CN106706647A - Road crack size estimation method based on mobile phone photographing - Google Patents
Road crack size estimation method based on mobile phone photographing Download PDFInfo
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- CN106706647A CN106706647A CN201611230287.2A CN201611230287A CN106706647A CN 106706647 A CN106706647 A CN 106706647A CN 201611230287 A CN201611230287 A CN 201611230287A CN 106706647 A CN106706647 A CN 106706647A
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
- G01N2021/8854—Grading and classifying of flaws
- G01N2021/8874—Taking dimensions of defect into account
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
- G01N21/84—Systems specially adapted for particular applications
- G01N21/88—Investigating the presence of flaws or contamination
- G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
- G01N2021/8887—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges based on image processing techniques
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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
- G06T2207/10—Image acquisition modality
- G06T2207/10004—Still image; Photographic image
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Abstract
The invention discloses a road crack size estimation method based on mobile phone photographing. The method is used for solving the technical problem that the existing road crack size estimation method is low in practicability. According to the technical scheme, the method comprises the following steps: photographing road cracks by utilizing a smart phone, and collecting crack photos and mobile phone sensor data during photographing; performing median filter smoothness, sobel operator sharpening and grey processing on the collected crack photos, performing image segmentation by using an OTSU algorithm, segmenting the cracks from a road background, calculating the area of the cracks in the photos, determining the distance from the mobile phone camera to the cracks, and calculating the actual physical size of the cracks. According to the method disclosed by the invention, the cracks do not need to be photographed by a special crack detection vehicle, the actual physical size of the cracks is calculated by utilizing a convex lens imaging principle of the mobile phone camera only, and the practicality is high. Moreover, when the crack width is calculated, the root-mean-square error is 5.2cm, and when the crack length is calculated, the root-mean-square error is 17.1cm.
Description
Technical field
The present invention relates to a kind of road crack size method of estimation, more particularly to a kind of road crackle based on mobile phone photograph
Size estimation method.
Background technology
In recent years, traditional municipal services that develop into of smart mobile phone provide new means.In traditional urban road
, it is necessary to the equipment of the librarian use specialty of specialty is checked in crack detecting method, substantial amounts of man power and material is so wasted.
With the development of smart mobile phone, common citizen can be taken pictures using mobile phone to crackle, but due to mobile phone photograph when
Wait, the direction taken pictures and the distance taken pictures all are arbitrary, in the present invention we using mobile phone when taking pictures sensor information, such as
Accelerometer, magnetometer, barometer etc., and photo information in itself, estimate road flaw size.
Document " the pavement distress survey technical research Wuhan University of Technologys based on image procossing, 2013 " discloses one kind
The method of estimation of road surface crack size.The method fracture first is refined, and obtains the skeleton in crack, and the skeleton to crackle is made
Element marking, then the length and width in crack is just can obtain by way of scanning and counting.But because the method is using special
Crack detection car crackle is taken pictures, what the distance and angle of camera to crackle were to determine, so the reality of crackle
What the proportionate relationship between the size of the picture of physical size and crackle in photo was to determine.
In sum, when judging crack size using mobile phone photograph, because camera is from the angle and distance of crackle
It is uncertain, the size of crackle picture can be only obtained by photo.
The content of the invention
In order to overcome the shortcomings of existing road crack size method of estimation poor practicability, the present invention provides a kind of based on mobile phone
The road crack size method of estimation taken pictures.The method is taken pictures first with smart mobile phone to road crackle, gathers crackle
Photo and mobile phone sensor data when taking pictures.Crackle photo to collecting carries out including median filter smoothness of image, sobel operators
Sharpen and gray proces, then carry out image segmentation using OTSU algorithms, crackle is partitioned into from the background of road surface, calculate crackle and exist
Area in photo, determines mobile phone camera to the distance of crackle, calculates the actual physical size of crackle.The present invention need not
Special crack detection car is taken pictures to crackle, and the reality of crackle is calculated merely with the convex lens imaging principle of mobile phone camera
Physical size, practicality is good.
