CN109816253A - A kind of defect analysis method based on bar number identification - Google Patents
A kind of defect analysis method based on bar number identification Download PDFInfo
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Abstract
The invention discloses a kind of defect analysis methods based on bar number identification, including S1: alert data, inspection data loading；S2: pillar unique positions calculate；S3: defect Multidimensional Comprehensive analysis.Image can be aligned using the bar recognition methods based on deep learning based on bar number, correct the position location 1C, 3C, 4C, 5C, 6C and identification 2C image bar number.Recognition efficiency is very high, can accurately identify the number in rod board, and bar board is avoided to fail to judge, and increases recall rate, improves efficiency and combine accuracy.Support the testing result comparative analysis of single detection device history dimension and different detection type (1C, 2C, 3C, 4C, 5C, 6C) all testing results of history when front standing pillar are associated check, the ability of comparative analysis, there is very high defect dipoles analysis validity and efficiency.
Description
Technical field
The present invention relates to the comprehensive analysis field of railway 6C data center more particularly to a kind of synthesis based on bar number identification
Defect analysis method.
Background technique
The defect of railway 6C data center is analyzed, and is needed 1C, 2C, 3C, 4C, 5C, 6C alarm data and hard disk number
According to based on pillar alignment, that is to say, that 1C-6C alarm data and hard disc data near some pillar will be put into the branch
Column correspondence database, main purpose be to provide various dimensions defect analysis, some power supply unit (any one equipment in 1C-6C)
It was found that defect, but since weather, camera angle or clarity etc. influence, it needs to provide detection data (figure by other C equipment
Picture, detected value), the authenticity of comprehensive analysis and judgement defect, influencing factor.It is also required to check the history testing number of same pillar simultaneously
According to, the variation tendency of defect is analyzed, to maintenance result check.
Comprehensive analysis is that 1C-6C image data needs to be aligned according to pillar there are difficult point, only same pillar image alignment
It just can be carried out comprehensive analysis, if comprehensive analysis can not be carried out with pillar image offset；When 1C, 3C, 4C, 5C, 6C acquire image
Image can be aligned based on pillar by GPS, LKJ equipment etc., if there is certain dislocation images (being based on pillar) need manually
Adjustment, and 2C equipment is not installed GPS, LKJ therefore can not be positioned, and needs manually to be aligned, consumes a large amount of man power and material.
Common bar recognition methods is similar to Car license recognition: the first step identifies rod board by target detection from image；
Second step identifies number by bar board.Since railway detection background is extremely complex, camera shooting area is big in addition, bar board
It is too small relative to whole image target, increase identification difficulty, often first step bar board identifies mistake, therefore can not
Rod number is accurately identified from image.
1C-6C bar number alignment after, carry out defect analysis, obtain three aspect contents: find positive true defect reason,
Development of defects trend is observed, is checked defect has been repaired.
Summary of the invention
To solve the above-mentioned problems, the present invention proposes a kind of railway bar recognition methods based on deep learning, using base
Image can be aligned in the bar number identification of deep learning based on bar number, correct the position location 1C, 3C, 4C, 5C, 6C and identification 2C
Image bar number.
A kind of defect analysis method based on pillar number identification, comprising the following steps:
S1: alert data, inspection data loading；
S2: pillar unique positions calculate；
S3: defect Multidimensional Comprehensive analysis.
The step S2 includes following sub-step:
S21: pillar image is extracted from original image；
S22: bar board image is extracted from pillar image；
S23: digital information is extracted from bar board image；
S24: pillar position location correction.
Preferably, S1 uses YoloV3 target detection network algorithm, and pillar image is obtained from original image.
Preferably, S2 extracts bar board image from pillar image, if using YoloV3 target detection network algorithm from branch
Rod board is detected in column image, there are more wrong report, main cause is bar board relative to whole image very little, that is, is directed to
This Small object YoloV3 target detection network algorithm of bar board is lower in the accuracy of detection, it is therefore desirable to a kind of improved side
Method.
As shown in Fig. 2, the feature of bottom extracted also is used as to the object of target detection, this makes it possible to small mesh
Mark is detected, and YoloV3 is used as detection target (3 every layer) using 3 layers of 9 anchor, and the present invention extends 4 layer 12
A anchor thus is avoided that bar board is failed to judge, and increases recall rate.
