CN109978828A - A kind of track for a train quality discrimination method and system based on image procossing - Google Patents
A kind of track for a train quality discrimination method and system based on image procossing Download PDFInfo
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- CN109978828A CN109978828A CN201910142029.6A CN201910142029A CN109978828A CN 109978828 A CN109978828 A CN 109978828A CN 201910142029 A CN201910142029 A CN 201910142029A CN 109978828 A CN109978828 A CN 109978828A
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- 238000012850 discrimination method Methods 0.000 title claims abstract description 16
- 230000007547 defect Effects 0.000 claims abstract description 32
- 238000012545 processing Methods 0.000 claims abstract description 9
- 238000013528 artificial neural network Methods 0.000 claims abstract description 6
- 238000006243 chemical reaction Methods 0.000 claims abstract description 6
- 230000002708 enhancing effect Effects 0.000 claims abstract description 4
- 230000002146 bilateral effect Effects 0.000 claims description 7
- 238000001914 filtration Methods 0.000 claims description 7
- 239000000284 extract Substances 0.000 claims description 3
- 238000000605 extraction Methods 0.000 claims description 3
- 238000000280 densification Methods 0.000 claims description 2
- 238000004590 computer program Methods 0.000 claims 2
- 238000000034 method Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 5
- 238000005461 lubrication Methods 0.000 description 5
- 230000001050 lubricating effect Effects 0.000 description 3
- 208000037656 Respiratory Sounds Diseases 0.000 description 2
- 238000005299 abrasion Methods 0.000 description 2
- 238000011835 investigation Methods 0.000 description 2
- 230000003137 locomotive effect Effects 0.000 description 2
- 229910000831 Steel Inorganic materials 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000004364 calculation method Methods 0.000 description 1
- 238000005265 energy consumption Methods 0.000 description 1
- 238000003912 environmental pollution Methods 0.000 description 1
- 230000003628 erosive effect Effects 0.000 description 1
- 230000002045 lasting effect Effects 0.000 description 1
- 239000010687 lubricating oil Substances 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 210000005036 nerve Anatomy 0.000 description 1
- 239000003921 oil Substances 0.000 description 1
- 238000010422 painting Methods 0.000 description 1
- 230000002035 prolonged effect Effects 0.000 description 1
- 231100000241 scar Toxicity 0.000 description 1
- 239000010959 steel Substances 0.000 description 1
- 230000036561 sun exposure Effects 0.000 description 1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T5/00—Image enhancement or restoration
- G06T5/70—Denoising; Smoothing
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/136—Segmentation; Edge detection involving thresholding
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30168—Image quality inspection
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Abstract
The track for a train quality discrimination method and system based on image procossing that the invention discloses a kind of, comprising the following steps: track for a train surface image information is acquired using array camera；Acquired image information is pre-processed, the pretreatment includes image denoising processing and edge crack enhancing processing；Pretreated image carries out global binary conversion treatment, determines the gray threshold T of crack area, the pixel of target area and background area is partitioned into according to the threshold value T；It determines crack area pixel number, comparison result is divided into rail level, normally defect image identification is carried out by BP neural network and is classified with two class of rail level defect, exported according to the variation of different characteristic value and classification is made to rail level defect.
Description
Technical field
The present invention relates to track for a train quality discrimination technical field more particularly to a kind of track for a trains based on image procossing
Quality discrimination method and system.
Background technique
Only there is provided background technical informations related to the present invention for the statement of this part, it is not necessary to so constitute first skill
Art.
It is well known that traveling of the train in track corner is the friction by wheel track and rail to realize, with when
Between passage, the abrasion between train wheel and rail also gets worse, and undoubtedly this is a kind of significant losses to steel, modern
In rail vehicle transportation field, the situational relationship of wheel track to locomotive traction energy consumption, traffic safety, wheel rail material consumption and is tieed up
Accomplish this etc., rationally effective wheel-rail lubricating can reduce wheel-rail wear and tear, and the safety of train driving not only can be improved, can be with
Energy saving, raising locomotive traction rate.Therefore, carrying out periodically painting oil lubrication in track for a train corner is necessary.
