CN110631812A - Track vibration detection method and device and vibration detection equipment - Google Patents
Track vibration detection method and device and vibration detection equipment Download PDFInfo
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Abstract
The application discloses a track vibration detection method and device and vibration detection equipment, wherein the method comprises the following steps: the vibration detection equipment acquires a track vibration video of a track to be detected, and selects a first RGB image according to the track vibration video of the track to be detected; linearly converting the first RGB image from an RGB color space to a YIQ color space to obtain a first YIQ image; processing the Y-channel image in the first YIQ image through a video amplification algorithm to obtain a characteristic image sequence; extracting actual vibration data of the track to be detected according to the characteristic image sequence; and determining the stability of the rail to be detected according to the actual vibration data, so that the accuracy of vibration data extraction and the universality of engineering application are improved, and the efficiency, accuracy and reliability of rail vibration detection are further improved.
Description
Technical Field
The application relates to the technical field of computers, in particular to a track vibration detection method and device and vibration detection equipment.
Background
The track is a route laid by using strip-shaped steel rails and used for running of trains, trams and the like. Wherein, maintainer need regularly maintain and inspect the track to guarantee transportation's safety. In the rail maintenance and inspection work, rail vibration detection is an important link.
In traditional vibration detection, whether security hidden danger exists in track joint, track fastener and railway roadbed etc. is often investigated through personal experience or simple vibration check out test set to maintainer, and this detection efficiency that also leads to track vibration to detect is low and the not accurate scheduling problem of testing result. In addition, in the related art, videos of the track vibration are shot through related equipment, and the track vibration videos are processed and analyzed so as to reduce labor cost and detection time, but the track vibration videos also have the defects that the vibration amplitude of a vibration part is fine, the vibration part is difficult to observe by naked eyes, and the extraction difficulty of vibration information is large.
Disclosure of Invention
The application provides a track vibration detection method and device and vibration detection equipment, aiming to improve the accuracy of vibration information extraction and the application universality and further improve the efficiency, accuracy and reliability of vibration detection.
In a first aspect, an embodiment of the present application provides a rail vibration detection method, which is applied to a vibration detection device, and the method includes:
acquiring a track vibration video of a track to be detected, and selecting a first RGB image according to the track vibration video of the track to be detected;
linearly converting the first RGB image from an RGB color space to a YIQ color space to obtain a first YIQ image;
processing the Y-channel image in the first YIQ image through a video amplification algorithm to obtain a characteristic image sequence;
extracting actual vibration data of the track to be detected according to the characteristic image sequence;
and determining the stability of the track to be tested according to the actual vibration data.
In a second aspect, an embodiment of the present application provides an apparatus for detecting track vibration, which is applied to a vibration detection device, and includes a processing unit and a communication unit, where:
the processing unit is used for acquiring a track vibration video of a track to be detected and selecting a first RGB image according to the track vibration video of the track to be detected; and the first RGB image is linearly converted from an RGB color space to a YIQ color space, and a first YIQ image is obtained; the Y-channel image in the first YIQ image is processed through a video amplification algorithm to obtain a characteristic image sequence; the system is used for extracting actual vibration data of the track to be detected according to the characteristic image sequence; and the stability of the track to be tested is determined according to the actual vibration data.
In a third aspect, embodiments of the present application provide a vibration detection apparatus, comprising a processor, a memory, a communication interface, and one or more programs, stored in the memory and configured to be executed by the processor, the programs including instructions for performing the steps of any of the methods of the first aspect of the embodiments of the present application.
In a fourth aspect, embodiments of the present application provide a computer-readable storage medium, where the computer-readable storage medium stores a computer program for electronic data exchange, and the computer program enables a computer to perform some or all of the steps described in any one of the methods of the first aspect of the embodiments of the present application.
In a fifth aspect, the embodiments of the present application provide a computer program product, where the computer program product includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform some or all of the steps as described in any one of the methods of the first aspect of the embodiments of the present application. The computer program product may be a software installation package.
The embodiment of the application discloses a method and a device for detecting track vibration, wherein a track vibration video of a track to be detected is obtained, and a first RGB image is selected according to the track vibration video of the track to be detected; linearly converting the first RGB image from an RGB color space to a YIQ color space to obtain a first YIQ image; processing the Y-channel image in the first YIQ image through a video amplification algorithm to obtain a characteristic image sequence; extracting actual vibration data of the track to be detected according to the characteristic image sequence; and determining the stability of the rail to be detected according to the actual vibration data, improving the accuracy of vibration data extraction and the universality of engineering application, and further improving the efficiency, accuracy and reliability of rail vibration detection.
Drawings
In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, it is obvious that the drawings in the following description are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic diagram of a system for detecting rail vibration according to an embodiment of the present disclosure;
fig. 2a is a schematic flowchart of a track vibration detection method according to an embodiment of the present disclosure;
FIG. 2b is a schematic diagram of a possible processing track vibration video provided by an embodiment of the present application;
fig. 2c is a schematic plan view of a possible rail clip according to an embodiment of the present disclosure;
FIG. 3 is a schematic flow chart of another track vibration detection method provided in the embodiments of the present application;
fig. 4 is a schematic flow chart of another track vibration detection method according to an embodiment of the present application
Fig. 5 is a schematic structural diagram of a vibration detection apparatus provided in an embodiment of the present application;
fig. 6 is a block diagram of functional units of a track vibration detection apparatus according to an embodiment of the present disclosure.
Detailed Description
In order to make the technical solutions of the present application better understood, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are only a part of the embodiments of the present application, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
The terms "first," "second," and the like in the description and claims of the present application and in the above-described drawings are used for distinguishing between different objects and not for describing a particular order. Furthermore, the terms "include" and "have," as well as any variations thereof, are intended to cover non-exclusive inclusions. For example, a process, method, system, article, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements listed, but may alternatively include other steps or elements not listed, or inherent to such process, method, article, or apparatus.
