CN111507418A - Encaustic tile quality detection method - Google Patents

Encaustic tile quality detection method Download PDF

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CN111507418A
CN111507418A CN202010316550.XA CN202010316550A CN111507418A CN 111507418 A CN111507418 A CN 111507418A CN 202010316550 A CN202010316550 A CN 202010316550A CN 111507418 A CN111507418 A CN 111507418A
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叶中付
朱媛媛
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University of Science and Technology of China USTC
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Abstract

The invention discloses a method for detecting the quality of encaustic tiles, which comprises the following steps: recording the knocked sound of the complete encaustic tile, the damaged encaustic tile and the crack encaustic tile respectively, and constructing three sound signal training sets of the complete encaustic tile, the damaged encaustic tile and the crack encaustic tile; in the training stage, short-time Fourier transform is respectively carried out on three sound signal training sets, corresponding real parts and imaginary parts are extracted, dictionaries of the real parts and the imaginary parts are jointly learned based on a K-SVD algorithm, and therefore three dictionaries are obtained; and in the detection stage, recording the knocked sound of the encaustic tiles to be detected, respectively projecting the sound on the three dictionaries, reconstructing corresponding sound signals according to each sparse representation coefficient obtained by projection, and judging the category of the encaustic tiles to be detected according to the reconstruction error. The method not only utilizes the amplitude information of the sound signal frequency spectrum, but also utilizes the phase information of the signal, thereby being capable of keeping better detection capability, and the training data is easy to obtain, thereby being convenient for the industry to realize.

Description

Encaustic tile quality detection method
Technical Field
The invention relates to the technical field of encaustic tile quality detection, in particular to encaustic tile quality detection technology based on dictionary learning and sparse representation.
Background
At present, the encaustic tiles become an indispensable building material in our lives, and the encaustic tiles are needed in engineering project buildings, commercial houses, rural self-building villas and self-building houses, and small pavilions and small wooden houses. The production mode of the tile is mostly industrialized flow production, and the defective tile, including the damaged encaustic tile, the cracked encaustic tile and the like, is inevitably produced in the production process. How to detect the defective tile quickly and accurately has become a problem to be solved urgently in the industry.
The existing encaustic tile quality detection technology is mainly divided into two categories, namely manual detection and automatic detection. The manual detection is mostly carried out through naked eye judgment of workers or the flawed encaustic tiles are screened out according to some physical characteristics, the method is low in efficiency, and the human resource waste is large, so that the industrial industry mostly adopts an automatic detection method nowadays. In the automatic detection method, whether the encaustic tile has flaws is mostly judged based on visual information, the basic method is to adopt a camera to collect a picture of a complete encaustic tile and a picture of a detected encaustic tile, then corresponding pixel points of the two pictures are compared, whether the difference value is larger than a preset threshold range is judged, if yes, the encaustic tile is obtained, and if not, the encaustic tile is judged to be the flaw encaustic tile, but the method has higher requirements on the quality of the collected pictures and has certain requirements on equipment such as the camera; therefore, the equipment involved is costly.
Disclosure of Invention
The invention aims to provide a method for detecting the quality of a encaustic tile, which is used for detecting the quality of the encaustic tile by mining the knocked sound information of the encaustic tile and utilizing the sparsity of sound signals, and has the advantages of higher detection precision and lower cost.
The purpose of the invention is realized by the following technical scheme:
a encaustic tile quality detection method comprises the following steps:
recording the knocked sound of the complete encaustic tile, the damaged encaustic tile and the crack encaustic tile respectively, and constructing three sound signal training sets of the complete encaustic tile, the damaged encaustic tile and the crack encaustic tile;
in the training stage, short-time Fourier transform is respectively carried out on three sound signal training sets, corresponding real parts and imaginary parts are extracted, dictionaries of the real parts and the imaginary parts are jointly learned based on a K-SVD algorithm, and therefore three dictionaries are obtained;
and in the detection stage, recording the knocked sound of the encaustic tiles to be detected, respectively projecting the sound on the three dictionaries, reconstructing corresponding sound signals according to each sparse representation coefficient obtained by projection, and judging the category of the encaustic tiles to be detected according to the reconstruction error.