Although train rail wear problem can be solved by lubricating, when carrying out rail lubrication still is one
Urgent problem to be solved.Inventors have found that lubrication time is too early during lubricating rail, lubricating oil can be not only wasted, also
Environmental pollution can be caused to a certain extent；Lubrication time can cause to be lost to the service life of rail again too late, and causing need not
The wasting of resources wanted.In addition, rail abrasion is also influenced by human factor and natural cause, and such as: burning sun exposure is artificially broken
Bad and rain erosion etc., therefore the suitable rail lubrication time is selected to be necessary.
Summary of the invention
The purpose of the present invention is to solve the above problem, proposes a kind of track for a train quality based on image procossing and sentence
Other method and system are classified by situation of the intellectual analysis to rail, judge whether rail needs to lubricate, or are
Damage is needed repairing or is replaced.
To achieve the goals above, the present invention adopts the following technical scheme:
A kind of track for a train quality discrimination method based on image procossing disclosed in one or more embodiments, packet
Include following steps:
Track for a train surface image information is acquired using array camera；
Acquired image information is pre-processed, the pretreatment includes that image denoising processing and edge crack increase
Strength reason；
Pretreated image carries out global binary conversion treatment, the gray threshold T of crack area is determined, according to the threshold value
T is partitioned into the pixel of target area and background area；
It determines crack area pixel number, comparison result is divided into rail level is normal and two class of rail level defect；
By bilateral filtering treated global binary image, eight characteristic values for extracting rail level defect are respectively as follows: matter
The Chang ﹑ Mian Ji ﹑ Chang Zhou ﹑ Duan Zhou ﹑ Ju Xing Du ﹑ densification Du ﹑ length-width ratio of Xin ﹑ week；Then defect image knowledge is carried out by BP neural network
Do not classify, is exported according to the variation of different characteristic value and classification is made to rail level defect.
Further, described image denoising specifically: the image of acquisition is carried out at denoising using two-sided filter
Reason, obtains accurate image detail information.
Further, the image after filter process is subjected to global binary conversion treatment, from by from bilateral filtering
In global binary image after reason, extracts and use eight characteristic indexs as the characteristic item for measuring rail level defect, be respectively as follows:
The Chang ﹑ Mian Ji ﹑ Chang Zhou ﹑ Duan Zhou ﹑ Ju Xing Du ﹑ consistency of Zhi Xin ﹑ week and length-width ratio；With eight characteristic value ratios of the normal rail level of extraction
Compared with, when characteristic value error exceed safe range when be judged to rail level defect occur.
Further, passed through according to eight characteristic value indexs from image zooming-out as the characteristic value for measuring rail level defect
It makes comparisons with normal orbit characteristic value, when finding certain characteristic values beyond safe range, the characteristic value of input will pass through BP nerve
Network carries out defect image identification classification, finds similar characteristic value to judge rail level defect type.
Further, when wheel is greater than given threshold by the number of track, determine that track needs are lubricated processing；
When defect occurs in raceway surface, carries out position section track and report for repairment.
A kind of track for a train quality discrimination system based on image procossing disclosed in one or more embodiments, packet
Include server, the server include memory, processor and storage on a memory and the calculating that can run on a processor
Machine program, the processor realize the above-mentioned track for a train quality discrimination method based on image procossing when executing described program.
A kind of computer readable storage medium disclosed in one or more embodiments, is stored thereon with computer journey
Sequence, the program execute the above-mentioned track for a train quality discrimination method based on image procossing when being executed by processor.
Compared with prior art, the beneficial effects of the present invention are:
Using the intellectual analysis mode of image procossing, classify to the situation of rail, judge whether rail needs to lubricate,
It or is to have damaged to need repairing or replace.
By image processing techniques realize rail state automatic identification and judgement, as a result accurately, it is high-efficient.
Detailed description of the invention
The accompanying drawings constituting a part of this application is used to provide further understanding of the present application, and the application's shows
Meaning property embodiment and its explanation are not constituted an undue limitation on the present application for explaining the application.
Fig. 1 is the track for a train quality discrimination method schematic diagram based on image procossing in embodiment one；
Fig. 2 is in embodiment one by bilateral filtering treated global binary image schematic diagram；
Fig. 3 is target area boundaries minimum circumscribed rectangle schematic diagram in embodiment one.