Reference herein to "an embodiment" means that a particular feature, structure, or characteristic described in connection with the embodiment can be included in at least one embodiment of the application. The appearances of the phrase in various places in the specification are not necessarily all referring to the same embodiment, nor are separate or alternative embodiments mutually exclusive of other embodiments. It is explicitly and implicitly understood by one skilled in the art that the embodiments described herein can be combined with other embodiments.
The following describes embodiments of the present application in detail.
Referring to fig. 1, fig. 1 is a schematic diagram of a system for detecting track vibration according to an embodiment of the present disclosure. Fig. 1 includes a rail 100 and a vibration detection device 110.
Specifically, the track 100 of the present application primarily includes rails 101, ties 102, track joints 103, track clips 104, a track bed 105, and the like. Among other things, the tie 102 includes a wood tie 1021 and a concrete tie 1022, which take up vertical and horizontal forces from the rail 101 and distribute these forces over the bed 105 while effectively maintaining the gauge, direction and position of the track 100. Thus, the tie 102 needs to be somewhat sturdy, durable, and resilient, while having sufficient resistance to prevent lateral movement under the action of the train.
Specifically, the steel rail connecting structure of the application is divided into an intermediate connecting structure and a joint connecting structure. The intermediate connection is a connection between the rail 101 and the tie 102, and the intermediate connection structure is called a rail fastener 104, and mainly has a function of preventing the rail 101 from moving longitudinally and transversely relative to the tie 102 and keeping the rail 101 in a stable position. The rail fasteners 104 include wood tie fasteners 1041 and concrete tie fasteners 1042. The wooden sleeper fastener 1041 includes a baffle, a spike backing plate and a common spike, and the concrete sleeper fastener 1042 includes a baffle, a spike backing plate and a common spike. Next, the joint connection is a connection of the rail 101 and the rail 101. The joint connection is made up of clamps, bolts, spring washers, etc., known as rail joints 103, which function to join the rails 101 at the joint so that the rail joint portions have the same integrity as the rails 101 to resist bending and displacement.
Specifically, the track bed 105 of the present application functions to uniformly distribute the force transmitted from the tie 102 to the roadbed, fix the position of the tie 102, maintain the stability of the track 100, remove roadbed moisture, maintain the elasticity of the track 100, and adjust the plane and profile of the track 100.
Specifically, the vibration detection apparatus 110 of the present application includes an electronic apparatus 111 having a video processing capability, a camera 113, and a sensor 112. The electronic device 111 may include various handheld devices with video processing capabilities, in-vehicle devices, wearable devices, computing devices or other video processing devices connected to a wireless modem, and various forms of intelligent terminal devices (intelligent terminal devices), among others. The camera 112 may include an infrared camera and a visible light camera, and the visible light camera may also include a general camera or a wide-angle camera, which is not limited herein. The camera 113 is mainly used for recording a video segment of the track 100 and uploading the video segment to the electronic device 111, and the track vibration detection device in the electronic device 111 performs vibration detection on the uploaded video segment. The sensor 112 comprises an ultrasonic sensor for detecting the rail 100.
Now, the following describes the implementation steps of track vibration detection, please refer to fig. 2 a. Fig. 2a is a schematic flowchart of a track vibration detection method provided in an embodiment of the present application, and the method is applied to a vibration detection device, and the method includes:
s201, acquiring a track vibration video of a track to be detected, and selecting a first RGB image according to the track vibration video of the track to be detected;
specifically, the track vibration video of the track to be detected can be obtained through the following operations: shooting a track to be detected according to the vibration detection equipment, and obtaining a first video and a second video of the track to be detected, wherein the first video and the second video are different videos shot by the track to be detected in the same time; acquiring a first image corresponding to the first video and a second image corresponding to the second video; and overlapping the first image and the second image, removing pixel points which cannot be overlapped by the first image and the second image, and obtaining the track vibration video of the track to be detected.
When the track to be detected is shot by the vibration detection equipment, external interference factors such as camera shaking and camera failure exist, so that the track vibration video of the track to be detected has certain deviation from the real situation. Therefore, different videos of the track to be detected are shot by adopting different cameras in the same time, a first video and a second video of the track to be detected are obtained, and a first image corresponding to the first video and a second image corresponding to the second video are overlapped. Under the condition that the camera does not have the interference factors, pixel points corresponding to the first image and the second image can be completely overlapped, and images with less noise can be obtained by clearing pixel points which cannot be overlapped by the first image and the second image, namely shooting deviation pixel points in different cameras. Also, more different videos in the same time period can be shot for the track to be measured, so as to further reduce image noise.
S202, linearly converting the first RGB image from an RGB color space to a YIQ color space to obtain a first YIQ image;
specifically, RGB is a color standard in the industry represented by three color components of a Red primary color (Red), a Green primary color (Green), and a Blue primary color (Blue). The RGB color space represents color using a linear combination of three color components, while YIQ is a National Television Standards Committee (NTSC) Television system standard. Y denotes a luminance component providing black-and-white and color televisions, I (In-phase) denotes a chrominance component of colors from orange to cyan, and Q (Quadrature-phase) denotes a chrominance component of colors from violet to yellow-green. The YIQ color space can separate and extract the brightness component in the video image, and the YIQ color space and the RGB color space are in a linear transformation relation, so that the method has the advantages of small calculation amount, good clustering characteristic and the like, and can adapt to occasions with constantly changing illumination intensity.
