bandwidth weighting coefficient as the relevant bandwidth adjustment of the corresponding area Coefficient, which makes the filtering bandwidth of each correlation peak more reasonable. For example, if the vibration of some key parts of the device to be tested is more able to reflect the operating conditions of the device to be tested than other parts, then the bandwidth weighting coefficient of the corresponding area of the key part in the detection video screen can be set to a larger value. , Making the filtering bandwidth larger, so that more useful information can be extracted.
  • the filter bandwidth obtained in this way comprehensively considers the corresponding position of the correlation peak in the detection video and the frequency band corresponding to the correlation peak, so that the filter bandwidth is more adapted to each correlation peak, thereby helping to extract useful information from the cross cross power spectrum.
  • the state change signal The filter bandwidth obtained in this way comprehensively considers the corresponding position of the correlation peak in the detection video and the frequency band corresponding to the correlation peak, so that the filter bandwidth is more adapted to each correlation peak, thereby helping to extract useful information from the cross cross power spectrum.
  • the calculation method of the filter bandwidth is not limited to the above method. In other embodiments, a suitable calculation method may be selected according to the frequency band corresponding to each correlation peak and the corresponding position of each correlation peak in the detection video to determine the filter bandwidth.
  • the embodiment of the present application also provides a signal processing device, including:
  • the signal acquisition module is used to acquire multiple state change signals corresponding to the crossed cross power spectrum obtained by performing phase correlation calculation on the frame sequence corresponding to the detected video, and the state change signal is a time domain signal;
  • Analysis module used to analyze multiple state change signals to obtain non-periodic signals among multiple state change signals
  • the signal processing module is used to remove non-periodic signals from multiple state change signals.
  • the signal processing device of the embodiment of the present application obtains multiple state change signals corresponding to the cross cross power spectrum obtained by performing phase correlation calculation on the frame sequence corresponding to the detected video, and the state change signal is a time domain signal; analyzes the multiple state change signals, In order to obtain the non-periodic signals in the multiple state change signals; remove the non-periodic signals in the multiple state change signals.
  • the state change signal obtained from the detection video has more useful information, which can make the operating status of the device to be detected obtained from the detection video more accurate.
  • the analysis module includes:
  • the first signal extraction unit is configured to extract a plurality of first state change signal segments from each state change signal, and obtain the frequency of each first state change signal segment;
  • the first execution unit is configured to use, among the plurality of state change signals, corresponding state change signals with inconsistent frequencies of the plurality of first state change signal segments as non-periodic signals.
  • the analysis module includes:
  • the second signal extraction unit is used to extract a predetermined length of the target state change signal segment from each state change signal, and obtain the target frequency of the target state change signal segment;
  • the signal sending unit is used to set the corresponding sliding window according to the target frequency corresponding to each state change signal, and send each state change signal to the corresponding sliding window;
  • the second execution unit is configured to use the state change signal that cannot pass through the corresponding sliding window as a non-periodic signal.
  • the signal acquisition module includes:
  • the signal acquisition unit is used to acquire the cross-cross power spectrum obtained by performing phase correlation calculation on the frame sequence corresponding to the detected video, and the cross-cross power spectrum is a frequency domain signal;
  • the signal conversion unit is used to perform inverse Fourier transform on the cross cross power spectrum to obtain multiple state change signals.
  • the signal acquisition module further includes:
  • a filtering strategy determination unit configured to determine the filtering strategy corresponding to each correlation peak according to the corresponding position of each correlation peak in the cross cross power spectrum in the detection video and the frequency band of the correlation peak;
  • the filtering unit is configured to perform filtering processing on each correlation peak in the cross cross power spectrum according to the filtering strategy corresponding to each correlation peak;
  • the signal conversion unit is also used for:
  • Inverse Fourier transform is performed on the cross cross power spectrum after filtering processing to obtain multiple state change signals.
  • the filtering strategy includes a filtering bandwidth
  • the filtering strategy determination unit includes:
  • the initial filter bandwidth determination subunit is used to determine the initial filter bandwidth corresponding to each correlation peak according to the frequency band corresponding to each correlation peak in the cross cross power spectrum;
  • the bandwidth adjustment coefficient determination subunit is used to determine the bandwidth adjustment coefficient corresponding to the correlation peak according to the corresponding position of each correlation peak in the detection video;
  • the filter bandwidth determining subunit is used to use the product of the initial filter bandwidth corresponding to each correlation peak and the bandwidth adjustment coefficient as the filter bandwidth corresponding to each correlation peak.
  • each module in the above-mentioned signal processing device corresponds to the steps in the above-mentioned signal processing method embodiment, and the functions and realization processes thereof will not be repeated here.
  • the present application also provides a computer-readable storage medium with a signal processing program stored on the computer-readable storage medium, where the signal processing program implements the steps of the signal processing method in any of the above embodiments when the signal processing program is executed by the processor.
  • An embodiment of the present application also provides a detection device for detecting the operating status of the device to be detected, and the detection device includes the signal processing device of the foregoing embodiment or the electronic device of the foregoing embodiment.
  • the detection device of the embodiment of the present application obtains multiple state change signals corresponding to the cross cross power spectrum obtained by performing phase correlation calculation on the frame sequence corresponding to the detection video, and the state change signal is a time domain signal; analyzes the multiple state change signals to Obtain the non-periodic signals in the multiple state change signals; remove the non-periodic signals in the multiple state change signals.
  • the state change signal obtained from the detection video has more useful information, which can make the operating status of the device to be detected obtained from the detection video more accurate.
  • the vibration information can be reflected by state change information.
  • the frame sequence in the detection video can be converted from the RGB color space to the YIQ color space, and the brightness information and chroma information of the video frames can be separated.
  • the conversion relationship between RGB and YIQ is:
  • the Euler motion amplification algorithm to amplify the video data line. Specifically, it includes: firstly decompose the Y-channel image after FFT transformation in the complex and manipulable gold tower spatial domain, and apply the time-domain band-pass filtering to the images of different scales after the Y-channel spatial domain decomposition. The brightness of the frame sequence is reflected, then the vibration information in the detection video can be obtained by analyzing the Y channel in the detection video.
