CN107527067A - A kind of Railway Roadbed intelligent identification Method based on GPR - Google Patents

A kind of Railway Roadbed intelligent identification Method based on GPR Download PDF

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Publication number
CN107527067A
CN107527067A CN201710648746.7A CN201710648746A CN107527067A CN 107527067 A CN107527067 A CN 107527067A CN 201710648746 A CN201710648746 A CN 201710648746A CN 107527067 A CN107527067 A CN 107527067A
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railway
identification
gpr
dimension
dimensionality reduction
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CN107527067B (en
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杜翠
张千里
刘杰
韩自力
蔡德钩
马伟斌
陈锋
程远水
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China Academy of Railway Sciences Corp Ltd CARS
Railway Engineering Research Institute of CARS
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Railway Engineering Research Institute of CARS
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06F—ELECTRIC DIGITAL DATA PROCESSING
    • G06F18/00—Pattern recognition
    • G06F18/20—Analysing
    • G06F18/24—Classification techniques
    • G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
    • G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01S—RADIO DIRECTION-FINDING; RADIO NAVIGATION; DETERMINING DISTANCE OR VELOCITY BY USE OF RADIO WAVES; LOCATING OR PRESENCE-DETECTING BY USE OF THE REFLECTION OR RERADIATION OF RADIO WAVES; ANALOGOUS ARRANGEMENTS USING OTHER WAVES
    • G01S13/00—Systems using the reflection or reradiation of radio waves, e.g. radar systems; Analogous systems using reflection or reradiation of waves whose nature or wavelength is irrelevant or unspecified
    • G01S13/88—Radar or analogous systems specially adapted for specific applications
    • G01S13/885—Radar or analogous systems specially adapted for specific applications for ground probing
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/46—Descriptors for shape, contour or point-related descriptors, e.g. scale invariant feature transform [SIFT] or bags of words [BoW]; Salient regional features
    • G06V10/462—Salient features, e.g. scale invariant feature transforms [SIFT]
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/40—Extraction of image or video features
    • G06V10/50—Extraction of image or video features by performing operations within image blocks; by using histograms, e.g. histogram of oriented gradients [HoG]; by summing image-intensity values; Projection analysis
    • G06V10/507—Summing image-intensity values; Histogram projection analysis

Abstract

The present invention relates to a kind of Railway Roadbed intelligent identification Method based on GPR, belong to inspection of railway subgrade technical field.The step of realizing of this method is：Establish Railway Roadbed Intelligent Recognition software systems, including Coherent Noise in GPR Record collection, pretreatment, two-dimensional discrete, feature extraction, Feature Dimension Reduction and structure identification model；Railway Roadbed identification is carried out using the Intelligent Recognition software of foundation.The present invention instead of artificial interpretation Coherent Noise in GPR Record, can be achieved quick, accurate, the lossless Intelligent Recognition of a variety of Railway Roadbeds, lifted the ageing of GPR detection, promote the intellectuality of inspection of railway subgrade using machine vision, mode identification technology.