Specific embodiment
It is noted that described further below be all exemplary, it is intended to provide further instruction to the application.Unless another
It indicates, all technical and scientific terms that the present invention uses have logical with the application person of an ordinary skill in the technical field
The identical meanings understood.
It should be noted that term used herein above is merely to describe specific embodiment, and be not intended to restricted root
According to the illustrative embodiments of the application.As used herein, unless the context clearly indicates otherwise, otherwise singular
Also it is intended to include plural form, additionally, it should be understood that, when in the present specification using term "comprising" and/or " packet
Include " when, indicate existing characteristics, step, operation, device, component and/or their combination.
Embodiment one
In one or more embodiments, a kind of track for a train quality discrimination method based on image procossing is disclosed,
As shown in Figure 1, comprising the following steps:
Track for a train surface image information is acquired using array camera；
Acquired image information is pre-processed, pretreatment includes at image denoising processing and edge crack enhancing
Reason；
The image of acquisition is filtered, two-sided filter denoising, local gray-value changes lesser region and obtains
Enhancing.After two-sided filter, accurate image detail information is obtained.
Global binary conversion treatment will be carried out through the pretreated image of wave filter, find the gray threshold T of crack area,
It is partitioned into the pixel of target area and background area according to threshold value T, calculates pixel number, comparison result is divided into rail level
Normal and two class of rail level defect.Fig. 2 gives by bilateral filtering treated global binary image schematic diagram.
In order to describe the geometric dimension and shape of rail level defect, from by the bilateral filtering global binary picture that treated
As in, target area minimum circumscribed rectangle is determined, and then extract and use eight characteristic indexs as the feature for measuring rail level defect
, it is respectively as follows: mass center, perimeter, area, long axis, short axle, rectangular degree, consistency and length-width ratio；With the eight of the normal rail level of extraction
A characteristic value compares, and is judged to rail level defect occur when characteristic value error exceeds safe range.Fig. 3 gives target area side
Boundary's minimum circumscribed rectangle schematic diagram.
Table 1 gives a certain target area railroad flaws characteristic parameter
According to on-the-spot investigation, investigation, it has been found that at present the common surface defect of rail mainly have crackle, scratch, removing,
The states such as fold, broken rail.Wherein crackle and scar is relatively common and the defect type of especially severe.It is prolonged in train
It rolls down, it is easy to cause rail level to remove chip off-falling, and impact of the train to rail can be further increased by removing chip off-falling in turn
Power causes rail damage to aggravate, and repeatedly, forms a vicious circle circle.Under the influence of this lasting vicious circle, tire out
Product will jeopardize traffic safety to a certain extent.
Therefore, rail state is divided by we: level-one damage: face crack, secondary damage: galled spots, three-level damage:
Rail surface removing, level Four damage: rail break；
According to eight characteristic value indexs from image zooming-out as measure rail level defect characteristic value, by with normal orbit
Characteristic value is made comparisons, and when finding certain characteristic values beyond safe range, the characteristic value of input will be carried out scarce by BP neural network
Image recognition classification is fallen into, finds similar characteristic value to judge rail level defect type.
It should be noted that determining the Chang ﹑ Mian Ji ﹑ Chang Zhou ﹑ Duan Zhou ﹑ Ju Xing Du ﹑ of Zhi Xin ﹑ week according to minimum circumscribed rectangle method
Consistency and length-width ratio, and carry out defect image identification classification using BP neural network are all those skilled in the art according to
What the prior art can be realized, therefore do not make that explanation is unfolded.
Embodiment two
In one or more embodiments, a kind of track for a train quality discrimination system based on image procossing is disclosed,
Including server, the server include memory, processor and storage on a memory and the meter that can run on a processor
Calculation machine program, the processor realize the track for a train matter based on image procossing described in embodiment one when executing described program
Measure method of discrimination.
Embodiment three
In one or more embodiments, a kind of computer readable storage medium is disclosed, computer is stored thereon with
Program executes the track for a train quality discrimination side based on image procossing described in embodiment one when the program is executed by processor
Method.
Above-mentioned, although the foregoing specific embodiments of the present invention is described with reference to the accompanying drawings, not protects model to the present invention
The limitation enclosed, those skilled in the art should understand that, based on the technical solutions of the present invention, those skilled in the art are not
Need to make the creative labor the various modifications or changes that can be made still within protection scope of the present invention.