  • phase correlation algorithm is used to calculate the cross power spectrum between the frame sequences on the frame sequence after the video motion amplification processing.
  • the phase correlation algorithm uses the following formula to calculate the cross cross power spectrum.
  • Fa is the Fourier transform of a frame image
  • the cross cross power spectrum obtained in this way is a frequency domain signal
  • the cross cross power spectrum is subjected to inverse Fourier transform and phase-by-phase comparison to obtain multiple state change signals, which can reflect the state change information of various locations in the detected video.
  • the multiple state change signals are processed to remove the non-periodic signals, and the signal-to-noise ratio of the useful signal is improved, so that the detection video that contains the image during the operation of the detection device is extracted Information, useful signals have a higher signal-to-noise ratio.
  • the computer program product includes one or more computer instructions.
  • the computer may be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices.
  • the computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions may be transmitted from a website, computer, server, or data center.
  • the computer-readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server or a data center integrated with one or more available media.
  • the usable medium may be a magnetic medium (such as a floppy disk, a hard disk, and a magnetic tape), an optical medium (such as an optical disk), or a semiconductor medium (such as a solid-state hard disk).
  • the disclosed device may also be implemented in other ways.
  • the device embodiments described above are only illustrative.
  • the division of the units is only a logical function division, and there may be other divisions in actual implementation, for example, multiple units or components can be combined or integrated. To another system, or some features can be ignored or not implemented.
  • the displayed or discussed indirect coupling or direct coupling or communication connection between each other may be through some interfaces, indirect coupling or communication connection between devices or units, and may be in electrical or other forms.
  • the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed to multiple network units. . Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
  • the functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may exist alone physically, or two or more units may be integrated into one unit.
  • the above-mentioned integrated unit may be implemented in the form of hardware, or may also be implemented in the form of a software functional unit.