Description

A kind of Railway Roadbed intelligent identification Method based on GPR
Technical field
The invention belongs to inspection of railway subgrade technical field, is related to a kind of Railway Roadbed intelligence based on GPR Recognition methods.
Background technology
The evaluation of railway bed state-detection is the key link of railway maintenance maintenance.At present in detection means, GPR （GPR）It is a kind of ideal detection method.But GPR data process and interpretations rely primarily on artificial interpretation both at home and abroad at present, Efficiency is low, poor in timeliness.Therefore, a kind of fast and accurately railway bed GPR data processing methods are established, are China railways roads Urgent problem to be solved in base detection.
From 2008, start the research for a small amount of Railway Roadbed intelligent identification Method occur.Research is more with railway at present Professional is the technical background such as main body, machine vision, pattern-recognition weakness, and the Damage Types of consideration are less, and do not examine Consider the main units such as track switch, bridge；Using one-dimensional single track radar data as recognition unit, it is segmented along depth direction, extraction one The radar signal characteristic value of dimension；The accuracy rate for finally carrying out pattern-recognition is not high, it is difficult to puts into actual engineer applied.
The content of the invention
It is an object of the invention to provide a kind of Railway Roadbed intelligent identification Method based on GPR, realizes fast Fast, accurate, lossless Railway Roadbed identification.
The present invention solves the technical scheme that its technical problem uses：A kind of Railway Roadbed intelligence based on GPR Energy recognition methods, is specifically included：
（1）Establish Railway Roadbed Intelligent Recognition software systems
a）Normal railway bed, the railway bed comprising different type subgrade defect, railroad bridge, road are detected using GPR Trouble, preserve detection data；
b）Pretreatment：Detection data are subjected to zero line correction, are converted to gray level image；
c）Two-dimensional discrete：The ground penetrating radar image of acquisition is divided into several recognition units along mileage direction, each identification is single Member include 50 ~ 150 track datas, then each recognition unit is divided into several along depth direction and identifies subelement, and adjacent two Individual identification subelement has 50% overlapping region；
d）Feature extraction：Various features value, all identification subelements of each recognition unit are extracted in units of identifying subelement Characteristic value form the characteristic vector of the recognition unit, original dimensions M=identification subelement number × characteristic value of characteristic vector Number；
e）Feature Dimension Reduction：Determine dimensionality reduction dimension N, using principal component analysis to characteristic vector carry out dimensionality reduction, structure low-dimensional feature to Amount；
f）Build identification model：Support vector machine classifier is established, low-dimensional characteristic vector is input in grader, trains this point Class device, build the Railway Roadbed intelligent recognition model based on GPR；
（2）Carry out Railway Roadbed identification
Railway bed to be identified is detected using GPR, preserves detection data；Carried out by the Intelligent Recognition software established pre- Processing, two-dimensional discrete and feature extraction, Feature Dimension Reduction is carried out with dimensionality reduction dimension N；The section of railway track roadbed is entered using identification model Row identification, obtain the roadbed type of each recognition unit of section of railway track roadbed.
Described subgrade defect includes rising soil, ballast contamination, sinking, aqueous, empty.
Described characteristic value includes the signal characteristic of radar image, such as energy, variance；Histogram statistical features, as average, Standard variance, smoothness, third moment, uniformity, entropy.
Described determination dimensionality reduction dimension N detailed process is as follows：
（1）A series of dimension values are set in the range of 8 ~ M, principal component analysis is utilized respectively and dimensionality reduction is carried out to characteristic vector, often The data set after one group of dimensionality reduction is obtained under individual dimension values；
（2）It is utilized respectively the Data set reconstruction raw data set after each group dimensionality reduction, calculates reconstructed data set and raw data set Root-mean-square error；
（3）In dimension values of the root-mean-square error less than 0.5%, the dimension values of minimum are selected as dimensionality reduction dimension N.