According to the technical scheme provided by the invention, the invention not only utilizes the amplitude information of the sound signal frequency spectrum, but also utilizes the phase information of the signal, so that the better detection capability can be kept, and the training data is easy to obtain and is convenient to realize in the industry.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is a flowchart of a method for detecting encaustic tile quality according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention are clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort, shall fall within the protection scope of the present invention.
Different from the traditional detection method based on visual information, the embodiment of the invention provides a method for detecting the quality of a encaustic tile, which detects the quality of the encaustic tile by mining the sound information of the encaustic tile being knocked and utilizing the sparsity of sound signals, as shown in fig. 1, and the method mainly comprises the following steps:
step 1, recording the knocked sound of the complete encaustic tile, the damaged encaustic tile and the crack encaustic tile respectively, and constructing three sound signal training sets of the complete encaustic tile, the damaged encaustic tile and the crack encaustic tile.
Illustratively, the full, broken and cracked encaustic tiles are struck with the same tool for a period of time, e.g., about 10s to about 20s, and the sounds of the striking are recorded by a microphone, respectively
Figure BDA0002459627780000031
And
Figure BDA0002459627780000032
and constructing three sound signal training sets of complete encaustic tiles, damaged encaustic tiles and cracked encaustic tiles.
In the embodiment of the invention, the damaged encaustic tiles refer to the existing missing parts of encaustic tiles, and the cracked encaustic tiles refer to the existing cracks of encaustic tiles but do not exist.
And 2, in a training stage, respectively carrying out short-time Fourier transform on three sound signal training sets, extracting corresponding real parts and imaginary parts, and jointly learning dictionaries of the real parts and the imaginary parts based on a K-SVD algorithm so as to obtain three dictionaries, wherein the three dictionaries are obtained, and not only amplitude spectrum information of the signals but also phase information of the signals are learned.
The preferred embodiment of the training phase is as follows:
training stage, the sound of knocking the complete encaustic tiles, the damaged encaustic tiles and the cracked encaustic tiles
Figure BDA0002459627780000033
And
Figure BDA0002459627780000034
obtaining sound complex spectrum of complete encaustic tiles on time-frequency domain through short-time Fourier transform
Figure BDA0002459627780000035
Sound complex spectrum of damaged encaustic tile
Figure BDA0002459627780000036
And crackle colorTile sound complex spectrum
Figure BDA0002459627780000037
Learning the real part and the imaginary part of the three sound complex spectrums respectively based on a K-SVD algorithm to obtain corresponding dictionaries
Figure BDA0002459627780000038
And
Figure BDA0002459627780000039
Figure BDA00024596277800000310
Figure BDA00024596277800000311
Figure BDA00024596277800000312
wherein the content of the first and second substances,
Figure BDA00024596277800000313
and
Figure BDA00024596277800000314
are respectively
Figure BDA00024596277800000315
Real and imaginary parts of, Dr1And Di1Real and imaginary dictionaries, respectively;
Figure BDA00024596277800000316
and
Figure BDA00024596277800000317
are respectively
Figure BDA00024596277800000318
Real and imaginary parts of, Dr2And Di2Real and imaginary dictionaries, respectively;
Figure BDA00024596277800000319
and
Figure BDA00024596277800000320
are respectively
Figure BDA00024596277800000321
Real and imaginary parts of, Dr3And Di3Real and imaginary dictionaries, respectively; c1Is a sparse representation coefficient, C, of the sound signal for a complete encaustic tile2Is a sparse representation coefficient, C, of the sound signal of a broken encaustic tile3Is a sparse representation coefficient of the sound signal for the crack encaustic tile; sparse representation coefficients of the sound signal in matrix form, c1,g、c2,gAnd c3,gAre respectively C1、C2And C3Q is a sparse constraint;
Figure BDA00024596277800000322
represented by the Frobenius norm, | · | |. luminance1Representative is the 1-norm.