  • the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer readable storage medium.
  • the technical solution of this application essentially or the part that contributes to the existing technology or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium
  • a number of instructions are included to enable a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present application.
  • the aforementioned storage medium may include, for example: U disk, mobile hard disk, Read-Only Memory (ROM), Random Access Memory (RAM, Random Access Memory), magnetic disks or optical disks and other storable program codes. Medium.

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Abstract

本申请公开了一种信号处理方法、装置及相关产品。信号处理方法包括获取对检测视频对应的帧序列进行相位相关计算得到的交叉互功率谱对应的多个状态变化信号，所述状态变化信号为时域信号；分析所述多个状态变化信号，以获取所述多个状态变化信号中的非周期信号；去除所述多个状态变化信号中的非周期信号。这样从检测视频中获取到的状态变化信号中，有用信息更多，从而可以使得根据检测视频得到的待检测装置的运行状况更加准确。

Description

信号处理方法、装置及相关产品 技术领域
本申请涉及检测技术领域，尤其涉及了一种信号处理方法、装置及相关产品。
背景技术
在相关技术中，振动可以反应某些机械结构的运行状况，可以拍摄振动装置运行时的视频，然后从视频中获取待检测装置的振动情况，提取振动信号时，可通过先提取视频中各个像素点的状态变化信号，再根据状态变化信号得到待检测装置的振动情况，但是由于拍摄视频时，会受环境影响而引入各种各样的噪声信息，例如光照也会导致视频中各个像素点的状态变化，得到的待检测装置的振动情况也包含噪声信息，这样导致无法准确地获得装置的运行状况。
发明内容
本申请实施例提供一种信号处理方法、装置及相关产品。
第一方面，本申请实施例提供了一种信号处理方法，包括：
获取对检测视频对应的帧序列进行相位相关计算得到的交叉互功率谱对应的多个状态变化信号，所述状态变化信号为时域信号；
分析所述多个状态变化信号，以获取所述多个状态变化信号中的非周期信号；
去除所述多个状态变化信号中的非周期信号。
在某些实施例中，所述分析所述多个状态变化信号，以获取所述多个状态变化信号中的非周期信号包括：
从第一状态变化信号段，并获取各第一状态变化信号段的频率；
将所述多个状态变化信号中，对应的多个第一状态变化信号段的频率不一致的状态变化信号作为所述非周期信号。
在某些实施例中，所述分析所述多个状态变化信号，以获取所述多个状态变化信号中的非周期信号包括：
从各状态变化信号中提取预设长度的目标状态变化信号段,并获取所述目标状态变化信号段的目标频率；
根据各状态变化信号对应的目标频率设定对应的滑窗,并将各状态变化信号发送至对应的滑窗；
将不能通过对应的滑窗的状态变化信号作为所述非周期信号。
在某些实施例中，所述获取对检测视频对应的帧序列进行相位相关计算得到的交叉互功率谱对应的多个状态变化信号包括：
获取对检测视频对应的帧序列进行相位相关计算得到的交叉互功率谱，所述交叉互功率谱为频域信号；
对所述交叉互功率谱进行反傅里叶变换得到多个状态变化信号。
在某些实施例中，所述对所述交叉互功率谱进行反傅里叶变换得到多个状态变化信号之前，所述获取对检测视频对应的帧序列进行相位相关计算得到的交叉互功率谱对应的多个状态变化信号还包括：
根据所述交叉互功率谱中的各相关峰在所述检测视频中对应的位置及相关峰的频段确定各相关峰对应的滤波策略；
按照各相关峰对应的滤波策略对所述交叉互功率谱中的各相关峰进行滤波处理；
所述对所述交叉互功率谱进行反傅里叶变换得到多个状态变化信号包括：
对进行滤波处理之后的交叉互功率谱进行反傅里叶变换得到多个状态变化信号。
在某些实施例中，所述滤波策略包括滤波带宽，所述根据所述交叉互功率谱中的各相关峰在所述检测视频中对应的位置及相关峰的频段确定各相关峰对应的滤波策略包括：
根据所述交叉互功率谱中的各相关峰对应的频段确定各相关峰对应的初始滤波带宽；
根据各相关峰在所述检测视频中对应的位置确定所述相关峰对应的带宽调整系数；
将各相关峰对应的初始滤波带宽和带宽调整系数的乘积作为各相关峰对应的滤波带宽。
第二方面，本申请实施例还提供一种信号处理装置，包括：
信号获取模块，用于获取对检测视频对应的帧序列进行相位相关计算得到的交叉互功率谱对应的多个状态变化信号，所述状态变化信号为时域信号；
分析模块，用于分析所述多个状态变化信号，以获取所述多个状态变化信号中的非周期信号；
信号处理模块，用于去除所述多个状态变化信号中的非周期信号。
第三方面，本申请实施例还提供一种电子装置，包括处理器、存储器、以及存储在所述存储器上并可被所述处理器执行的信息推荐程序，其中所述信息推荐程序被所述处理器执行时，实现上述任一项实施例所述的信号处理方法中的步骤的指令。
第四方面，本申请还提供一种计算机可读存储介质，计算机可读存储介质上存储有信号处理程序，其中所述信号处理程序被处理器执行时，实现上述任一项所述的信号处理方法。
第五方面，本申请还提供一种检测设备，用于检测待检测装置的运行状况，所述检测设备包括上述实施例的信号处理装置或上述实施例的电子装置。
本申请实施例的技术方案，获取对检测视频对应的帧序列进行相位相关计算得到的交叉互功率谱对应的多个状态变化信号，状态变化信号为时域信号；分析多个状态变化信号，以获取多个状态变化信号中的非周期信号；去除多个状态变化信号中的非周期信号。这样从检测视频中获取到的状态变化信号中，有用信息更多，从而可以使得根据检测视频得到的待检测装置的运行状况更加准确。
