WO2018122818A1 - 一种基于红外热像图分析的沥青路面裂缝发育程度检测方法 - Google Patents
一种基于红外热像图分析的沥青路面裂缝发育程度检测方法 Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D5/00—Protection or supervision of installations
- F17D5/02—Preventing, monitoring, or locating loss
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N3/00—Investigating strength properties of solid materials by application of mechanical stress
- G01N3/08—Investigating strength properties of solid materials by application of mechanical stress by applying steady tensile or compressive forces
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- E—FIXED CONSTRUCTIONS
- E01—CONSTRUCTION OF ROADS, RAILWAYS, OR BRIDGES
- E01C—CONSTRUCTION OF, OR SURFACES FOR, ROADS, SPORTS GROUNDS, OR THE LIKE; MACHINES OR AUXILIARY TOOLS FOR CONSTRUCTION OR REPAIR
- E01C23/00—Auxiliary devices or arrangements for constructing, repairing, reconditioning, or taking-up road or like surfaces
- E01C23/01—Devices or auxiliary means for setting-out or checking the configuration of new surfacing, e.g. templates, screed or reference line supports; Applications of apparatus for measuring, indicating, or recording the surface configuration of existing surfacing, e.g. profilographs
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- E—FIXED CONSTRUCTIONS
- E01—CONSTRUCTION OF ROADS, RAILWAYS, OR BRIDGES
- E01C—CONSTRUCTION OF, OR SURFACES FOR, ROADS, SPORTS GROUNDS, OR THE LIKE; MACHINES OR AUXILIARY TOOLS FOR CONSTRUCTION OR REPAIR
- E01C7/00—Coherent pavings made in situ
- E01C7/08—Coherent pavings made in situ made of road-metal and binders
- E01C7/18—Coherent pavings made in situ made of road-metal and binders of road-metal and bituminous binders
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
- F17—STORING OR DISTRIBUTING GASES OR LIQUIDS
- F17D—PIPE-LINE SYSTEMS; PIPE-LINES
- F17D5/00—Protection or supervision of installations
- F17D5/02—Preventing, monitoring, or locating loss
- F17D5/06—Preventing, monitoring, or locating loss using electric or acoustic means
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M3/00—Investigating fluid-tightness of structures
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M3/00—Investigating fluid-tightness of structures
- G01M3/002—Investigating fluid-tightness of structures by using thermal means
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M3/00—Investigating fluid-tightness of structures
- G01M3/38—Investigating fluid-tightness of structures by using light
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M5/00—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings
- G01M5/0033—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings by determining damage, crack or wear
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01M—TESTING STATIC OR DYNAMIC BALANCE OF MACHINES OR STRUCTURES; TESTING OF STRUCTURES OR APPARATUS, NOT OTHERWISE PROVIDED FOR
- G01M5/00—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings
- G01M5/0066—Investigating the elasticity of structures, e.g. deflection of bridges or air-craft wings by exciting or detecting vibration or acceleration
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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
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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
- G01N21/95—Investigating the presence of flaws or contamination characterised by the material or shape of the object to be examined
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N33/00—Investigating or analysing materials by specific methods not covered by groups G01N1/00 - G01N31/00
- G01N33/42—Road-making materials
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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
- G01N21/8806—Specially adapted optical and illumination features
- G01N2021/8845—Multiple wavelengths of illumination or detection
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N25/00—Investigating or analyzing materials by the use of thermal means
- G01N25/72—Investigating presence of flaws
Definitions
- the invention belongs to the field of intelligent transportation system and information technology, and relates to detecting the development degree of cracks on asphalt pavement by using temperature measuring infrared camera and image processing technology.
- the gray scale information and temperature information of asphalt pavement crack can be obtained.
- the gray scale information can realize the target of conventional image-based crack recognition, and the temperature information can be used to detect the crack development degree, mainly through experiment and machine learning. Establish a relationship model between road surface and crack temperature difference and crack development degree. Background technique
- the damage of the pavement surface is the main basis used by road engineers to judge the repair of the pavement. In the four major indicators of pavement damage (crack, loose, deformed, other), the surface damage is the most difficult to measure. The traditional method is manual visual inspection. However, with the advancement of technology and the improvement of testing requirements, research on the development and detection methods of high-speed and high-efficiency testing equipment has not been stopped at home and abroad.
- the system uses an artificial light source mounted on both sides of the inspection vehicle for illumination.
- Data acquisition is obtained through a television camera, sensor, signal processor and image recording device.
- Data storage consists of a high-density video tape recorder and a universal video tape recorder.