Specifically, the linear conversion relationship between the RGB color space and the YIQ color space is:
Y＝a1*R+a2*G+a3*B；
I＝b1*R+b2*G+b3*B；
Q＝c1*R+c2*G+c3*B；
wherein, a1, a2, a3, b1, b2, b3, c1, c2 and c3 have the value range of [ -1,1 ].
Preferably, the linear conversion relationship between the RGB color space and the YIQ color space is:
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。
for example, please refer to fig. 2b, fig. 2b is a schematic diagram of a possible processing track vibration video according to an embodiment of the present application. Wherein, vibration detection device 110 shoots the track vibration video that obtains a section track 100 to gather the image of track fastener 104 vibration in the track vibration video. The red, green and blue primary color components of the first image are extracted separately by the RGB color space. And obtaining a Y image component, a Q image component and an I image component of the first YIQ image according to the linear conversion relation between the RGB color space and the YIQ color space.
S203, processing the Y-channel image in the first YIQ image through a video amplification algorithm to obtain a characteristic image sequence;
specifically, the video amplification algorithm may include at least one of: laplace motion amplification algorithm, Euler motion amplification algorithm, complex phase motion amplification algorithm, and Reed pyramid motion amplification algorithm.
S204, extracting actual vibration data of the track to be detected according to the characteristic image sequence;
s205, determining the stability of the track to be tested according to the actual vibration data.
Specifically, the stability of the rail to be tested is determined by comparing the actual vibration data with the vibration data of the rail joint 103, the rail clip 104 and the track bed 105 in the rail to be tested.
It can be seen that the rail vibration detection method described in the embodiments of the present application is applied to rail vibration equipment. The track vibration equipment extracts vibration data from the track vibration video through a video amplification algorithm, so that the accuracy of vibration data extraction is improved, and the efficiency, accuracy and reliability of track vibration detection are improved.
In one possible example, the processing the Y-channel image in the first YIQ image through a video amplification algorithm to obtain a feature image sequence includes: performing Fourier transform on a Y-channel image in the first YIQ image; performing image pyramid decomposition on the Y-channel image subjected to Fourier transform; carrying out normalization, space-time filtering and linear amplification processing on the Y-channel image subjected to image pyramid decomposition; and synthesizing the processed Y-channel image with an I-channel image and a Q-channel image in the YIQ image, and forming a characteristic image sequence.
The Y-channel image represents luminance information, and the luminance information in the time domain is converted into phase change in the frequency domain by performing Fourier transform on the Y-channel image.
Specifically, the image pyramid is a method for performing multi-resolution processing on an image in a spatial domain. The size and contrast of the object image are different, and multi-resolution analysis of the object image can be beneficial to analyzing the object image and extracting the characteristic parameters of the object image. In addition, the object image is decomposed into high-frequency and low-frequency components through time-frequency wavelet decomposition, and wavelet decomposition coefficients are obtained through two sampling. The object image is decomposed into a plurality of scales and directions according to the wavelet decomposition coefficients to obtain subband images of a plurality of object images.
Specifically, image normalization is a process of subjecting an image to a series of standard transformation processes and transforming it into a standard image, which is referred to as a normalized image. And processing the image to obtain multiple sub-band images, wherein the multiple sub-band images can obtain standard images in the same form after image normalization processing with the same parameters. Further, image normalization is to convert an image into a corresponding unique standard form through a series of transformations, and the standard form has invariant characteristics to affine transformations such as translation, rotation, scaling and the like.
Optionally, the processing the Y-channel image in the first YIQ image through a video amplification algorithm to obtain a feature image sequence includes: shooting a track to be detected according to the vibration detection equipment, and obtaining a track vibration video of the track to be detected; acquiring multi-frame images to be processed of the track vibration video, and partitioning the images to be processed; taking the pixel points in each partition as initial feature points, and matching based on the least-difference square sum SSD to calculate the flow vectors of the initial feature points; calculating the offset distance of the initial characteristic point according to the flow vector corresponding to the initial characteristic point; clustering a plurality of offset distances corresponding to a plurality of initial feature points by adopting a k-means clustering algorithm to obtain a plurality of clustering clusters; determining whether an offset distance average value in each of the plurality of clustering clusters is within a preset range; if so, determining the partition corresponding to the initial characteristic point in the cluster as a motion partition; and reserving the motion subareas in the multi-frame images to be processed of the target video to form a characteristic image sequence.
Optionally, the processing the Y-channel image in the first YIQ image through a video amplification algorithm to obtain a feature image sequence includes: performing Fourier transform on a Y-channel image in the first YIQ image; carrying out downsampling processing on the Y-channel image after Fourier transform to obtain a first sub-image, wherein the downsampling processing is to reduce the resolution of the image; performing upsampling processing on the first sub-image to obtain a second sub-image, wherein the upsampling processing is to improve the resolution of the image; performing pixel processing on the Y-channel image subjected to Fourier transform and the second sub-image to obtain a third sub-image; performing time-domain filtering processing on the third subgraph to obtain a target frequency band; determining a plurality of Y-channel image signals according to the target frequency band and a Y-channel image in the first YIQ image; amplifying the plurality of Y-channel image signals to obtain a plurality of amplified Y-channel image signals; synthesizing the amplified multiple Y-channel image signals according to a Laplacian pyramid reconstruction algorithm; and acquiring an I channel image and a Q channel image in the first YIQ image, and adding the synthesized multiple Y channel images with the I channel image and the Q channel image to form a characteristic image sequence.