附图说明
下面将对本申请实施例涉及的一些附图进行说明。
图1为本申请实施例的电子装置的硬件结构示意图；
图2为本申请实施例的信号处理方法的流程示意图；
图3为本申请实施例的信号处理方法用于检测待检测装置的运行状况时涉及的放大检测视频的过程示意图；
图4为本申请实施例的信号处理方法的另一流程示意图；
图5为本申请实施例的信号处理方法的又一流程示意图；
图6为本申请实施例的信号处理方法的再一流程示意图；
图7为本申请实施例的信号处理方法的再另一流程示意图；
图8为本申请实施例的信号处理方法的再又一流程示意图。
具体实施方式
下面结合本申请实施例中的附图对本申请实施例进行描述。
请参阅图1，图1是本申请实施例提供的电子装置100的硬件结构示意图。电子装置100包括处理器101、存储器102、输入输出接口103，以及一个或多个程序，一个或多个程序被存储在存储器102中，并且被配置由处理器101执行，程序包括用以下任一实施例的信号处理方法的步骤的指令。电子装置100可以是服务器装置，也可以是终端装置。存储器102可以是高速RAM存储器，也可以是稳定的存储器(non-volatile memory)，例如磁盘存储器，存储器102可选的还可以是独立于前述处理器101的存储装置。输入输出接口103可选的可以包括USB接口、标准的有线接口、无线接口(如WI-FI接口)。
请参阅图2，图2是本申请实施例提供的一种信号处理方法的流程示意图，这种方法可包括但不限于如下步骤：
01、获取对检测视频对应的帧序列进行相位相关计算得到的交叉互功率谱对应的多个状态变化信号，状态变化信号为时域信号；
本申请实施例的滤波方法可用于但不限于检测待检测装置的振动情况。待检测装置的振动情况可以反应待检测装置的运行状况。检测视频可为由成像模组拍摄待检测装置得到的视频。可通过分析检测视频，从检测视频中提取出待检测装置的状态变化信息，根据状态变化信息得到振动信息，然后分析该振动信息得到待检测装置的运行状况。
在一个优选实施例中，检测视频为将成像模组拍摄待检测装置得到的视频采用欧拉算法对视频中的振动进行放大之后的振动放大视频。待检测装置包括但不限于各类机械设备、建筑结构等。得到检测视频之后，对检测视频对应的帧序列进行相位相关计算得到交叉互功率谱，交叉互功率谱中包括一个或多个相关峰。这样得到的交叉互功率谱为频域信号，将交叉互功率谱进行反傅里叶变换之后即可得到各相关峰对应的状态变化信号。每个状态变化信号可以反应检测视频中的某个位置的状态变化情况，对检测视频对应的帧序列进行相位相关计算得到交叉互功率谱可以理解为，从检测视频中提取出视频画面中的状态变化信息。状态变化信息包括振动信息和其他噪声信息，例如光照的变化也会导致视频画面中的状态变化，振动信息可以反应待检测装置的运行状况。本申请实施例中，通可通过本申请实施例的滤波方法，对交叉互功率谱中的各相关峰对应的状态变化信号进行处理，以使得根据检测视频得到的待检测装置的运行状况更加准确。
02、分析多个状态变化信号，以获取多个状态变化信号中的非周期信号；
可以理解，待检测装置的振动是一种周期往复运动，振动引起的状态变化也是周期性的。很多噪声信息虽然会导致检测视频中各像素点的状态变化，但是，噪声引起的状态变化常常不是周期性的，例如在室外场景下，由于室外光纤的明暗变化导致的状态变化。而且根据待检测装置的振动分析待检测装置的运行状况时，呈周期性的振动才能用于反应待检测装置的运行状况，因为非周期性的振动常常是由外界环境导致的，而不是由待检测装置自身引起的，那这部分非周期性的信号也不能用于分析待检测装置的运行状况。那么可通过获取状态变化信号中的非周期信号获取不是由自身振动引起的噪声信号。
03、去除多个状态变化信号中的非周期信号。
获得多个状态变化信号中的非周期信号之后，由于非周期信号常常是对分析待检测装置的运行状况作用不大或者没有作用甚至会有干扰的信号，那么可去除这部分非周期信号，以使得从检测视频中获取到的状态变化信号中，有用信息更多。
本申请实施例的信号处理方法，获取对检测视频对应的帧序列进行相位相关计算得到的交叉互功率谱对应的多个状态变化信号，状态变化信号为时域信号；分析多个状态变化信号，以获取多个状态变化信号中的非周期信号；去除多个状态变化信号中的非周期信号。这样从检测视频中获取到的状态变化信号中，有用信息更多，从而可以使得根据检测视频得到的待检测装置的运行状况更加准确。
检测待检测装置的运行状况时，需要从包含有检测装置运行时的画面的检测视频中提取振动信息，在检测视频中，振动信息可由状态变化信息体现。如图3所示，可先将检测视频中的帧序列由RGB颜色空间转换到YIQ颜色空间，分离视频帧的亮度信息和色度信息。RGB和YIQ的转换关系为：
Y＝0.299*R+0.587*G+0.114*B；
I＝0.596*R–0.275*G–0.321*B；
Q＝0.212*R-0.523*G+0.311*B。
然后保持I、Q通道不变，对Y通道进行FFT操作，再利用欧拉运动放大算法对视频数据行放大处理。具体包括：先将FFT变换后的Y通道图像进行复数可操纵金子塔空域分解，得到由多个不同空间分辨率的子图像组成的金字塔形结构；对金字塔型结构中的多个子图像中每个子图像进行时域带通滤波处理，得到目标频带对应的变换信号。可以理解，在视频画面中，振动可由视频帧序列的亮度反映，那么可通过分析检测视频中的Y通道获取检测视频中的振动信息。然后放大时域带通滤波后得到的目标频带对应的变换信号并提取感兴趣的运动信息，对感兴趣的运动信息进行复数可操纵金字塔重建，得到放大后的Y通道图像；最后将重建的Y通道图像与原来的I、Q通道图像相加，再转化为RGB色彩空间，得到输出视频。
再对视频运动放大处理后的帧序列采用相位相关算法计算帧序列间的交叉互功率谱。相位相关算法采用如下的公式计算交叉互功率谱。
Figure PCTCN2020105591-appb-000001
上式中，Fa为a帧图像的傅立叶变换，
Figure PCTCN2020105591-appb-000002
为b帧图像的傅里叶变换的共轭信号，
Figure PCTCN2020105591-appb-000003