The technical solution adopted for the present invention to solve the technical problems：A kind of road crack size based on mobile phone photograph is estimated
Meter method, is characterized in comprising the following steps：
Step one, road crackle is taken pictures using smart mobile phone, collection crackle photo and mobile phone sensing when taking pictures
Device data.Mobile phone baroceptor, acceleration transducer and magnetometric sensor are as calculating mobile phone camera with a distance from crackle
Data foundation.By all mobile phone sensor data preparations into triple<Crackle photo, sensor name, sensor values>.Its
Middle acceleration transducer, the sensor values form of magnetometric sensor are a three-dimensional vectors<X, y, z>, baroceptor is hand
The atmospheric pressure value of machine position<baro>.
Step 2, the crackle photo to being collected in step one include median filter smoothness of image, sobel operators sharpen and
Gray proces, then carry out image segmentation using OTSU algorithms, and crackle is partitioned into from the background of road surface.Binaryzation square after treatment
Battle array is designated as G.
Step 3, mathematical morphology make-before-break treatment is carried out to binaryzation matrix G, the matrix after treatment is designated as M, then
Dilation erosion and Refinement operation are carried out to matrix M, crackle skeleton is extracted, the matrix after treatment is designated as N.
Step 4, for pattern cracking, the every a line for the matrix M that scanning step three is obtained, the maximum for finding crackle vertical is sat
Punctuate y1With minimum ordinate point y2.Each row of scan matrix M, find the maximum abscissa point x of crackle1With minimum abscissa
Point x2.Area s of the pattern cracking in photo is calculated using formula (1)：
S=(y1-y2)*(x1-x2) (1)
Split for perpendicular, every a line of scan matrix M calculates the crackle pixel number per a line, is multiplied by each pixel
Length, the as crack width of the row.Then the width of the every a line crackle to trying to achieve is averaging and obtains the perpendicular average width for splitting
Degree.When seeking the length erected and split, the matrix N that scanning step three is obtained calculates the pixel number of crackle skeleton, is multiplied by each picture
The length of element, as erects the length split.Each row of scan matrix M, obtain the crack width of the row, are averaging and obtain transverse fissure
Mean breadth.Scan matrix N obtains the length of transverse fissure.
Step 5, camera to three parameters of the distance of crackle is calculated, the vertical range h of mobile phone to ground, mobile phone
The left rotation and right rotation angle beta of tilt fore and aft α, mobile phone.Camera is calculated to crackle apart from od by formula (2)：
Od=h/ (cos | α | cos | β |) (2)
Mobile phone is calculated to the vertical range h on ground by formula (3), pmIt is the atmospheric pressure value of mobile phone position, by hand
The baroceptor of machine is obtained, pgIt is the atmospheric pressure value on ground：
The tilt fore and aft α of mobile phone, left rotation and right rotation angle beta are calculated by acceleration transducer and magnetometric sensor, are passed through
Android exploitations API is obtained.
Step 6, size of the crackle in photo is obtained according to step 4, step 5 obtains camera to crackle
The focal length of distance and mobile phone, by convex lens formula (4) (5), derives that formula (6) calculates crackle actual physics chi
Very little, d is object distance, and i is image distance, and f is focal length, and imagesize is the size of picture, and physicalsize is the actual physics chi of crackle
It is very little.
The beneficial effects of the invention are as follows：The method is taken pictures first with smart mobile phone to road crackle, gathers crackle
Photo and mobile phone sensor data when taking pictures.Crackle photo to collecting carries out including median filter smoothness of image, sobel operators
Sharpen and gray proces, then carry out image segmentation using OTSU algorithms, crackle is partitioned into from the background of road surface, calculate crackle and exist
Area in photo, determines mobile phone camera to the distance of crackle, calculates the actual physical size of crackle.The present invention need not
Special crack detection car is taken pictures to crackle, and the reality of crackle is calculated merely with the convex lens imaging principle of mobile phone camera
Physical size, practicality is good.And when calculating crack width, root-mean-square error is 5.2cm, when counting crack length,
Root-mean-square error is 17.1cm.
The present invention is elaborated with reference to the accompanying drawings and detailed description.
Brief description of the drawings
Fig. 1 is the flow chart of road crack size method of estimation of the present invention based on mobile phone photograph.
Specific embodiment
Reference picture 1.Road crack size method of estimation of the present invention based on mobile phone photograph is comprised the following steps that：
Step one, road crackle is taken pictures using smart mobile phone, collection crackle photo and mobile phone when taking pictures are passed
Sensor data.The number of baroceptor, acceleration transducer and magnetometric sensor as calculating mobile phone camera with a distance from crackle
According to foundation.And further by all data preparations into triple<Crackle photo, sensor name, sensor values>.Wherein plus
Velocity sensor, the sensor values form of magnetometric sensor are a three-dimensional vectors<X, y, z>, baroceptor is mobile phone institute
In the atmospheric pressure value of position<baro>.