Beneficial effect
Compared with background technology, the invention has the advantages that：
（1）Using machine vision, mode identification technology, quick, accurate, the lossless intelligence of a variety of Railway Roadbeds can be achieved Identification；
（2）Polytype two dimensional character such as two-dimensional discrete, extraction signal characteristic, histogram statistical features is carried out to GPR images Value, optimize the character representation of subgrade defect so that discrimination significantly improves；
（3）Dimensionality reduction dimension is determined using the root-mean-square error of reconstructed data set and raw data set, both can guarantee that low after dimensionality reduction Dimensional feature vector reduces data redudancy, lifts recognition efficiency again to the sign performance of raw data set.
Brief description of the drawings
The software systems that Fig. 1 is the present invention build flow chart.
Fig. 2 is the two-dimensional discrete process schematic of the present invention.
Embodiment
The invention will be further described with embodiment below in conjunction with the accompanying drawings.
A kind of Railway Roadbed intelligent identification Method based on GPR, including establish Railway Roadbed and intelligently know Other software systems, progress Railway Roadbed identify two parts.
First, establish Railway Roadbed Intelligent Recognition software systems.
（1）Normal railway bed, the railway bed comprising different type subgrade defect, railway bridge are detected using GPR Beam, track switch, preserve detection data；Subgrade defect includes rising soil, ballast contamination, sinking, aqueous, empty；
（2）Pretreatment：Detection data are subjected to zero line correction, are converted to gray level image；
（3）Two-dimensional discrete：As shown in Fig. 2 the ground penetrating radar image A of acquisition is divided into several recognition units along mileage direction {A1, A2..., Ak, each recognition unit includes 50 ~ 150 track datas, if then being divided into each recognition unit along depth direction Dry identification subelement, adjacent two identification subelements have 50% overlapping region, as shown in Fig. 2 recognition unit A2It is divided into { A21, A22, A23, A24...；
（4）Feature extraction：Various features value is extracted in units of identifying subelement, includes the signal characteristic of radar image, if Amount, variance；Histogram statistical features, such as average, standard variance, smoothness, third moment, uniformity, entropy；Each recognition unit The characteristic values of all identification subelements form the characteristic vector of the recognition unit, the original dimensions M of characteristic vector=for identification it is single First number × characteristic value number；
（5）Feature Dimension Reduction：Different dimension values { D is set in the range of 8 ~ M1, D2..., Dk, it is utilized respectively principal component analysis Dimensionality reduction is carried out to characteristic vector, the data set after one group of dimensionality reduction is obtained under each dimension values；The number being utilized respectively after each group dimensionality reduction Raw data set is reconstructed according to collection, calculates the root-mean-square error { E of reconstructed data set and raw data set1, E2..., Ek}；Equal In dimension values of the square error less than 0.5%, the dimension values of minimum are selected as dimensionality reduction dimension N；
（6）Build identification model：Support vector machine classifier is established, low-dimensional characteristic vector is input in grader, training should Grader, the Railway Roadbed intelligent recognition model based on GPR is built, it is soft to complete Railway Roadbed Intelligent Recognition The structure of part system.
Second, carry out Railway Roadbed identification.
（1）Railway bed to be identified is detected using GPR, preserves detection data；
（2）Pre-processed by the Intelligent Recognition software established, two-dimensional discrete, feature extraction；
（3）Feature Dimension Reduction is carried out with dimensionality reduction dimension N, builds low-dimensional characteristic vector；
（4）Low-dimensional characteristic vector is input in identification model, the section of railway track roadbed is identified using identification model, is obtained The roadbed type of each recognition unit of section of railway track roadbed.
Embodiment described above is only used for the present invention, rather than limitation of the present invention, person skilled in the relevant technique, In the case of not departing from the spirit and scope of the present invention, various conversion or modification, therefore all equivalent technologies can also be made Scheme should also belong to scope of the invention, should be limited by each claim.