为两个傅里叶变换的信号的相关积的模。R为本步骤的计算结果交叉互功率谱(包含频域噪音)。这样得到的交叉互功率谱为频域信号，将交叉互功率谱进行反傅立叶变换，逐相位比较，便可得到多个状态变化信号，可以反应检测视频中各处的状态变化信息。
最后按照本申请实施例的上述滤波方法对多个状态变化信号进行处理，去除非周期信号，提高了有用信号的信噪比，使得从包含有检测装置运行时的画面的检测视频中提取出的信息，有用信号的信噪比更高。
具体地，对目标振动视频的图像帧序列进行金字塔分解后，获得金字塔型结构。金字塔型结构包括多层子图像，且从上到下的图像分辨率依次降低，空间频率依次降低。对每层图像进行时域带通滤波处理，是为了得到目标频带，使得目标频带的子图像分辨率能够清晰表达图像的运动特征，同时不会因为分辨率过高造成计算量过大。因此，按照金字塔结构从下到上获取每个子图像在不同空间频率的频带，并且按照获取顺序与标准频带进行对比，当确定某一层子图像的频带与标准频带匹配成功时，就不需要再对该层子图像之上的一层或多层子图像进行空间频率的频带获取和匹配，提升了时域带通滤波的效率。另外，金字塔分解的最大层数确定：log 2(min(xres,yres))，其中xres为图像的宽度像素值，yres为图像的高度像素值。
进一步地，对金字塔型结构中的多个子图像中每个子图像进行时域带通滤波处理，得到目标频带对应的变换信号，具体包括：
获取金字塔结构的总层数N，其中金字塔结构由下到上层数依次增大，对应的子图像分辨率依次升高；
对金字塔结构中的多个子图像进行分组，其中第一组包括2n+1层对应的子图像，第二组包括2(n+1)层对应的子图像，其中n为大于或等于0的整数，N≥max(2(n+1),2n+1)；
将第一组子图像采用第一处理器按照层数从小到大进行时域带通滤波处理，将第二组子图像采用第二处理器按照层数从小到大进行时域带通滤波处理，时域带通滤波处理为将子图像对应的不同空间频率的频带与标准频带进行匹配，第一处理器和第二处理器为独立运行的处理器；
当第一处理器或第二处理器确定频带与标准频带匹配成功时，确定频带为目标频带；
获取目标频带对应的子图像为第一目标子图像，获取第一目标子图像上面一层子图像为第二目标子图像；
确定第一目标子图像和第二目标子图像为目标频带对应的变换信号。
具体地，在对目标振动视频的多帧图像组成的帧序列进行空域金字塔分解后，可以获得金字塔结构的总层数N，且金字塔结构从下到上1～N层对应子图像的分辨率依次升高。然后对N层子图像进行分组，包括基数组和偶数组，基数组采用第一处理器按照1,3,5…2n+1的顺序进行时域带通滤波处理，偶数组采用第二处理器按照2,4,6,…2(n+1)的顺序进行时域带通滤波处理，两个处理器可以同时开始运行，也可以具有一定的处理时间间隔，另外，时域带通滤波处理是为了获取目标频带，获取方法可以是将子图像对应的不同空间频率的频带与标准频带进行匹配。当第一处理器或第二处理器确定频带与标准频带匹配成功时，确定所述频带为目标频带。
因为目标频带表示能反应图像振动信息的最低分辨率子图像的空间频率，那么确定目 标频带对应的子图像为第一目标子图像，然后获取目标频带对应子图像上一层的子图像作为第二目标子图像，将第一目标子图像和第二目标子图像为目标频带对应的变换信号，进行后续放大变换。这样，可以更准确地对目标振动视频对应的帧图像进行放大，获得更准确的运动信息，同时也避免了对更高分辨率图像进行放大的需要增加的运算量。
在本申请实施例中，对金字塔结构对应的子图像进行分组，然后通过两个独立运行的处理器对不同组的子图像进行时域带通滤波处理，可以提升滤波处理效率，同时在获取到目标频带对应层的子图像作为第一目标子图像后，获取其上一层的子图像作为第二目标子图像，然后将第一目标子图像和第二目标子图像为目标频带对应的变换信号，这样可以获得更准确的放大运动信息，同时也避免了对更高分辨率图像进行放大的需要增加的运算量。
请参阅图4，基于上述实施例，在某些实施例中，步骤02包括但不限于以下步骤：
021、从每个状态变化信号中提取多个第一状态变化信号段，并获取各第一状态变化信号段的频率；
状态变化信号为时域信号，可从各状态变化信号中提取不同时段的第一状态变化信号段，然后通过数学方法获得各第一状态变化信号段的频率。从每个状态变化信号中提取多个第一状态变化信号段可理解为，从每个状态变化信号中提取出至少2个第一状态变化信号段，然后获取各个第一状态变化信号段的频率。多个可以为2个、3个、4个或其他任意大于4的个数。
022、将多个状态变化信号中，对应的多个第一状态变化信号段的频率不一致的状态变化信号作为非周期信号。
可将一个状态变化信号对应的多个第一状态变化信号段的频率进行比较，判断多个第一状态变化信号段的频率是否一致。如果一个状态变化信号对应的多个第一状态变化信号段的频率不一致，例如，从一个状态变化信号中提取出了4个第一状态变化信号段，这4个第一状态变化信号段中，有两个第一状态变化信号段的频率为a，有两个第一状态变化信号段的频率为b，则该状态变化信号段对应的第一状态变化信号段的频率不一致，说明该状态变化信号至少存在两个不同频率值的频段，则可将该状态变化信号作为非周期信号。
请参阅图5，在某些实施例中，步骤02包括但不限于以下步骤：
023、从各状态变化信号中提取预设长度的目标状态变化信号段,并获取目标状态变化信号段的目标频率；
判断各状态变化信号是否为周期信号时，可先提取预设长度的目标状态变化信号段，获取目标变化信号段的目标频率，然后将状态变化信号中的其他部分的频率与目标频率比对，如果状态变化信号中的其他部分的频率与目标频率不一致，则可认为该状态变化信号为非周期信号。预设长度可以由用户设定一个确定的值，也可以在信号处理过程中根据信号的长度自行适配，例如可将预设长度设定为状态变化信号长度的1/10。
024、根据各状态变化信号对应的目标频率设定对应的滑窗,并将各状态变化信号发送至对应的滑窗；
获得目标状态变换信号段的目标频率之后，根据目标频率设定滑窗的窗口大小，例如可将滑窗的窗口大小设定为与目标频率一致，这样只有频率与目标频率一致的信号才能通 过滑窗，而与目标频率不一致的信号则不能通过滑窗。
025、将不能通过对应的滑窗的状态变化信号作为非周期信号。
如果状态变化信号不能通过对应的滑窗，则说明该状态变化信号中存在频率与目标频率不一致的信号段，也即该状态变化信号为非周期信号。
本实施例中，通过滑窗的方式判断状态变化信号中的其他部分的频率与目标频率是否一致，这样可以方便快捷地得到结论，计算量更小。