Optionally, the processing the Y-channel image in the first YIQ image through a video amplification algorithm to obtain a feature image sequence includes: performing Fourier transform on a Y-channel image in the first YIQ image; carrying out spatial filtering on the Y-channel image after Fourier transform to obtain Y-channel images with different spatial resolutions; performing time domain filtering processing on the Y-channel images with different spatial resolutions to obtain a target frequency band; determining a plurality of Y-channel image signals according to the target frequency band and the Y-channel images with different spatial resolutions; amplifying the plurality of Y-channel image signals to obtain a plurality of amplified Y-channel image signals; synthesizing the amplified multiple Y-channel image signals according to a complex steerable pyramid reconstruction algorithm; and acquiring an I channel image and a Q channel image in the first YIQ image, and adding the synthesized multiple Y channel images with the I channel image and the Q channel image to form a characteristic image sequence.
Optionally, the processing the Y-channel image in the first YIQ image through a video amplification algorithm to obtain a feature image sequence includes: performing Fourier transform on a Y-channel image in the first YIQ image; decomposing the Y-channel image after Fourier transform according to the Laplacian pyramid; performing Rics transformation on the decomposed Y-channel image; performing orthogonal and phase processing on the Y-channel image after the Reed transform; performing spatial time domain filtering on the processed Y-channel image; and carrying out method and phase shift on the Y-channel image after spatial time domain filtering to form a characteristic image sequence.
The track vibration device decomposes the Y-channel image in the first YIQ image through the image pyramid, and performs normalization, space-time filtering and linear amplification processing on the Y-channel image decomposed by the image pyramid so as to amplify the track vibration video, improve the accuracy of vibration data extraction, and further improve the efficiency, accuracy and reliability of track vibration detection.
In one possible example, the extracting the actual vibration data of the rail to be measured according to the characteristic image sequence includes: calculating the cross power spectrum of the characteristic image sequence according to a phase correlation formula; extracting vibration information of pixels in the characteristic image sequence according to the cross interaction power spectrum; and determining the actual vibration data of the track to be tested according to the vibration information.
Specifically, the phase correlation algorithm calculates the cross-power spectrum of the feature image sequence by using the following formula:
wherein, FaIs the fourier transform of the image of the a-frame,the conjugate signal of the fourier transform of the b frame image,
is represented by FaAnd
r represents the cross-power spectrum (containing frequency-domain noise).
It can be seen that the vibration detection device calculates the cross power spectrum by using a phase correlation algorithm on the feature image sequence after video amplification, so as to improve the accuracy of track vibration detection.
In one possible example, the extracting vibration information of pixels in the feature image sequence according to the cross-interaction power spectrum includes: selecting an adaptive filter according to the peak position of the cross-power spectrum; filtering the cross power spectrum according to the self-adaptive filter; and performing inverse Fourier transform on the filtered cross-power spectrum; and extracting vibration information of pixels in the characteristic image sequence according to the cross power spectrum after the inverse Fourier transform.
Specifically, because the rail vibration of the rail to be detected is directional, normalization, space-time filtering and linear amplification processing are performed on the processed Y-channel image, and the obtained characteristic image sequence contains redundant signals. The redundant signals are subjected to difference, weighting and reconstruction, so that high computation is brought, and the redundant signals have no effect on analysis of vibration information. The main component decomposition is adopted to carry out dimensionality reduction on the characteristic image sequence, and then the self-adaptive filter is used for filtering the dimensionality reduced characteristic image sequence, so that the influence of operation processing time and noise signals can be effectively reduced.
It can be seen that the vibration detection device filters the characteristic image sequence through the adaptive filter, so as to reduce the calculation amount of the rail vibration detection, improve the efficiency of the rail vibration detection, and effectively ensure the stability of the rail.
In one possible example, the determining the stability of the rail to be tested according to the actual vibration data includes: determining a vibration part of the track to be detected according to the actual vibration data, wherein the vibration part comprises a track fastener, a track joint or a track bed; and determining the stability of the track to be tested according to the vibration part of the track to be tested and the actual vibration data.
It can be seen that the vibration detection equipment determines the stability of the rail to be detected through the vibration part of the rail to be detected and the actual vibration data so as to ensure the accuracy and reliability of rail vibration detection.
In one possible example, when the vibration portion includes a rail fastener or a rail joint, the determining the stability of the rail to be tested according to the vibration portion of the rail to be tested and the actual vibration data includes: collecting N sections of vibration videos of a track fastener or a track joint of the track to be detected; counting the average vibration frequency, the average amplitude and the average vibration period of the N sections of vibration videos; and comparing the average vibration frequency, the average amplitude and the average vibration period with the actual vibration data, and determining the stability of the track to be tested according to the comparison result.
Specifically, fig. 2c is a schematic plan view of a possible rail clip according to an embodiment of the present disclosure. The concrete tie fastener 1042 includes a baffle 301, a common spike 302, and a spike plate 303. The vibration detection equipment collects N sections of vibration videos of the concrete sleeper fastener 1042, and counts the average vibration frequency, the average amplitude and the average vibration period of the baffle 301 and the common spike 302 in the vertical vibration direction and the horizontal vibration direction through the N ends of the vibration videos.
It can be seen that the vibration detection equipment calculates the average vibration data of the rail fastener of the rail to be detected, and compares the average vibration data with the actual vibration data to improve the accuracy and reliability of the rail vibration detection.
In one possible example, when the vibration portion includes a track bed, the determining the stability of the track to be measured according to the vibration portion of the track to be measured and the actual vibration data includes: acquiring a track bed coefficient of a track bed; determining the vibration rigidity and the vibration elasticity coefficient of the track bed according to the track bed coefficient; and comparing the vibration rigidity and the vibration elasticity coefficient with the actual vibration data, and determining the stability of the track to be tested according to the comparison result.
The vibration detection equipment can be seen to improve the accuracy and the reliability of track vibration detection by acquiring the vibration rigidity and the vibration elasticity coefficient of the track bed of the track to be detected and comparing the vibration rigidity and the vibration elasticity coefficient with actual vibration data.