Claims (4)

1. a kind of Railway Roadbed intelligent identification Method based on GPR, it is characterised in that comprise the following steps：
（1）Establish Railway Roadbed Intelligent Recognition software systems
a）Normal railway bed, the railway bed comprising different type subgrade defect, railroad bridge, road are detected using GPR Trouble, preserve detection data；
b）Pretreatment：Detection data are subjected to zero line correction, are converted to gray level image；
c）Two-dimensional discrete：The ground penetrating radar image of acquisition is divided into several recognition units along mileage direction, each identification is single Member include 50 ~ 150 track datas, then each recognition unit is divided into several along depth direction and identifies subelement, and adjacent two Individual identification subelement has 50% overlapping region；
d）Feature extraction：Various features value, all identification subelements of each recognition unit are extracted in units of identifying subelement Characteristic value form the characteristic vector of the recognition unit, original dimensions M=identification subelement number × characteristic value of characteristic vector Number；
e）Feature Dimension Reduction：Determine dimensionality reduction dimension N, using principal component analysis to characteristic vector carry out dimensionality reduction, structure low-dimensional feature to Amount；
f）Build identification model：Support vector machine classifier is established, low-dimensional characteristic vector is input in grader, trains this point Class device, build the Railway Roadbed intelligent recognition model based on GPR；
（2）Carry out Railway Roadbed identification
Railway bed to be identified is detected using GPR, preserves detection data；
Pre-processed by the Intelligent Recognition software established, two-dimensional discrete and feature extraction, feature drop is carried out with dimensionality reduction dimension N Dimension；
The section of railway track roadbed is identified using identification model, obtains the roadbed class of each recognition unit of section of railway track roadbed Type.
2. the method as described in claim 1, it is characterised in that described subgrade defect include rise soil, ballast contamination, under It is heavy, aqueous, empty.
3. the method as described in claim 1, it is characterised in that described characteristic value includes the signal characteristic of radar image, such as Energy, variance；Histogram statistical features, such as average, standard variance, smoothness, third moment, uniformity, entropy.
4. the method as described in claim 1, it is characterised in that described determination dimensionality reduction dimension N detailed process is as follows：
（1）A series of dimension values are set in the range of 8 ~ M, principal component analysis is utilized respectively and dimensionality reduction is carried out to characteristic vector, often The data set after one group of dimensionality reduction is obtained under individual dimension values；
（2）It is utilized respectively the Data set reconstruction raw data set after each group dimensionality reduction, calculates reconstructed data set and raw data set Root-mean-square error；
（3）In dimension values of the root-mean-square error less than 0.5%, the dimension values of minimum are selected as dimensionality reduction dimension N.
CN201710648746.7A 2017-08-01 2017-08-01 Railway roadbed disease intelligent identification method based on ground penetrating radar Active CN107527067B (en)

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CN108387894A (en) * 2018-04-13 2018-08-10 中南大学 The processing method of through-wall radar echo data
CN108612076A (en) * 2018-05-14 2018-10-02 西南交通大学 A kind of unit plate type non-fragment orbit railway sub-grade frost boiling method of discrimination
CN108759648A (en) * 2018-04-09 2018-11-06 中国科学院电子学研究所 Ground Penetrating Radar detection method based on machine learning
CN109034246A (en) * 2018-07-27 2018-12-18 中国矿业大学(北京) A kind of the determination method and determining system of roadbed saturation state
CN109031431A (en) * 2018-08-10 2018-12-18 中国铁道科学研究院集团有限公司铁道建筑研究所 A kind of data processing method and system for Coherent Noise in GPR Record
CN109029881A (en) * 2018-06-21 2018-12-18 中国铁道科学研究院铁道建筑研究所 A kind of ballast-bed state appraisal procedure detected based on orbit rigidity and Ground Penetrating Radar
CN109145424A (en) * 2018-08-10 2019-01-04 中国铁道科学研究院集团有限公司铁道建筑研究所 It is a kind of for the bridge data recognition methods of Coherent Noise in GPR Record and system
CN109190510A (en) * 2018-08-13 2019-01-11 中国矿业大学(北京) Underground cavity based on Ground Penetrating Radar quantifies recognition methods
CN109782274A (en) * 2019-01-31 2019-05-21 长安大学 A kind of Moisture Damage recognition methods based on Gpr Signal time-frequency statistical nature
CN110717464A (en) * 2019-10-15 2020-01-21 中国矿业大学(北京) Intelligent railway roadbed disease identification method based on radar data
CN112232392A (en) * 2020-09-29 2021-01-15 深圳安德空间技术有限公司 Data interpretation and identification method for three-dimensional ground penetrating radar
CN112257735A (en) * 2020-05-22 2021-01-22 广东省建设工程质量安全检测总站有限公司 Internal defect radar detection system based on artificial intelligence
CN112731381A (en) * 2020-12-16 2021-04-30 华南农业大学 Method for intelligently detecting hard foreign matters in soil by utilizing android debugging bridge and vehicle-mounted radar
CN113030867A (en) * 2021-03-12 2021-06-25 中国铁道科学研究院集团有限公司 Method and device for determining state of railway ballast bed
CN113064166A (en) * 2021-03-22 2021-07-02 石家庄铁道大学 Method and device for detecting thickness of thin layer defect of multilayer concrete structure and terminal
CN113389125A (en) * 2021-06-25 2021-09-14 华南理工大学 Urban road roadbed cavity detection and repair device and repair construction method