- Image processing is performed in two stages using parallel technology. The first stage is mainly image segmentation and feature extraction, which is performed by a parallel microprocessor. The second stage performs noise reduction, sub-image connection and recovery in parallel. Since the system cannot analyze the type of crack and can only work at night, it also requires multiple super microprocessors to complete the two-stage image processing, which is ultimately not commercially available.
- CCD cameras have high dynamic range, resolution and sensitivity.
- the video signal of the CCD camera can be conveniently stored in the computer through the video capture card or the image capture card for real-time display, storage and processing.
- the cost of digital image systems based on CCD cameras has been greatly reduced. Therefore, in the development of digital road surface damage detection system under scene conditions, CCD camera and computer image processing technology have been widely used.
- the University of Arkansas in the United States has developed a real-time road surface crack measurement system "Digital Highway Data Vehicle (DHDV)", and proposed a parallel algorithm for image processing and a hardware implementation platform.
- the image processing hardware system is based on a common multi-processor (CPUX86) platform, which obtains a damaged image of the road surface by a CCD camera mounted on the test vehicle, applies a GPS system for crack location, and uses a distance measuring device (DM) to collect the distance. information.
- DM distance measuring device
- a dual CPU microprocessor collects this data and transmits it to a multi-CPU computer for damage analysis in real time.
- the system integrates digital image acquisition and digital image processing to enable data acquisition, identification and classification of pavement cracks at higher speeds. However, this system requires the assistance of a supercomputer and has high requirements on hardware. It is still in the research stage.
- structured light three-dimensional detection technology has the principles of simple principle, high speed, high precision, easy to implement, and shadow on the road surface, black spots and The advantages of random noise insensitivity have become the development trend of highway pavement detection technology.
- the three-dimensional visual road surface disease measurement technology based on structured light uses the road surface contour as an information carrier to extract the crack features contained in the road surface profile by signal processing. Since most of the road surface crack detection methods are based on the road surface two-dimensional image analysis technology, the research on the road surface crack three-dimensional detection technology and the corresponding road surface contour data processing methods are relatively few.
- the crack calculation process is as follows: (1) With a sampling interval of 3 cm, starting from the starting point of the contour signal, find the lowest point a within the range of 3 cm sampling interval; (2) Centering on point a, extending 1.5 cm to the left and right, looking for The highest point in the range of 1.5 cm; (3) The difference between a and the highest point on the left and right is taken as the depth of the crack, and the difference between the abscissas between the two highest points is defined as the width of the crack. (4) When the depth is greater than the minimum threshold and the width is greater than the maximum threshold, the definition is crack. This detection criterion is cycled until the stop criterion is met.
- the method has the advantages of simple principle and fast processing speed, but when the crack happens at the sampling interval, the crack is misdetected.
- the Laser Vision system developed by G.I.E. Canada includes the BIRIS laser sensor, which contains digital filters and feature analysis systems.
- the system can extract the crack features of the road surface contour signal during the data acquisition process.
- the main principle is as follows: Using the adaptive adjustable width moving window function, the width, position and other information of the crack can be directly extracted, and the noise in the road surface contour signal can be removed.
- the third-dimensional road surface detection technology such as laser has appeared.
- the other uses ordinary image pairs.
- Two-dimensional detection of pavement cracks the research mainly focuses on the identification and classification of cracks, but there are few researches on the development degree of cracks and the depth of cracks, mainly based on rough grading of crack width, and the use of ground penetrating radar and ultrasonic waves. The depth of the crack is detected, and the effect is seriously affected by the inclusions in the crack.
- the present invention proposes a method for detecting the degree of crack development using a two-dimensional infrared image, which is based on an infrared heat map.
- a relationship model with the degree of development of the crack, and to measure the degree of crack development by numbers, the greater the index of crack development, the more serious the surface development, the greater the temperature difference will be.
- machine learning is used to determine the classification function of development degree.
- Patent document CN105719259 published a road crack image detection method, which performs grayscale and filtering processing on the acquired road surface image, constructs a pulse coupled neural network PCNN model, and uses the genetic algorithm to quickly find the most in the solution space nonlinearity.
- the advantages of the optimal solution optimize the important parameters of the model, and quickly and accurately segment the cracks and background in the image; then according to the characteristics of the segmented image, the connected image is detected in the whole image to filter out the interference of noise and background texture; , extract the crack skeleton, find the maximum width of the crack along the normal line of the skeleton, and mark it in the original image.
- Patent document CN105719283 discloses a road surface crack image detection method based on Hessian matrix multi-scale filtering.