And 3, in the detection stage, recording the knocked sound of the encaustic tiles to be detected, respectively projecting the sound on the three dictionaries, reconstructing corresponding sound signals according to each sparse representation coefficient obtained by projection, and judging the category of the encaustic tiles to be detected according to the reconstruction error.
The preferred embodiment of the detection stage is as follows:
in the detection stage, the sound of the knocked encaustic tile to be detected is subjected to short-time Fourier transform to calculate a corresponding complex spectrum SteGet its real part
Figure BDA0002459627780000041
And imaginary part
Figure BDA0002459627780000042
Respectively projected to three dictionaries
Figure BDA0002459627780000043
And
Figure BDA0002459627780000044
and calculating sparse representation coefficients respectively:
Figure BDA0002459627780000045
Figure BDA0002459627780000046
Figure BDA0002459627780000047
wherein D isr1And Di1Dictionaries, D, representing the real and imaginary parts of the complex spectrum of a sound in which a complete encaustic tile has been struckr2And Di2Dictionaries, D, representing real and imaginary parts of complex spectrum of sound with broken tiles struckr3And Di3Dictionaries respectively representing the real and imaginary parts of the complex spectrum of a sound struck by a crackle colortile, E1、E2And E3Respectively candidate sparse representation coefficients projected onto three dictionaries,
Figure BDA0002459627780000048
and
Figure BDA0002459627780000049
respectively selecting optimal sparse representation coefficients from the candidate sparse representation coefficients by the formula; sparse representation of coefficients in matrix form, e1,g、e2,gAnd e3,gAre respectively E1、E2And E3Q is a sparse constraint;
and calculating the real part and the imaginary part of the signal complex spectrum reconstructed by each dictionary by using the sparse representation coefficient, as shown in the following formula:
Figure BDA00024596277800000410
Figure BDA00024596277800000411
Figure BDA00024596277800000412
wherein the content of the first and second substances,
Figure BDA00024596277800000413
and
Figure BDA00024596277800000414
separately representing using sparse representation coefficients
Figure BDA00024596277800000415
And dictionary
Figure BDA00024596277800000416
Real and imaginary parts of a reconstructed signal complex spectrum;
Figure BDA00024596277800000417
and
Figure BDA00024596277800000418
separately representing using sparse representation coefficients
Figure BDA00024596277800000419
And dictionary
Figure BDA00024596277800000420
Real and imaginary parts of a reconstructed signal complex spectrum;
Figure BDA00024596277800000421
and
Figure BDA00024596277800000422
separately representing using sparse representation coefficients
Figure BDA00024596277800000423
Chinese character' HeDian (Chinese character)
Figure BDA00024596277800000424
Real and imaginary parts of a reconstructed signal complex spectrum;
calculating the reconstruction error of the sound signal:
Figure BDA0002459627780000051
Figure BDA0002459627780000052
Figure BDA0002459627780000053
judging the type of the encaustic tile to be detected according to the size of the reconstruction error, if the reconstruction error is large1Minimum, sound dictionary indicating complete encaustic tile
Figure BDA0002459627780000054
The reconstruction error of the sound signal of the detected encaustic tile is minimum, and the sparse representation accuracy is highest, so that the encaustic tile to be detected is judged to belong to the complete encaustic tile class; if the reconstruction error is2Sound dictionary for indicating damaged encaustic tile when minimum
Figure BDA0002459627780000055
The reconstruction error of the sound signal of the detected encaustic tile is minimum, and the sparse representation accuracy is highest, so that the encaustic tile to be detected is judged to belong to the damaged encaustic tile; if the reconstruction error is3Sound dictionary of encaustic tile indicating crack if minimum
Figure BDA0002459627780000056
The reconstruction error of the sound signal of the detected encaustic tile is minimum, and the sparse representation accuracy is highest, so that the encaustic tile to be detected belongs to the category of crack encaustic tiles.