Step 2, the crackle photo to being collected in step one carry out image procossing.Image is carried out to photo first to locate in advance
Reason, including the sharpening of median filter smoothness of image, sobel operators, gray proces, then carry out image segmentation, from road using OTSU algorithms
Crackle is partitioned into the background of face.Binaryzation matrix after treatment is designated as G.
Step 3, in order to be precisely calculated flaw size in photo, matrix G is carried out at mathematical morphology make-before-break
Reason, the matrix after treatment is designated as M, and dilation erosion and Refinement operation are then carried out to matrix M, crackle skeleton is extracted, after treatment
Matrix is designated as N.
The size of step 4, calculating crackle in photo.For pattern cracking, the matrix M that scanning step three is obtained
Every a line, find the maximum ordinate point y of crackle1With minimum ordinate point y2.Each row of M are scanned, the maximum of crackle is found
Abscissa point x1With minimum abscissa point x2.Area s of the pattern cracking in photo is calculated using formula (1)：
S=(y1-y2)*(x1-x2) (1)
Split for perpendicular, every a line of scan matrix M calculates the crackle pixel number per a line, is multiplied by each pixel
Length, the as crack width of the row.Then the width of the every a line crackle to trying to achieve is averaging and obtains the perpendicular average width for splitting
Degree.When seeking the length erected and split, the matrix N that scanning step three is obtained calculates the pixel number of crackle skeleton, is multiplied by each picture
The length of element, as erects the length split.Similarly, each row of scan matrix M, obtain the crack width of the row, are averaging and obtain
The mean breadth of transverse fissure.Scan matrix N obtains the length of transverse fissure.
Step 5, mobile phone camera are to crackle determination of distance.Calculate camera needs three parameters to the distance of crackle,
Mobile phone to the vertical range h on ground, the tilt fore and aft α of mobile phone, mobile phone left rotation and right rotation angle beta.Calculated by formula (2) and imaged
Head is to crackle apart from od：
Od=h/ (cos | α | cos | β |) (2)
Mobile phone is calculated to the vertical range h on ground by formula (3), pmIt is the atmospheric pressure value of mobile phone position, by hand
The baroceptor of machine can be obtained, pgIt is the atmospheric pressure value on ground：
The tilt fore and aft α of mobile phone, left rotation and right rotation angle beta are calculated by acceleration transducer and magnetometric sensor, are passed through
Android exploitations API is obtained.
The calculating of step 6, crackle actual physical size.The principle of taking pictures of mobile phone camera is convex lens imaging principle, step
Rapid four have obtained size of the crackle in photo, that is, as size, step 5 obtained camera to crackle away from
From, i.e. object distance, the focal length of mobile phone is known, by convex lens formula (4) (5), derives that formula (6) calculates crackle reality
Border physical size, d is object distance, and i is image distance, and f is focal length, and imagesize is the size of picture, and physicalsize is the reality of crackle
Border physical size.