请参阅图6，在某些实施例中，步骤01包括：
011、获取对检测视频对应的帧序列进行相位相关计算得到的交叉互功率谱，交叉互功率谱为频域信号；
012、对交叉互功率谱进行反傅里叶变换得到多个状态变化信号。
这样实现将频域信号转换为时域信号，便于对识别出非周期信号，并去除非周期信号，提高了有用信号的信噪比，使得从包含有检测装置运行时的画面的检测视频中提取出的信息，有用信号的信噪比更高。
进一步地，请参阅图7，步骤012之前，步骤01还包括：
013、根据交叉互功率谱中的各相关峰在检测视频中对应的位置及相关峰的频段确定各相关峰对应的滤波策略；
步骤013在步骤011之后、步骤012之前执行。可分析各相关峰得到各相关峰在检测视频中对应的位置，相关峰在检测视频中对应的位置可以理解为该相关峰所反映的状态变化对应的检测视频中的位置。例如，检测视频中有一块位置的亮度随着时间呈周期性变化，对检测视频的对应的帧序列进行相位相关计算得到的的一个或多个相关峰中，则包含至少一个相关峰可以体现该位置的亮度变化。
由于检测视频中，不同位置的振动对检测待检测装置的运行状况的作用大小不一样。例如，处于检测视频的画面中的边缘位置的振动相比较于中间位置的振动，对检测待检测装置的运行状况的作用更小，那么可将各相关峰在检测视频中对应的位置作为依据，确定各相关峰的滤波策略。每个相关峰对应的频率范围(即频段)不一样，根据相关峰对应的频段确定各相关峰的滤波策略，这样使得滤波策略与每个相关峰更加适配，从而有助于从各相关峰中提取出有用的状态变化信号。滤波策略例如可包括但不限于滤波器的带宽，滤波器的类型等。
014、按照各相关峰对应的滤波策略对交叉互功率谱中的各相关峰进行滤波处理；
获得各相关峰的滤波策略之后，按照对应的滤波策略对各相关峰进行滤波处理，以使得从检测视频中获得的信号的信噪比更高。较佳地，可以按照各相关峰对应的滤波策略对各相关峰通过插值滤波的方式进行滤波处理，这样滤波效果更好。
步骤012包括：
0121、对进行滤波处理之后的交叉互功率谱进行反傅里叶变换得到多个状态变化信号。
这样在进行反傅里叶变换得到状态变化信号之前，先对交叉互功率谱进行滤波处理，这样可以使得后续识别非周期信号的过程运算量更小。而且，通过根据各相关峰的位置及对应的频段选择合适的滤波策略进行滤波处理，在去除非周期信号之前，先进行一次去燥， 减少各相关峰中的噪声信息，进一步地使得从检测视频中获得的状态变化信号的信噪比更高，从而使得根据检测视频得到的待检测装置的运行状况更加准确。
更进一步地，在根据交叉互功率谱中的各相关峰在检测视频中对应的位置及相关峰的频段确定各相关峰对应的滤波策略之前，还可以先利用主成分分解方法(PCA)对各相关峰进行降维处理，这样在步骤013中，对降维处理后的相关峰进行计算，得到各相关峰对应的滤波策略，这样可降低滤波过程的计算量，提高信号处理效率。主成分分解(PCA)用来将相关峰数据在振动检测的两个主方向上对数据维数约减。
再进一步地，在利用主成分分解方法对各相关峰进行降维处理之前对相关峰进行筛选，去除峰值不在预设范围内的相关峰，对峰值在预设峰值范围内的相关峰进行降维处理，过滤不在预设峰值范围内的相关峰，这样一方面可以提高从检测视频中获得的状态变化信号的信噪比，有用信号更多，另一方面由于去除了部分的噪声信号，从而可以减少后续的过滤的计算量，提升过滤效率。
请参阅图8，基于上述实施例，在某些实施例中，滤波策略包括滤波带宽，步骤013包括：
0131、根据交叉互功率谱中的各相关峰对应的频段确定各相关峰对应的初始滤波带宽；
可以理解，对检测视频对应的帧序列进行相位相关计算得到的的相关峰中，包括检测视频对应的画面中位置相近的多个像素点的相似的多个状态变化信息，从而可以反应多个像素点所在的位置的状态变化。那么检测视频对应的画面中，不同位置的状态变化情况不同，对应的相关峰的频率分布也不同。如果用相同的滤波带宽进行滤波，那么为了避免有用信号被过滤，需要将滤波带宽设为较大值，但是这样有会导致噪声信号无法被过滤。而本申请的滤波方法中，根据各相关峰对应的频段确定个相关峰对应的初始滤波带宽，相关峰对应的频段与初始滤波带宽正相关，相关峰对应的频段越大，对应的初始滤波带宽越宽，可使得滤波带宽更好地与各相关峰适配，既能够有效地过滤噪声信号，也有助于避免有用信号被过滤，从而可以使得过滤效果更好。
例如，可以将B*(1-a)作为初始滤波带宽，B为各相关峰对应的频段，a可以根据实际需求确定，当需要得到更好的去燥效果时，可将a设为较大值，当需要获取到更多的有用信号时，可将a设定为较小值。当然，在其他实施例中，初始滤波带宽的设定方法不限于上述举例，在此不做限定。
0132、根据各相关峰在检测视频中对应的位置确定相关峰对应的带宽调整系数；
检测视频中不同位置的振动对检测待检测装置的运行状况的作用大小不一样。例如画面边缘的振动与画面中心的振动相比，画面边缘的作用对检测待检测装置的运行状况的作用更小。那么可根据各相关峰在检测视频中对应的位置确定相关峰对应的带宽调整系数。利用带宽调整系数来调整初始滤波带宽，使得滤波带宽更合理。
例如，可根据相关峰在检测视频中对应的位置的振动对检测待检测装置的运行状况的作用大小，将检测视频对应的画面分为多个区域，并为每个区域预先设定预设带宽调整系数，这样在步骤022中确定带宽调整系数时，可直接获取各相关峰在检测视频中对应的位置所在的区域对应的预设带宽调整系数，并将预设带宽调整系数作为相关峰对应的带宽调整系数。这样可以快速准确的获得带宽调整系数。
进一步地，用户可根据待检测装置的结构，对检测视频对应的画面中的各个区域设定带宽加权系数，然后将预设带宽调整系数与带宽加权系数的乘积作为对应的区域的相关的带宽调整系数，这样使得得到的各个相关峰的滤波带宽更加合理。例如，待检测装置中某些关键部位的振动相对其他部位更加能够反应待检测装置的运行状况，那么可将该关键部位在检测视频的画面中对应的区域的带宽加权系数设定为较大值，使得滤波带宽更大，从而可以提取到更多的有用信息。
0133、将各相关峰对应的初始滤波带宽和带宽调整系数的乘积作为各相关峰对应的滤波带宽。
这样得到的滤波带宽，综合考虑了相关峰在检测视频中对应的位置及相关峰对应的频段，使得滤波带宽与每个相关峰更加适配，从而有助于从交叉互功率谱中提取出有用的状态变化信号。
需要说明的是，滤波带宽的计算方法不限于上述方法，在其他实施例中也可以根据各相关峰对应的频段和各相关峰在检测视频中对应的位置选择合适的计算方法来确定滤波带宽。