According to the scheme of the embodiment of the invention, a training method of combined dictionary learning is adopted firstly, and a real part dictionary and an imaginary part dictionary of a knocked sound signal of three types of encaustic tiles, namely complete encaustic tiles, damaged encaustic tiles and cracked encaustic tiles are learned according to the environment used by the method. In the detection stage, the sound signal of the encaustic tile to be detected is subjected to short-time Fourier transform to obtain a corresponding real part and an imaginary part, the real part and the imaginary part are respectively projected to three types of dictionaries to reconstruct the real part and the imaginary part of the signal, and the type of the encaustic tile to be detected is judged according to the reconstructed error. Therefore, the method not only utilizes the amplitude information of the sound signal, but also utilizes the phase information of the signal, thereby maintaining better detection capability, and the training data is easy to obtain and convenient to realize in the industry.
Through the above description of the embodiments, it is clear to those skilled in the art that the above embodiments can be implemented by software, and can also be implemented by software plus a necessary general hardware platform. With this understanding, the technical solutions of the embodiments can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.), and includes several instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods according to the embodiments of the present invention.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (3)

1. A encaustic tile quality detection method is characterized by comprising the following steps:
recording the knocked sound of the complete encaustic tile, the damaged encaustic tile and the crack encaustic tile respectively, and constructing three sound signal training sets of the complete encaustic tile, the damaged encaustic tile and the crack encaustic tile;
in the training stage, short-time Fourier transform is respectively carried out on three sound signal training sets, corresponding real parts and imaginary parts are extracted, dictionaries of the real parts and the imaginary parts are jointly learned based on a K-SVD algorithm, and therefore three dictionaries are obtained;
and in the detection stage, recording the knocked sound of the encaustic tiles to be detected, respectively projecting the sound on the three dictionaries, reconstructing corresponding sound signals according to each sparse representation coefficient obtained by projection, and judging the category of the encaustic tiles to be detected according to the reconstruction error.
2. The encaustic tile quality detection method according to claim 1,
training stage, the sound of knocking the complete encaustic tiles, the damaged encaustic tiles and the cracked encaustic tiles
Figure FDA0002459627770000011
And
Figure FDA0002459627770000012
obtaining sound complex spectrum of complete encaustic tiles on time-frequency domain through short-time Fourier transform
Figure FDA0002459627770000013
Sound complex spectrum of damaged encaustic tile
Figure FDA0002459627770000014
And crackle colour tile sound complex spectrum
Figure FDA0002459627770000015
Learning the real part and the imaginary part of the three sound complex spectrums respectively based on a K-SVD algorithm to obtain corresponding dictionaries
Figure FDA0002459627770000016
And
Figure FDA0002459627770000017
Figure FDA0002459627770000018
Figure FDA0002459627770000019
Figure FDA00024596277700000110
wherein the content of the first and second substances,
Figure FDA00024596277700000111
and
Figure FDA00024596277700000112
are respectively
Figure FDA00024596277700000113
Real and imaginary parts of, Dr1And Di1Real and imaginary dictionaries, respectively;
Figure FDA00024596277700000114
and
Figure FDA00024596277700000115
are respectively
Figure FDA00024596277700000116
Real and imaginary parts of, Dr2And Di2Real and imaginary dictionaries, respectively;
Figure FDA00024596277700000117
and
Figure FDA00024596277700000118
are respectively
Figure FDA00024596277700000119
Real and imaginary parts of, Dr3And Di3Are respectively the real part andan imaginary part dictionary; c1Is a sparse representation coefficient, C, of the sound signal for a complete encaustic tile2Is a sparse representation coefficient, C, of the sound signal of a broken encaustic tile3Is a sparse representation coefficient of the sound signal for the crack encaustic tile; sparse representation coefficients of the sound signal in matrix form, c1,g、c2,gAnd c3,gAre respectively C1、C2And C3Q is a sparse constraint;
Figure FDA00024596277700000120
represented by the Frobenius norm, | · | |. luminance1Representative is the 1-norm.