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Cited By (23)

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Publication number Priority date Publication date Assignee Title
CN108759648A (en) * 2018-04-09 2018-11-06 中国科学院电子学研究所 Ground Penetrating Radar detection method based on machine learning
CN108387894A (en) * 2018-04-13 2018-08-10 中南大学 The processing method of through-wall radar echo data
CN108387894B (en) * 2018-04-13 2021-07-27 中南大学 Processing method of through-wall radar echo data
CN108612076A (en) * 2018-05-14 2018-10-02 西南交通大学 A kind of unit plate type non-fragment orbit railway sub-grade frost boiling method of discrimination
CN109029881A (en) * 2018-06-21 2018-12-18 中国铁道科学研究院铁道建筑研究所 A kind of ballast-bed state appraisal procedure detected based on orbit rigidity and Ground Penetrating Radar
CN109034246A (en) * 2018-07-27 2018-12-18 中国矿业大学(北京) A kind of the determination method and determining system of roadbed saturation state
CN109145424A (en) * 2018-08-10 2019-01-04 中国铁道科学研究院集团有限公司铁道建筑研究所 It is a kind of for the bridge data recognition methods of Coherent Noise in GPR Record and system
CN109145424B (en) * 2018-08-10 2023-09-26 中国铁道科学研究院集团有限公司铁道建筑研究所 Bridge data identification method and system for ground penetrating radar data
CN109031431A (en) * 2018-08-10 2018-12-18 中国铁道科学研究院集团有限公司铁道建筑研究所 A kind of data processing method and system for Coherent Noise in GPR Record
CN109190510A (en) * 2018-08-13 2019-01-11 中国矿业大学(北京) Underground cavity based on Ground Penetrating Radar quantifies recognition methods
CN109782274A (en) * 2019-01-31 2019-05-21 长安大学 A kind of Moisture Damage recognition methods based on Gpr Signal time-frequency statistical nature
CN109782274B (en) * 2019-01-31 2023-05-16 长安大学 Water damage identification method based on time-frequency statistical characteristics of ground penetrating radar signals
CN110717464A (en) * 2019-10-15 2020-01-21 中国矿业大学(北京) Intelligent railway roadbed disease identification method based on radar data
CN110717464B (en) * 2019-10-15 2022-03-22 中国矿业大学(北京) Intelligent railway roadbed disease identification method based on radar data
CN112257735A (en) * 2020-05-22 2021-01-22 广东省建设工程质量安全检测总站有限公司 Internal defect radar detection system based on artificial intelligence
CN112232392B (en) * 2020-09-29 2022-03-22 深圳安德空间技术有限公司 Data interpretation and identification method for three-dimensional ground penetrating radar
CN112232392A (en) * 2020-09-29 2021-01-15 深圳安德空间技术有限公司 Data interpretation and identification method for three-dimensional ground penetrating radar
CN112731381A (en) * 2020-12-16 2021-04-30 华南农业大学 Method for intelligently detecting hard foreign matters in soil by utilizing android debugging bridge and vehicle-mounted radar
CN113030867A (en) * 2021-03-12 2021-06-25 中国铁道科学研究院集团有限公司 Method and device for determining state of railway ballast bed
CN113030867B (en) * 2021-03-12 2023-12-01 中国铁道科学研究院集团有限公司 Method and device for determining state of railway ballast bed
CN113064166A (en) * 2021-03-22 2021-07-02 石家庄铁道大学 Method and device for detecting thickness of thin layer defect of multilayer concrete structure and terminal
CN113064166B (en) * 2021-03-22 2023-01-06 石家庄铁道大学 Method and device for detecting thickness of thin layer defect of multilayer concrete structure and terminal
CN113389125A (en) * 2021-06-25 2021-09-14 华南理工大学 Urban road roadbed cavity detection and repair device and repair construction method

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