Claims (1)
1. a kind of road crack size method of estimation based on mobile phone photograph, it is characterised in that comprise the following steps：
Step one, road crackle is taken pictures using smart mobile phone, collection crackle photo and mobile phone sensor number when taking pictures
According to；The data of mobile phone baroceptor, acceleration transducer and magnetometric sensor as calculating mobile phone camera with a distance from crackle
Foundation；By all mobile phone sensor data preparations into triple<Crackle photo, sensor name, sensor values>；Wherein plus
Velocity sensor, the sensor values form of magnetometric sensor are a three-dimensional vectors<X, y, z>, baroceptor is mobile phone institute
In the atmospheric pressure value of position<baro>；
Step 2, the crackle photo to being collected in step one carry out including that median filter smoothness of image, sobel operators are sharpened and gray scale
Treatment, then carries out image segmentation using OTSU algorithms, and crackle is partitioned into from the background of road surface；Binaryzation matrix note after treatment
It is G；
Step 3, mathematical morphology make-before-break treatment is carried out to binaryzation matrix G, the matrix after treatment is designated as M, then to square
Battle array M carries out dilation erosion and Refinement operation, extracts crackle skeleton, and the matrix after treatment is designated as N；
Step 4, for pattern cracking, the every a line for the matrix M that scanning step three is obtained finds the maximum ordinate point y of crackle1
With minimum ordinate point y2；Each row of scan matrix M, find the maximum abscissa point x of crackle1With minimum abscissa point x2；Make
Area s of the pattern cracking in photo is calculated with formula (1)：
S=(y1-y2)*(x1-x2) (1)
Split for perpendicular, every a line of scan matrix M calculates the crackle pixel number per a line, is multiplied by the length of each pixel
Degree, the as crack width of the row；Then the width of the every a line crackle to trying to achieve is averaging and obtains the perpendicular mean breadth for splitting；Ask
When erecting the length split, the matrix N that scanning step three is obtained calculates the pixel number of crackle skeleton, is multiplied by each pixel
Length, as erects the length split；Each row of scan matrix M, obtain the crack width of the row, are averaging and obtain the average of transverse fissure
Width；Scan matrix N obtains the length of transverse fissure；
Step 5, calculate camera to three parameters of the distance of crackle, the vertical range h of mobile phone to ground, mobile phone it is front and rear
Inclined angle alpha, the left rotation and right rotation angle beta of mobile phone；Camera is calculated to crackle apart from od by formula (2)：
Od=h/ (cos | α | cos | β |) (2)
Mobile phone is calculated to the vertical range h on ground by formula (3), pmIt is the atmospheric pressure value of mobile phone position, by the gas of mobile phone
Pressure sensor is obtained, pgIt is the atmospheric pressure value on ground：
The tilt fore and aft α of mobile phone, left rotation and right rotation angle beta are calculated by acceleration transducer and magnetometric sensor, by android
Exploitation API is obtained；
Step 6, size of the crackle in photo is obtained according to step 4, step 5 obtains camera to the distance of crackle
And the focal length of mobile phone, by convex lens formula (4) (5), deriving that formula (6) calculates crackle actual physical size, d is
Object distance, i is image distance, and f is focal length, and imagesize is the size of picture, and physicalsize is the actual physical size of crackle；
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109163698A (en) * | 2018-08-06 | 2019-01-08 | 百度在线网络技术（北京）有限公司 | Building settlement measurement method, device and storage medium |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100053318A1 (en) * | 2008-08-28 | 2010-03-04 | Hironori Sasaki | Camera module and method of producing the same |
| CN104089580A (en) * | 2014-06-26 | 2014-10-08 | 华南理工大学 | Concrete surface crack width measuring instrument and method based on smart phone |
| US20140330438A1 (en) * | 2013-03-27 | 2014-11-06 | Russell Haines | Intelligent hvac register |
| CN104316601A (en) * | 2014-10-29 | 2015-01-28 | 上海斐讯数据通信技术有限公司 | Mobile phone with floor internal defect detection function and working principle of mobile phone |
| CN204288301U (en) * | 2014-12-19 | 2015-04-22 | 中国科学院武汉岩土力学研究所 | A kind of fracture width change monitoring device |
| CN105160695A (en) * | 2015-06-30 | 2015-12-16 | 广东欧珀移动通信有限公司 | Picture processing method and mobile terminal |
-
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- 2016-12-28 CN CN201611230287.2A patent/CN106706647B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20100053318A1 (en) * | 2008-08-28 | 2010-03-04 | Hironori Sasaki | Camera module and method of producing the same |
| US20140330438A1 (en) * | 2013-03-27 | 2014-11-06 | Russell Haines | Intelligent hvac register |
| CN104089580A (en) * | 2014-06-26 | 2014-10-08 | 华南理工大学 | Concrete surface crack width measuring instrument and method based on smart phone |
| CN104316601A (en) * | 2014-10-29 | 2015-01-28 | 上海斐讯数据通信技术有限公司 | Mobile phone with floor internal defect detection function and working principle of mobile phone |
| CN204288301U (en) * | 2014-12-19 | 2015-04-22 | 中国科学院武汉岩土力学研究所 | A kind of fracture width change monitoring device |
| CN105160695A (en) * | 2015-06-30 | 2015-12-16 | 广东欧珀移动通信有限公司 | Picture processing method and mobile terminal |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109163698A (en) * | 2018-08-06 | 2019-01-08 | 百度在线网络技术（北京）有限公司 | Building settlement measurement method, device and storage medium |
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