3. The encaustic tile quality detection method according to claim 1 or 2,
in the detection stage, the sound of the knocked encaustic tile to be detected is subjected to short-time Fourier transform to calculate a corresponding complex spectrum SteGet its real part
Figure FDA0002459627770000021
And imaginary part
Figure FDA0002459627770000022
Respectively projected to three dictionaries
Figure FDA0002459627770000023
And
Figure FDA0002459627770000024
and calculating sparse representation coefficients respectively:
Figure FDA0002459627770000025
Figure FDA0002459627770000026
Figure FDA0002459627770000027
wherein D isr1And Di1Dictionaries, D, representing the real and imaginary parts of the complex spectrum of a sound in which a complete encaustic tile has been struckr2And Di2Dictionaries, D, representing real and imaginary parts of complex spectrum of sound with broken tiles struckr3And Di3Dictionaries respectively representing the real and imaginary parts of the complex spectrum of a sound struck by a crackle colortile, E1、E2And E3Respectively candidate sparse representation coefficients projected onto three dictionaries,
Figure FDA0002459627770000028
and
Figure FDA0002459627770000029
respectively selecting optimal sparse representation coefficients from the candidate sparse representation coefficients by the formula; sparse representation of coefficients in matrix form, e1,g、e2,gAnd e3,gAre respectively E1、E2And E3Q is a sparse constraint;
and calculating the real part and the imaginary part of the signal complex spectrum reconstructed by each dictionary by using the sparse representation coefficient, as shown in the following formula:
Figure FDA00024596277700000210
Figure FDA00024596277700000211
Figure FDA00024596277700000212
wherein the content of the first and second substances,
Figure FDA00024596277700000213
and
Figure FDA00024596277700000214
separately representing using sparse representation coefficients
Figure FDA00024596277700000215
And dictionary
Figure FDA00024596277700000216
Real and imaginary parts of a reconstructed signal complex spectrum;
Figure FDA00024596277700000217
and
Figure FDA00024596277700000218
separately representing using sparse representation coefficients
Figure FDA00024596277700000219
And dictionary
Figure FDA00024596277700000220
Real and imaginary parts of a reconstructed signal complex spectrum;
Figure FDA00024596277700000221
and
Figure FDA00024596277700000222
separately representing using sparse representation coefficients
Figure FDA00024596277700000223
And dictionary
Figure FDA00024596277700000224
Real and imaginary parts of a reconstructed signal complex spectrum;
calculating the reconstruction error of the sound signal:
Figure FDA0002459627770000031
Figure FDA0002459627770000032
Figure FDA0002459627770000033
judging the type of the encaustic tile to be detected according to the size of the reconstruction error, if the reconstruction error is large1Minimum, sound dictionary indicating complete encaustic tile
Figure FDA0002459627770000034
The reconstruction error of the sound signal of the detected encaustic tile is minimum, and the sparse representation accuracy is highest, so that the encaustic tile to be detected is judged to belong to the complete encaustic tile class; if the reconstruction error is2Sound dictionary for indicating damaged encaustic tile when minimum
Figure FDA0002459627770000035
The reconstruction error of the sound signal of the detected encaustic tile is minimum, and the sparse representation accuracy is highest, so that the encaustic tile to be detected is judged to belong to the damaged encaustic tile; if the reconstruction error is3Sound dictionary of encaustic tile indicating crack if minimum
Figure FDA0002459627770000036
The reconstruction error of the sound signal of the detected encaustic tile is minimum, and the sparse representation accuracy is highest, so that the encaustic tile to be detected belongs to the category of crack encaustic tiles.
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