Claims (7)
1. a kind of track for a train quality discrimination method based on image procossing, which comprises the following steps:
Track for a train surface image information is acquired using array camera；
Acquired image information is pre-processed, the pretreatment includes at image denoising processing and edge crack enhancing
Reason；
Pretreated image carries out global binary conversion treatment, determines the gray threshold T of crack area, according to the threshold value T points
Cut out the pixel of target area and background area；
It determines crack area pixel number, comparison result is divided into rail level is normal and two class of rail level defect；
By bilateral filtering treated global binary image, eight characteristic values for extracting rail level defect are respectively as follows: Zhi Xin ﹑ week
Chang ﹑ Mian Ji ﹑ Chang Zhou ﹑ Duan Zhou ﹑ Ju Xing Du ﹑ densification Du ﹑ length-width ratio；Then defect image identification point is carried out by BP neural network
Class is exported according to the variation of different characteristic value and makes classification to rail level defect.
2. a kind of track for a train quality discrimination method based on image procossing as described in claim 1, which is characterized in that described
Image denoising processing specifically: denoising is carried out using two-sided filter to the image of acquisition, obtains accurate image detail
Information.
3. a kind of track for a train quality discrimination method based on image procossing as described in claim 1, which is characterized in that will be through
Wave filter treated image carries out global binary conversion treatment, from passing through the bilateral filtering global binary image that treated
In, it extracts and uses eight characteristic indexs as the characteristic item for measuring rail level defect, it is short to be respectively as follows: the Chang ﹑ Mian Ji ﹑ Chang Zhou ﹑ of Zhi Xin ﹑ week
Zhou ﹑ Ju Xing Du ﹑ consistency and length-width ratio；Compared with eight characteristic values of the normal rail level of extraction, when characteristic value error is beyond peace
It is determined as rail level defect occur when gamut.
4. a kind of track for a train quality discrimination method based on image procossing as described in claim 1, which is characterized in that according to
From eight characteristic value indexs of image zooming-out as the characteristic value for measuring rail level defect, by making ratio with normal orbit characteristic value
Compared with when finding certain characteristic values beyond safe range, the characteristic value of input will carry out defect image identification by BP neural network
Classification finds similar characteristic value to judge rail level defect type.
5. a kind of track for a train quality discrimination method based on image procossing as described in claim 1, which is characterized in that work as vehicle
When wheel is greater than given threshold by the number of track, determine that track needs are lubricated processing；When there is defect in raceway surface,
Position section track is carried out to report for repairment.
6. a kind of track for a train quality discrimination system based on image procossing, which is characterized in that including server, the server
Including memory, processor and the computer program that can be run on a memory and on a processor is stored, the processor is held
Claim 1-5 described in any item track for a train quality discrimination methods based on image procossing are realized when row described program.
7. a kind of computer readable storage medium, is stored thereon with computer program, which is characterized in that the program is held by processor
Perform claim requires the described in any item track for a train quality discrimination methods based on image procossing of 1-5 when row.
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| Application Number | Priority Date | Filing Date | Title |
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| CN201910142029.6A CN109978828A (en) | 2019-02-26 | 2019-02-26 | A kind of track for a train quality discrimination method and system based on image procossing |
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| CN201910142029.6A CN109978828A (en) | 2019-02-26 | 2019-02-26 | A kind of track for a train quality discrimination method and system based on image procossing |
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Cited By (3)
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| CN111127344A (en) * | 2019-12-06 | 2020-05-08 | 昆明理工大学 | Self-adaptive bilateral filtering ultrasound image noise reduction method based on BP neural network |
| CN111879776A (en) * | 2020-06-19 | 2020-11-03 | 山东师范大学 | Fault detection method and system for rail side lubricating device |
| CN114972117A (en) * | 2022-06-30 | 2022-08-30 | 成都理工大学 | Track surface wear identification and classification method and system |
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111127344A (en) * | 2019-12-06 | 2020-05-08 | 昆明理工大学 | Self-adaptive bilateral filtering ultrasound image noise reduction method based on BP neural network |
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| CN114972117A (en) * | 2022-06-30 | 2022-08-30 | 成都理工大学 | Track surface wear identification and classification method and system |
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Application publication date: 20190705 |