In accordance with the embodiment described above with reference to fig. 2a, please refer to fig. 3. Fig. 3 is a schematic flowchart of another track vibration detection method provided in an embodiment of the present application, and the method is applied to a vibration detection apparatus, and includes:
s301, acquiring a track vibration video of a track to be detected, and selecting a first RGB image according to the track vibration video of the track to be detected;
specifically, the track vibration video of the track to be detected can be obtained through the following operations: shooting a track to be detected according to the vibration detection equipment, and obtaining a first video and a second video of the track to be detected, wherein the first video and the second video are different videos shot by the track to be detected in the same time; acquiring a first image corresponding to the first video and a second image corresponding to the second video; and overlapping the first image and the second image, removing pixel points which cannot be overlapped by the first image and the second image, and obtaining the track vibration video of the track to be detected.
S302, linearly converting the first RGB image from an RGB color space to a YIQ color space to obtain a first YIQ image;
specifically, the linear conversion relationship between the RGB color space and the YIQ color space is:
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。
s303, carrying out Fourier transform on the Y-channel image in the first YIQ image, and carrying out image pyramid decomposition on the Y-channel image after Fourier transform;
the Y-channel image represents luminance information, and the luminance information in the time domain is converted into phase change in the frequency domain by performing Fourier transform on the Y-channel image.
The image pyramid includes a gaussian pyramid, a laplacian pyramid, a complex steerable pyramid, a rieus pyramid, and the like.
S304, carrying out normalization, space-time filtering and linear amplification processing on the Y-channel image subjected to image pyramid decomposition;
s305, synthesizing the processed Y-channel image with an I-channel image and a Q-channel image in the YIQ image, and forming a characteristic image sequence;
s306, calculating a cross power spectrum of the characteristic image sequence according to a phase correlation formula, and selecting an adaptive filter according to the peak position of the cross power spectrum;
s307, filtering the cross-power spectrum according to the self-adaptive filter, and performing inverse Fourier transform on the filtered cross-power spectrum;
s308, extracting vibration information of pixels in the characteristic image sequence according to the cross power spectrum after the inverse Fourier transform, and determining actual vibration data of the track to be detected according to the vibration information;
s309, determining a vibration part of the track to be detected according to the actual vibration data, wherein the vibration part comprises a track fastener, a track joint or a track bed;
s310, collecting N sections of vibration videos of a track fastener or a track joint of the track to be detected, and counting the average vibration frequency, the average amplitude and the average vibration period of the N sections of vibration videos;
s311, comparing the average vibration frequency, the average amplitude, the average vibration period and the actual vibration data, and determining the stability of the track fastener or the track joint according to the comparison result.
It can be seen that the rail vibration detection method described in the embodiments of the present application is applied to rail vibration equipment. The track vibration device amplifies the track vibration video through a video amplification algorithm, extracts actual vibration data of the track vibration video according to a phase correlation formula and inverse Fourier transform, and compares the average vibration frequency, the average amplitude and the average vibration period of the track fastener with the actual vibration data, so that the accuracy of vibration data extraction is improved, and the efficiency, accuracy and reliability of track vibration detection are improved.
Referring to fig. 4, fig. 4 is a schematic flowchart illustrating another track vibration detection method according to an embodiment of the present disclosure.
S401, acquiring a track vibration video of a track to be detected, and selecting a first RGB image according to the track vibration video of the track to be detected;
s402, processing the first RGB image, and obtaining a plurality of sub-band image sequences corresponding to a plurality of resolutions;
s403, screening at least one sub-band image sequence for amplification processing from the plurality of sub-band image sequences according to a preset partition gray value screening strategy;
specifically, the screening, according to a preset partition gray value screening strategy, at least one subband image sequence for amplification processing from the plurality of subband image sequences includes: determining a foreground image and a background image of the plurality of sub-band image sequences, wherein the foreground image comprises a region image of the detected product, which moves back and forth, and the background image is an image except the image of the detected product; determining the area ratio of the foreground image in the sub-band image; determining the sub-partition number of the foreground image according to the area ratio and a preset sub-partition calculation formula, and dividing the foreground image into a plurality of foreground sub-partitions according to the sub-partition number; and for each sub-band image sequence, performing the following operations (1) to (6) to obtain a gray value variation frequency of each sub-band image sequence: (1) determining a tested pixel point of each foreground sub-partition of each sub-band image in a currently processed sub-band image sequence; (2) generating a gray value time domain variation oscillogram of each pixel point to be detected according to the gray values of a plurality of sub-band images contained in the current sub-band image sequence by each pixel point to be detected; (3) performing the following (a) (b) (c) operations for each foreground sub-partition: (a) determining whether the currently processed foreground sub-partition contains a detected pixel point with periodically changed gray value according to a gray value time domain change oscillogram of a plurality of detected pixel points contained in the currently processed foreground sub-partition; (b) if so, marking the currently processed foreground sub-partition as the selected foreground sub-partition; (c) if not, marking the currently processed foreground subarea as an unselected foreground subarea; (4) splicing the foreground sub-partitions with adjacent relations into a vibration reference area according to the area relevance aiming at the marked selected plurality of foreground sub-partitions; (5) determining a plurality of reference pixel points with periodically changed gray values among the plurality of pixel points in the vibration reference region, and determining the gray value change frequency of each reference pixel point; (6) weighting and calculating the gray value change frequency of the plurality of reference pixel points in the vibration reference region to obtain the gray value change frequency of the currently processed sub-band image sequence; and screening out at least one sub-band image sequence which accords with a preset reference vibration frequency according to the gray value change frequency of each sub-band image sequence.