本申请实施例还提供一种信号处理装置，包括：
信号获取模块，用于获取对检测视频对应的帧序列进行相位相关计算得到的交叉互功率谱对应的多个状态变化信号，状态变化信号为时域信号；
分析模块，用于分析多个状态变化信号，以获取多个状态变化信号中的非周期信号；
信号处理模块，用于去除多个状态变化信号中的非周期信号。
本申请实施例的信号处理装置，获取对检测视频对应的帧序列进行相位相关计算得到的交叉互功率谱对应的多个状态变化信号，状态变化信号为时域信号；分析多个状态变化信号，以获取多个状态变化信号中的非周期信号；去除多个状态变化信号中的非周期信号。这样从检测视频中获取到的状态变化信号中，有用信息更多，从而可以使得根据检测视频得到的待检测装置的运行状况更加准确。需要说明的是，上述信号处理方法各实施例的解释说明及技术效果也适用于本实施例的信号处理装置，为避免冗余，在此不再赘述。
在某些实施例中，分析模块包括：
第一信号提取单元，用于从每个状态变化信号中提取多个第一状态变化信号段，并获取各第一状态变化信号段的频率；
第一执行单元，用于将多个状态变化信号中，对应的多个第一状态变化信号段的频率不一致的状态变化信号作为非周期信号。
在某些实施例中，分析模块包括：
第二信号提取单元，用于从各状态变化信号中提取预设长度的目标状态变化信号段,并获取目标状态变化信号段的目标频率；
信号发送单元，用于根据各状态变化信号对应的目标频率设定对应的滑窗,并将各状态变化信号发送至对应的滑窗；
第二执行单元，用于将不能通过对应的滑窗的状态变化信号作为非周期信号。
在某些实施例中，信号获取模块包括：
信号获取单元，用于获取对检测视频对应的帧序列进行相位相关计算得到的交叉互功 率谱，交叉互功率谱为频域信号；
信号转换单元，用于对交叉互功率谱进行反傅里叶变换得到多个状态变化信号。
在某些实施例中，信号获取模块还包括：
滤波策略确定单元，用于根据交叉互功率谱中的各相关峰在检测视频中对应的位置及相关峰的频段确定各相关峰对应的滤波策略；
滤波单元，用于按照各相关峰对应的滤波策略对交叉互功率谱中的各相关峰进行滤波处理；
信号转换单元还用于：
对进行滤波处理之后的交叉互功率谱进行反傅里叶变换得到多个状态变化信号。
在某些实施例中，滤波策略包括滤波带宽，滤波策略确定单元包括：
初始滤波带宽确定子单元，用于根据交叉互功率谱中的各相关峰对应的频段确定各相关峰对应的初始滤波带宽；
带宽调整系数确定子单元，用于根据各相关峰在检测视频中对应的位置确定相关峰对应的带宽调整系数；
滤波带宽确定子单元，用于将各相关峰对应的初始滤波带宽和带宽调整系数的乘积作为各相关峰对应的滤波带宽。
其中，上述信号处理装置中各个模块的功能实现及技术效果与上述信号处理方法实施例中各步骤相对应，其功能和实现过程在此处不再一一赘述。
本申请还提供一种计算机可读存储介质，计算机可读存储介质上存储有信号处理程序，其中信号处理程序被处理器执行时，实现上述任一实施例的信号处理方法的步骤。
其中，信号处理程序被执行时所实现的方法及对应的技术效果可参照本申请信号处理方法的各个实施例，此处不再赘述。
本申请实施例还提供一种检测设备，用于检测待检测装置的运行状况，检测设备包括上述实施例的信号处理装置或上述实施例的电子装置。
本申请实施例的检测设备，获取对检测视频对应的帧序列进行相位相关计算得到的交叉互功率谱对应的多个状态变化信号，状态变化信号为时域信号；分析多个状态变化信号，以获取多个状态变化信号中的非周期信号；去除多个状态变化信号中的非周期信号。这样从检测视频中获取到的状态变化信号中，有用信息更多，从而可以使得根据检测视频得到的待检测装置的运行状况更加准确。
检测待检测装置的运行状况时，需要从包含有检测装置运行时的画面的检测视频中提取振动信息，在检测视频中，振动信息可由状态变化信息体现。如图3所示，可先将检测视频中的帧序列由RGB颜色空间转换到YIQ颜色空间，分离视频帧的亮度信息和色度信息。RGB和YIQ的转换关系为：
Y＝0.299*R+0.587*G+0.114*B；
I＝0.596*R–0.275*G–0.321*B；
Q＝0.212*R-0.523*G+0.311*B。
然后保持I、Q通道不变，对Y通道进行FFT操作，再利用欧拉运动放大算法对视频数据行放大处理。具体包括：先将FFT变换后的Y通道图像进行复数可操纵金子塔空域分 解，将Y通道空域分解后的不同尺度的图像进行时域带通滤波，可以理解，在视频画面中，振动可由视频帧序列的亮度反映，那么可通过分析检测视频中的Y通道获取检测视频中的振动信息。然后放大时域带通滤波后感兴趣的运动信息，对感兴趣的运动信息进行复数可操纵金字塔重建，得到放大后的Y通道图像；最后将重建的Y通道图像与原来的I、Q通道图像相加，再转化为RGB色彩空间，得到输出视频。
再对视频运动放大处理后的帧序列采用相位相关算法计算帧序列间的交叉互功率谱。相位相关算法采用如下的公式计算交叉互功率谱。
Figure PCTCN2020105591-appb-000004
上式中，Fa为a帧图像的傅立叶变换，
Figure PCTCN2020105591-appb-000005
为b帧图像的傅里叶变换的共轭信号，
Figure PCTCN2020105591-appb-000006
为两个傅里叶变换的信号的相关积的模。R为本步骤的计算结果交叉互功率谱(包含频域噪音)。这样得到的交叉互功率谱为频域信号，将交叉互功率谱进行反傅立叶变换，逐相位比较，便可得到多个状态变化信号，可以反应检测视频中各处的状态变化信息。
最后按照本申请实施例的上述滤波方法对多个状态变化信号进行处理，去除非周期信号，提高了有用信号的信噪比，使得从包含有检测装置运行时的画面的检测视频中提取出的信息，有用信号的信噪比更高。
在上述实施例中，可全部或部分地通过软件、硬件、固件、或其任意组合来实现。当使用软件实现时，可以全部或部分地以计算机程序产品的形式实现。所述计算机程序产品包括一个或多个计算机指令。在计算机上加载和执行所述计算机程序指令时，全部或部分地产生按照本申请实施例所述的流程或功能。所述计算机可以是通用计算机、专用计算机、计算机网络、或者其他可编程装置。所述计算机指令可以存储在计算机可读存储介质中，或者从一个计算机可读存储介质向另一个计算机可读存储介质传输，例如，所述计算机指令可以从一个网站站点、计算机、服务器或数据中心通过有线(例如同轴电缆、光纤、数字用户线)或无线(例如红外、无线、微波等)方式向另一个网站站点、计算机、服务器或数据中心进行传输。所述计算机可读存储介质可以是计算机能够存取的任何可用介质或者是包含一个或多个可用介质集成的服务器、数据中心等数据存储设备。所述可用介质可以是磁性介质(例如软盘、硬盘、磁带)、光介质(例如光盘)、或者半导体介质(例如固态硬盘)等。在上述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详述的部分，可以参见其他实施例的相关描述。