Preferably, S3 combines accuracy to improve efficiency, using the VGG19 convolution mind for having used smaller convolution kernel
Through network, the number in rod board can be accurately identified.
Preferably, S4 corrects the position location 1C, 3C, 4C, 5C, 6C according to based on pillar number identification, for there is setting for GPS
It is standby, pillar is navigated to according to GPS and the conversion of foundation leg location information；Equipment for there is LKJ, obtains according to LKJ basic data
To " route, other, kilometer post of going ", pillar is navigated to further according to foundation leg location information.2C image pillar number is identified, by
In the equipment of no GPS, LKJ, match with foundation leg location information and navigate to branch by image recognition pillar number
Column has saved a large amount of manpowers.
The step S3 defect Multidimensional Comprehensive analysis includes following sub-step:
S31: Flaw discrimination analysis；
S32: development of defects trend analysis；
S33: defect overhauls result review.
Preferably, S1 Flaw discrimination is analyzed, and after some C has found defect, default association shows other 5 C same
The last detection data of pillar, including a variety of images, detected value.Since the detection focus of each C is different, have certainly
The data characteristics of body, such as: 2C pays close attention to entire pillar panorama, and 4C pays close attention to the details of pillar part, by different detection datas
Whether comprehensive analysis of the personnel to defect is analyzed in comprehensive analysis, help, to being that defect carries out qualitative confirmation, and passes through other C
Detection data can excavate occur defect the reason of.
Preferably, S2 development of defects trend analysis provides 6 C in all of same pillar after some C has found defect
The inquiry of history detection data is shown, passes through the comprehensive analysis of the different detection datas to different time points.Personnel's palm is analyzed in help
Historical development trend of the defect from rudiment to generation is held, summing up experience prevents trouble before it happens.
Preferably, S3 defect maintenance result review uploads the detection data with shore position after defect maintenance.It can be with
It checks analyzing defect treated state, testing result is checked.
The beneficial effects of the present invention are:
(1) recognition efficiency of the present invention is very high, can identify that 16 frame image accuracys rate can reach using the GPU of TITAN XP is per second
99.6% or so, the data in rod board can be accurately identified, bar board is avoided to fail to judge, increases recall rate, improves efficiency and combine
Accuracy；
(2) not only support single detection device history testing result comparative analysis, but also the different detection types of support (1C, 2C,
3C, 4C, 5C, 6C) the crutched all testing results of history of institute are associated check, comparative analysis, there is very high defect
Discriminatory analysis efficiency and use value.
Detailed description of the invention
Fig. 1 is a kind of defect analysis method flow chart based on bar number identification；
Fig. 2 is identification step figure；
Fig. 3 is that S2 step improves schematic diagram.
Specific embodiment
In order to make those skilled in the art more fully understand technical solution of the present invention, with reference to the accompanying drawing to the present invention
It is described in further detail.
As shown in Figure 1, a kind of defect analysis method based on bar number identification, comprising the following steps:
S1: alert data, inspection data loading；
S2: pillar unique positions calculate；
S3: defect Multidimensional Comprehensive analysis.
The step 2 includes following sub-step:
S21: pillar image is extracted from original image；
S22: bar board image is extracted from pillar image；
S23: digital information is extracted from bar board image；
S24: pillar position location correction.
Preferably, S1 uses YoloV3 target detection network algorithm, and pillar image is obtained from original image.
Preferably, S2 extracts bar board image from pillar image, if using YoloV3 target detection network algorithm from branch
Rod board is detected in column image, there are more wrong report, main cause is bar board relative to whole image very little, that is, is directed to
This Small object YoloV3 target detection network algorithm of bar board is lower in the accuracy of detection, it is therefore desirable to a kind of improved side
Method.
As shown in Fig. 2, the feature of bottom extracted also is used as to the object of target detection, this makes it possible to small mesh
Mark is detected, and YoloV3 is used as detection target (3 every layer) using 3 layers of 9 anchor, and the present invention extends 4 layer 12
A anchor thus is avoided that bar board is failed to judge, and increases recall rate.
Preferably, S3 combines accuracy to improve efficiency, using the VGG19 convolution mind for having used smaller convolution kernel
Through network, the number in rod board can be accurately identified.