The preset sub-partition calculation formula is as follows:
wherein x is the area ratio, y is the number of sub-partitions, and x is greater than 0 and less than or equal to 1.
Wherein, the determination of the tested pixel point of each foreground sub-partition of each sub-band image in the currently processed sub-band image sequence comprises at least one of the following: the edge pixel points of each foreground sub-partition; pixel points of the middle area of each foreground sub-partition; and a plurality of randomly screened pixel points of each foreground sub-partition.
S404, amplifying the at least one sub-band image sequence to obtain at least one amplified sub-band image sequence;
s405, fusing the at least one amplified sub-band image sequence and sub-band image sequences except the at least one sub-band image sequence in the plurality of sub-band image sequences, and forming a characteristic image sequence;
s406, calculating a cross power spectrum of the characteristic image sequence according to a phase correlation formula, and selecting an adaptive filter according to the peak position of the cross power spectrum;
s407, filtering the cross-power spectrum according to the adaptive filter, and performing inverse Fourier transform on the filtered cross-power spectrum;
s408, extracting vibration information of pixels in the characteristic image sequence according to the cross power spectrum after the inverse Fourier transform, and determining actual vibration data of the track to be detected according to the vibration information;
s409, determining a vibration part of the track to be detected according to the actual vibration data, wherein the vibration part comprises a track fastener, a track joint or a track bed;
s410, collecting N sections of vibration videos of a track fastener or a track joint of the track to be detected, and counting the average vibration frequency, the average amplitude and the average vibration period of the N sections of vibration videos;
s411, comparing the average vibration frequency, the average amplitude, the average vibration period and the actual vibration data, and determining the stability of the track fastener or the track joint according to the comparison result.
It can be seen that the rail vibration detection method described in the embodiments of the present application is applied to rail vibration equipment. The track vibration device processes the track vibration video through a partition gray value screening strategy, and extracts vibration data from the processed track vibration video, so that the accuracy of vibration data extraction is improved, and the efficiency, accuracy and reliability of track vibration detection are improved.
In line with the embodiment described above with reference to fig. 2a and 3, reference is made to fig. 5. Fig. 5 is a schematic structural diagram of a vibration detection apparatus 500 according to an embodiment of the present application, where the vibration detection apparatus 500 includes an application processor 510, a memory 520, a communication interface 530, and one or more programs 521, where the one or more programs 521 are stored in the memory 520 and configured to be executed by the application processor 510, and the one or more programs 521 include instructions for: acquiring a track vibration video of a track to be detected, and selecting a first RGB image according to the track vibration video of the track to be detected; linearly converting the first RGB image from an RGB color space to a YIQ color space to obtain a first YIQ image; processing the Y-channel image in the first YIQ image through a video amplification algorithm to obtain a characteristic image sequence; extracting actual vibration data of the track to be detected according to the characteristic image sequence; and determining the stability of the track to be tested according to the actual vibration data.
The method comprises the steps that a vibration detection device obtains a track vibration video of a track to be detected, and selects a first RGB image according to the track vibration video of the track to be detected; linearly converting the first RGB image from an RGB color space to a YIQ color space to obtain a first YIQ image; processing the Y-channel image in the first YIQ image through a video amplification algorithm to obtain a characteristic image sequence; extracting actual vibration data of the track to be detected according to the characteristic image sequence; and determining the stability of the rail to be detected according to the actual vibration data, improving the accuracy of vibration data extraction and the universality of engineering application, and further improving the efficiency, accuracy and reliability of rail vibration detection.
In one possible example, in terms of processing the Y-channel image in the first YIQ image by a video amplification algorithm to obtain a sequence of feature images, the instructions in the program are specifically configured to perform the following operations: performing Fourier transform on a Y-channel image in the first YIQ image; performing image pyramid decomposition on the Y-channel image subjected to Fourier transform; carrying out normalization, space-time filtering and linear amplification processing on the Y-channel image subjected to image pyramid decomposition; and synthesizing the processed Y-channel image with an I-channel image and a Q-channel image in the YIQ image, and forming a characteristic image sequence.
In one possible example, in the aspect of extracting the actual vibration data of the rail to be measured according to the characteristic image sequence, the instructions in the program are specifically configured to: calculating the cross power spectrum of the characteristic image sequence according to a phase correlation formula; extracting vibration information of pixels in the characteristic image sequence according to the cross interaction power spectrum; and determining the actual vibration data of the track to be tested according to the vibration information.
In one possible example, in the aspect of extracting vibration information of pixels in the feature image sequence according to the cross-interaction power spectrum, the instructions in the program are specifically configured to: selecting an adaptive filter according to the peak position of the cross-power spectrum; filtering the cross power spectrum according to the self-adaptive filter; and performing inverse Fourier transform on the filtered cross-power spectrum; and extracting vibration information of pixels in the characteristic image sequence according to the cross power spectrum after the inverse Fourier transform.
In one possible example, in the aspect of determining the stability of the track to be tested according to the actual vibration data, the instructions in the program are specifically configured to: determining a vibration part of the track to be detected according to the actual vibration data, wherein the vibration part comprises a track fastener, a track joint or a track bed; and determining the stability of the track to be tested according to the vibration part of the track to be tested and the actual vibration data.
In one possible example, when the vibration part includes a rail fastener or a rail joint, in the aspect of determining the stability of the rail to be tested according to the vibration part of the rail to be tested and the actual vibration data, the instructions in the program are specifically configured to perform the following operations: collecting N sections of vibration videos of a track fastener or a track joint of the track to be detected; counting the average vibration frequency, the average amplitude and the average vibration period of the N sections of vibration videos; and comparing the average vibration frequency, the average amplitude and the average vibration period with the actual vibration data, and determining the stability of the track to be tested according to the comparison result.