在上述实施例中，对各个实施例的描述都各有侧重，某个实施例中没有详述的部分，可以参见其他实施例的相关描述。
在本申请所提供的几个实施例中，应该理解到，所揭露的装置，也可以通过其它的方式实现。例如以上所描述的装置实施例仅仅是示意性的，例如所述单元的划分，仅仅为一种逻辑功能划分，实际实现时可以有另外的划分方式，例如多个单元或组件可结合或者可以集成到另一个系统，或一些特征可以忽略或不执行。另一点，所显示或讨论的相互之间的间接耦合或者直接耦合或通信连接可以是通过一些接口，装置或单元的间接耦合或通信连接，可以是电性或其它的形式。
所述作为分离部件说明的单元可以是或者也可以不是物理上分开的，作为单元显示的 部件可以是或者也可以不是物理单元，即可以位于一个地方，或者，也可以分布到多个网络单元上。可以根据实际的需要选择其中的部分或者全部单元来实现本实施例的方案的目的。
另外，在本申请各实施例中的各功能单元可集成在一个处理单元中，也可以是各单元单独物理存在，也可两个或两个以上单元集成在一个单元中。上述集成的单元既可以采用硬件的形式实现，或者也可以采用软件功能单元的形式实现。
所述集成的单元如果以软件功能单元的形式实现并作为独立的产品销售或使用时，可以存储在一个计算机可读取存储介质中。基于这样的理解，本申请技术方案本质上或者说对现有技术做出贡献的部分或者该技术方案的全部或部分可以以软件产品的形式体现出来，该计算机软件产品存储在一个存储介质中，包括若干指令用以使得一台计算机设备(可为个人计算机、服务器或者网络设备等)执行本申请各个实施例所述方法的全部或部分步骤。而前述的存储介质例如可包括：U盘、移动硬盘、只读存储器(ROM，Read-Only Memory)、随机存取存储器(RAM，Random Access Memory)、磁碟或光盘等各种可存储程序代码的介质。

Claims (10)

  1. 一种信号处理方法，其特征在于，包括：
    获取对检测视频对应的帧序列进行相位相关计算得到的交叉互功率谱对应的多个状态变化信号，所述状态变化信号为时域信号；
    分析所述多个状态变化信号，以获取所述多个状态变化信号中的非周期信号；
    去除所述多个状态变化信号中的非周期信号。
  2. 根据权利要求1所述的滤波方法，其特征在于，所述分析所述多个状态变化信号，以获取所述多个状态变化信号中的非周期信号包括：
    从每个状态变化信号中提取多个第一状态变化信号段，并获取各第一状态变化信号段的频率；
    将所述多个状态变化信号中，对应的多个第一状态变化信号段的频率不一致的状态变化信号作为所述非周期信号。
  3. 根据权利要求1所述的滤波方法，其特征在于，所述分析所述多个状态变化信号，以获取所述多个状态变化信号中的非周期信号包括：
    从各状态变化信号中提取预设长度的目标状态变化信号段,并获取所述目标状态变化信号段的目标频率；
    根据各状态变化信号对应的目标频率设定对应的滑窗,并将各状态变化信号发送至对应的滑窗；
    将不能通过对应的滑窗的状态变化信号作为所述非周期信号。
  4. 根据权利要求1所述的信号处理方法，其特征在于，所述获取对检测视频对应的帧序列进行相位相关计算得到的交叉互功率谱对应的多个状态变化信号包括：
    获取对检测视频对应的帧序列进行相位相关计算得到的交叉互功率谱，所述交叉互功率谱为频域信号；
    对所述交叉互功率谱进行反傅里叶变换得到多个状态变化信号。
  5. 根据权利要求4所述的信号处理方法，其特征在于，所述对所述交叉互功率谱进行反傅里叶变换得到多个状态变化信号之前，所述获取对检测视频对应的帧序列进行相位相关计算得到的交叉互功率谱对应的多个状态变化信号还包括：
    根据所述交叉互功率谱中的各相关峰在所述检测视频中对应的位置及相关峰的频段确定各相关峰对应的滤波策略；
    按照各相关峰对应的滤波策略对所述交叉互功率谱中的各相关峰进行滤波处理；
    所述对所述交叉互功率谱进行反傅里叶变换得到多个状态变化信号包括：
    对进行滤波处理之后的交叉互功率谱进行反傅里叶变换得到多个状态变化信号。
  6. 根据权利要求5所述的信号处理方法，其特征在于，所述滤波策略包括滤波带宽，所述根据所述交叉互功率谱中的各相关峰在所述检测视频中对应的位置及相关峰的频段确定各相关峰对应的滤波策略包括：
    根据所述交叉互功率谱中的各相关峰对应的频段确定各相关峰对应的初始滤波带宽；
    根据各相关峰在所述检测视频中对应的位置确定所述相关峰对应的带宽调整系数；
    将各相关峰对应的初始滤波带宽和带宽调整系数的乘积作为各相关峰对应的滤波带宽。
  7. 一种信号处理装置，其特征在于，包括：
    信号获取模块，用于获取对检测视频对应的帧序列进行相位相关计算得到的交叉互功率谱对应的多个状态变化信号，所述状态变化信号为时域信号；
    分析模块，用于分析所述多个状态变化信号，以获取所述多个状态变化信号中的非周期信号；
    信号处理模块，用于去除所述多个状态变化信号中的非周期信号。
  8. 一种电子装置，其特征在于，包括处理器、存储器、以及存储在所述存储器上并可被所述处理器执行的信号处理程序，其中所述信号处理程序被所述处理器执行时，实现权利要求1至6中任一项所述的信号处理方法的步骤的指令。
  9. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质上存储有信号处理程序，其中所述信号处理程序被处理器执行时，实现权利要求1至6中任一项所述的信号处理方法的步骤。
  10. 一种检测设备，用于检测待检测装置的运行状况，其特征在于，所述检测设备包括权利要求7所述的信号处理装置或权利要求8所述的电子装置。
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