Preferably, S4 corrects the position location 1C, 3C, 4C, 5C, 6C according to based on pillar number identification, for there is setting for GPS
It is standby, pillar is navigated to according to GPS and the conversion of foundation leg location information；Equipment for there is LKJ, obtains according to LKJ basic data
To " route, other, kilometer post of going ", pillar is navigated to further according to foundation leg location information.2C image pillar number is identified, by
In the equipment of no GPS, LKJ, match with foundation leg location information and navigate to branch by image recognition pillar number
Column has saved a large amount of manpowers.
The step c defect Multidimensional Comprehensive analysis includes following sub-step:
S1: Flaw discrimination analysis；
S2: development of defects trend analysis；
S3: defect overhauls result review.
Preferably, S1 Flaw discrimination is analyzed, and after some C has found defect, default association shows other 5 C same
The last detection data of pillar, including a variety of images, detected value.Since the detection focus of each C is different, have certainly
The data characteristics of body, such as: 2C pays close attention to entire pillar panorama, and 4C pays close attention to the details of pillar part, by different detection datas
Whether comprehensive analysis of the personnel to defect is analyzed in comprehensive analysis, help, to being that defect carries out qualitative confirmation, and passes through other C
Detection data can excavate occur defect the reason of.
Preferably, S2 development of defects trend analysis provides 6 C in all of same pillar after some C has found defect
The inquiry of history detection data is shown, passes through the comprehensive analysis of the different detection datas to different time points.Personnel's palm is analyzed in help
Historical development trend of the defect from rudiment to generation is held, summing up experience prevents trouble before it happens.
Preferably, S3 defect maintenance result review uploads the detection data with shore position after defect maintenance.It can be with
It checks analyzing defect treated state, testing result is checked.
The above disclosure is only the preferred embodiments of the present invention, cannot limit the right model of the present invention with this certainly
It encloses, therefore equivalent changes made in accordance with the claims of the present invention, is still within the scope of the present invention.
Claims (10)
1. a kind of defect analysis method based on bar number identification, it is characterised in that: the following steps are included:
S1: alert data, inspection data loading；
S2: pillar unique positions calculate；
S3: defect Multidimensional Comprehensive analysis.
2. a kind of defect analysis method based on bar number identification according to claim 1, it is characterised in that: the step
Rapid S2 the following steps are included:
S21: pillar image is extracted from original image；
S22: bar board image is extracted from pillar image；
S23: digital bar information is extracted from bar board image；
S24: pillar position location correction.
3. a kind of defect analysis method based on bar number identification according to claim 2, it is characterised in that: the step
Rapid S21 extracts pillar image using YoloV3 algorithm of target detection from original image.
4. a kind of defect analysis method based on bar number identification according to claim 2, it is characterised in that: the step
Rapid S22 uses YoloV3 algorithm of target detection, and the feature that bottom is extracted haves three layers 9 as the object of target detection in original
4 layers of 12 anchor are extended on the basis of anchor to detect Small object as detection target.
5. a kind of defect analysis method based on bar number identification according to claim 2, it is characterised in that: the step
Rapid S23 extracts digital bar information using 19 algorithm of depth convolutional neural networks VGG from bar board image.
6. a kind of defect analysis method based on bar number identification according to claim 2, it is characterised in that: described
S24 navigates to pillar for there is the equipment of GPS, according to GPS and the conversion of foundation leg location information；Equipment for there is LKJ, root
Route, capable other and kilometer post are obtained according to LKJ basic data, navigates to pillar further according to foundation leg location information；For not having
The equipment of GPS, LKJ match with foundation leg location information navigating to pillar by image recognition pillar number.
7. a kind of defect analysis method based on bar number identification according to claim 1, it is characterised in that: the step
Rapid S3 is further comprising the steps of:
S31: Flaw discrimination analysis；
S32: development of defects trend analysis；
S33: defect overhauls result review.
8. a kind of defect analysis method based on bar number identification according to claim 7, it is characterised in that: the step
Rapid S31 is associated with remaining equipment in the last detection data of same pillar, carries out qualitative after some equipment finds defect
Confirmation.
9. a kind of defect analysis method based on bar number identification according to claim 7, it is characterised in that: the step
Rapid S32 provides remaining equipment in all history detection datas of same pillar after some equipment finds defect.
10. a kind of defect analysis method based on bar number identification according to claim 7, it is characterised in that: described
Step S33 uploads the detection data with shore position after defect maintenance, analyzing defect is checked treated state, to detection
As a result it is checked.
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