In one possible example, when the vibration portion includes a track bed, in the aspect of determining the stability of the track to be measured according to the vibration portion of the track to be measured and the actual vibration data, the instructions in the program are specifically configured to perform the following operations: acquiring a track bed coefficient of a track bed; determining the vibration rigidity and the vibration elasticity coefficient of the track bed according to the track bed coefficient; and comparing the vibration rigidity and the vibration elasticity coefficient with the actual vibration data, and determining the stability of the track to be tested according to the comparison result.
The above description has introduced the solution of the embodiment of the present application mainly from the perspective of the method-side implementation process. It will be appreciated that the vibration detection apparatus, in order to carry out the above-described functions, comprises corresponding hardware structures and/or software modules for performing the respective functions. Those of skill in the art will readily appreciate that the present application is capable of hardware or a combination of hardware and computer software implementing the various illustrative elements and algorithm steps described in connection with the embodiments provided herein. Whether a function is performed as hardware or computer software drives hardware depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
The embodiment of the present application may perform division of the functional units for the vibration detection device according to the above method example, for example, each functional unit may be divided corresponding to each function, or two or more functions may be integrated into one processing unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit. It should be noted that the division of the unit in the embodiment of the present application is schematic, and is only a logic function division, and there may be another division manner in actual implementation.
Fig. 6 is a block diagram illustrating functional units of a track vibration detection apparatus 600 according to an embodiment of the present disclosure. The rail vibration detection apparatus 600 is applied to a vibration detection device, and includes a processing unit 601 and a communication unit 602.
The processing unit 601 is configured to perform any step in the above method embodiments, and when data transmission such as sending is performed, the communication unit 502 is optionally invoked to complete corresponding operations, which is described in detail below.
The processing unit 601 is specifically configured to: acquiring a track vibration video of a track to be detected, and selecting a first RGB image according to the track vibration video of the track to be detected; linearly converting the first RGB image from an RGB color space to a YIQ color space to obtain a first YIQ image; processing the Y-channel image in the first YIQ image through a video amplification algorithm to obtain a characteristic image sequence; extracting actual vibration data of the track to be detected according to the characteristic image sequence; and determining the stability of the track to be tested according to the actual vibration data.
The track vibration detection device comprises a first RGB image acquisition unit, a second RGB image acquisition unit, a first image acquisition unit and a second RGB image acquisition unit, wherein the first RGB image acquisition unit is used for acquiring a track vibration video of a track to be detected and selecting a first RGB image according to the track vibration video of the track to be detected; linearly converting the first RGB image from an RGB color space to a YIQ color space to obtain a first YIQ image; processing the Y-channel image in the first YIQ image through a video amplification algorithm to obtain a characteristic image sequence; extracting actual vibration data of the track to be detected according to the characteristic image sequence; and determining the stability of the rail to be detected according to the actual vibration data, improving the accuracy of vibration data extraction and the universality of engineering application, and further improving the efficiency, accuracy and reliability of rail vibration detection.
In a possible example, in terms of processing a Y-channel image in the first YIQ image through a video amplification algorithm to obtain a feature image sequence, the processing unit 601 is specifically configured to: performing Fourier transform on a Y-channel image in the first YIQ image; performing image pyramid decomposition on the Y-channel image subjected to Fourier transform; carrying out normalization, space-time filtering and linear amplification processing on the Y-channel image subjected to image pyramid decomposition; and synthesizing the processed Y-channel image with an I-channel image and a Q-channel image in the YIQ image, and forming a characteristic image sequence.
In a possible example, in terms of the extracting the actual vibration data of the rail to be measured according to the characteristic image sequence, the processing unit 601 is specifically configured to: calculating the cross power spectrum of the characteristic image sequence according to a phase correlation formula; extracting vibration information of pixels in the characteristic image sequence according to the cross interaction power spectrum; and determining the actual vibration data of the track to be tested according to the vibration information.
In one possible example, in terms of the extracting the vibration information of the pixels in the feature image sequence according to the cross-interaction power spectrum, the processing unit 601 is specifically configured to: selecting an adaptive filter according to the peak position of the cross-power spectrum; filtering the cross power spectrum according to the self-adaptive filter; and performing inverse Fourier transform on the filtered cross-power spectrum; and extracting vibration information of pixels in the characteristic image sequence according to the cross power spectrum after the inverse Fourier transform.
In one possible example, in the aspect of determining the stability of the track to be tested according to the actual vibration data, the processing unit 601 is specifically configured to: determining a vibration part of the track to be detected according to the actual vibration data, wherein the vibration part comprises a track fastener, a track joint or a track bed; and determining the stability of the track to be tested according to the vibration part of the track to be tested and the actual vibration data.
In a possible example, when the vibration portion includes a rail fastener or a rail joint, in the aspect of determining the stability of the rail to be tested according to the vibration portion of the rail to be tested and the actual vibration data, the processing unit 601 is specifically configured to: collecting N sections of vibration videos of a track fastener or a track joint of the track to be detected; counting the average vibration frequency, the average amplitude and the average vibration period of the N sections of vibration videos; and comparing the average vibration frequency, the average amplitude and the average vibration period with the actual vibration data, and determining the stability of the track to be tested according to the comparison result.
In a possible example, when the vibration portion includes a track bed, in the aspect of determining the stability of the track to be measured according to the vibration portion of the track to be measured and the actual vibration data, the processing unit 601 is specifically configured to: acquiring a track bed coefficient of a track bed; determining the vibration rigidity and the vibration elasticity coefficient of the track bed according to the track bed coefficient; and comparing the vibration rigidity and the vibration elasticity coefficient with the actual vibration data, and determining the stability of the track to be tested according to the comparison result.
Embodiments of the present application also provide a computer storage medium, wherein the computer storage medium stores a computer program for electronic data exchange, the computer program enabling a computer to perform part or all of the steps of any one of the methods as described in the above method embodiments, and the computer includes a vibration detection device.
Embodiments of the present application also provide a computer program product comprising a non-transitory computer readable storage medium storing a computer program operable to cause a computer to perform some or all of the steps of any of the methods as described in the above method embodiments. The computer program product may be a software installation package, said computer comprising vibration detection means.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present application is not limited by the order of acts described, as some steps may occur in other orders or concurrently depending on the application. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required in this application.
In the foregoing embodiments, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed apparatus may be implemented in other manners. For example, the above-described embodiments of the apparatus are merely illustrative, and for example, the above-described division of the units is only one type of division of logical functions, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection of some interfaces, devices or units, and may be an electric or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, 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 are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit may be stored in a computer readable memory if it is implemented in the form of a software functional unit and sold or used as a stand-alone product. Based on such understanding, the technical solution of the present application may be substantially implemented or a part of or all or part of the technical solution contributing to the prior art may be embodied in the form of a software product stored in a memory, and including several instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the above-mentioned method of the embodiments of the present application. And the aforementioned memory comprises: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable memory, which may include: flash Memory disks, Read-Only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.
The foregoing detailed description of the embodiments of the present application has been presented to illustrate the principles and implementations of the present application, and the above description of the embodiments is only provided to help understand the method and the core concept of the present application; meanwhile, for a person skilled in the art, according to the idea of the present application, there may be variations in the specific embodiments and the application scope, and in summary, the content of the present specification should not be construed as a limitation to the present application.
Claims (10)
1. A rail vibration detection method is applied to vibration detection equipment, and the method comprises the following steps:
acquiring a track vibration video of a track to be detected, and selecting a first RGB image according to the track vibration video of the track to be detected;
linearly converting the first RGB image from an RGB color space to a YIQ color space to obtain a first YIQ image;
processing the Y-channel image in the first YIQ image through a video amplification algorithm to obtain a characteristic image sequence;
extracting actual vibration data of the track to be detected according to the characteristic image sequence;
and determining the stability of the track to be tested according to the actual vibration data.
2. The method according to claim 1, wherein the processing the Y-channel image in the first YIQ image through a video amplification algorithm to obtain a sequence of feature images comprises:
performing Fourier transform on a Y-channel image in the first YIQ image;
carrying out image pyramid decomposition on the Y-channel image after Fourier transform;
carrying out normalization, space-time filtering and linear amplification processing on the Y-channel image subjected to image pyramid decomposition;
and synthesizing the processed Y-channel image with an I-channel image and a Q-channel image in the YIQ image, and forming a characteristic image sequence.
3. The method according to claim 1, wherein the extracting the actual vibration data of the rail to be measured according to the characteristic image sequence comprises:
calculating the cross power spectrum of the characteristic image sequence according to a phase correlation formula;
extracting vibration information of pixels in the characteristic image sequence according to the cross interaction power spectrum;
and determining the actual vibration data of the track to be detected according to the vibration information.
4. The method of claim 3, wherein extracting vibration information of pixels in the sequence of feature images from the cross-interaction power spectrum comprises:
selecting an adaptive filter according to the peak position of the cross-power spectrum;
filtering the cross power spectrum according to the adaptive filter;
performing inverse Fourier transform on the filtered cross-power spectrum;
and extracting vibration information of pixels in the characteristic image sequence according to the cross power spectrum after the inverse Fourier transform.
5. The method of claim 1, wherein determining the stability of the rail under test from the actual vibration data comprises:
determining a vibration part of the track to be detected according to the actual vibration data, wherein the vibration part comprises a track fastener, a track joint or a track bed;
and determining the stability of the track to be tested according to the vibration part of the track to be tested and the actual vibration data.
6. The method of claim 5, wherein when the vibration portion comprises a rail fastener or a rail joint, the determining the stability of the rail to be tested according to the vibration portion of the rail to be tested and the actual vibration data comprises:
collecting N sections of vibration videos of a track fastener or a track joint of the track to be detected;
counting the average vibration frequency, the average amplitude and the average vibration period of the N sections of vibration videos;
and comparing the average vibration frequency, the average amplitude, the average vibration period and the actual vibration data, and determining the stability of the track to be tested according to the comparison result.
7. The method of claim 5, wherein when the vibration portion comprises a track bed, the determining the stability of the track to be tested according to the vibration portion of the track to be tested and the actual vibration data comprises:
acquiring a track bed coefficient of a track bed;
determining the vibration rigidity and the vibration elasticity coefficient of the track bed according to the track bed coefficient;
and comparing the vibration rigidity and the vibration elasticity coefficient with the actual vibration data, and determining the stability of the track to be tested according to the comparison result.
8. A rail vibration detection device, applied to a vibration detection apparatus, the device comprising a processing unit and a communication unit, wherein:
the processing unit is used for acquiring a track vibration video of a track to be detected and selecting a first RGB image according to the track vibration video of the track to be detected; and the first RGB image is linearly converted from an RGB color space to a YIQ color space, and a first YIQ image is obtained; the Y-channel image in the first YIQ image is processed through a video amplification algorithm to obtain a characteristic image sequence; the system is used for extracting actual vibration data of the track to be detected according to the characteristic image sequence; and the stability of the track to be tested is determined according to the actual vibration data.
9. A vibration detection apparatus comprising a processor, a memory for storing one or more programs and configured for execution by the processor, the programs comprising instructions for performing the steps in the method of any of claims 1-7.
10. A computer-readable storage medium, characterized in that the computer-readable storage medium is used to store a computer program, which is executed by a processor to implement the method according to any of claims 1-7.
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