- the method collects road surface images in real time through a binocular CCD industrial camera, and the vehicle GPS records the position of the road image in real time, and then performs the acquired image target.
- Recognition Gaussian filtering of the pyramid structure, highlighting the crack characteristics of the pavement through feature recognition of multiple scales, using the eigenvalues and feature directions of the Hessian matrix to track the crack growth direction, and then quickly classifying the crack according to the curvature characteristics of the crack Denoising the road image with severe noise.
- the technical solution of the invention can quickly extract and classify the road surface crack target, effectively denoise the micro crack signal in the noise environment, has strong anti-noise ability, low error detection and false detection rate, and is suitable for most complex road surface detection.
- the invention proposes a detection method for the development degree of pavement crack based on infrared thermal image analysis, photographs the crack of the road surface by the temperature measuring infrared thermal imager, obtains the infrared thermal image of the crack of the road surface, and establishes the relationship between the degree of crack development and the temperature difference between the crack and the road surface.
- the model and then the machine learning, determines the classification function of the crack development degree index.
- FIG. 1 is an infrared thermal image diagram of the road surface crack obtained by the temperature measuring type infrared camera.
- the infrared thermal image obtained by the infrared camera can not only obtain the gray information of the crack and the pavement but also obtain their temperature information, as shown in Fig. 2, which is the gray scale information and temperature information of the road surface and the crack.
- the temperature below the road surface and the road surface will be different. Therefore, the material below the road surface will exchange heat with the road surface and air through the crack, and the temperature difference between the road surface and the crack will also occur.
- the greater the crack width and depth of the asphalt pavement the more intense the heat exchange of material air under the pavement and the road surface. The temperature difference will be bigger.
- the greater the depth of the crack the more likely the future water damage will occur, and the crack will also reflect the degree of development through the developmental index. Therefore, the more severe the crack, the greater the temperature difference between the road surface and the crack, and we can use the thermal imager to detect the temperature difference and then detect the development of the crack.
- the traditional evaluation indicators for asphalt pavement cracks are divided into the following three levels: light, medium and heavy:
- the edge of the crack is severely broken, and there are more joints, causing the vehicle to jump sharply.
- this classification level is not enough to reflect the severity of the crack.
- the severity of the crack is mainly measured by the damage it has caused to the pavement, that is, the potential damage in the near future.
- the severity of the crack is not only related to the length of the crack, but also The depth of the crack is related. The greater the depth, the greater the damage caused to the roadbed, and the greater the possibility of water damage.
- the humidity in the crack is also one of the factors affecting the severity of the crack. Therefore, the crack is serious. The degree needs to be considered comprehensively.
- image analysis can detect the width of the crack, and the depth of the crack can be detected by the bottom radar.
- the hygrometer can detect the humidity in the crack, but how to accurately and simply reflect the development degree of the crack is in the project. The problem that needs to be solved.
- the object of the present invention is to reflect the degree of development of cracks by the temperature difference between the road surface and the crack under certain conditions, and their corresponding relationship is the detection model.
- the roadmap of the invention is shown in Figure 1.
- Degree of development The degree of development mentioned in the invention characterizes the degree of damage to the pavement caused by cracks in the asphalt pavement and the severity of recent damage. It covers the traditional crack severity classification, the width of the crack, the depth and the humidity of the crack. Etc. may be related to factors that may increase the severity of crack damage. These factors are included to characterize the level of development of cracks from the beginning to the present.
- Support Vector Machine is a machine learning method based on statistical learning theory developed in the mid-1990s. It seeks to improve the generalization ability of learning machines by seeking structural risk minimization, and realizes the minimum of empirical risk and confidence range. In order to achieve a good statistical rule in the case of a small sample size.
- support vector machines SVMs, which also support vector networks
- SVMs are supervised learning models related to related learning algorithms, which can analyze data, identify patterns, and use for classification and regression analysis. Given a set of training samples, each marked as belonging to two categories, an SVM training algorithm builds a model, assigning new instances to one class or other classes, making it a non-probabilistic binary linear classification. Generally speaking, it is a two-class classification model.
- the basic model is defined as the linear classifier with the largest interval in the feature space. That is, the learning strategy of the support vector machine is to maximize the interval and finally transform into a convex quadratic. Solving the problem of planning.
- Classification function The temperature difference data is linearly classified according to the degree of crack development by the support vector machine. The development degree is divided into three levels of 1, 2, and 3, and 3 is the most serious. Then there will be one between 1, 2 and 2, 3.
- the line is the dividing line, and this line expression is the classification function.
- Crack area An area of asphalt pavement that includes not only the crack area itself, but also a road area within a certain range around it, including the requirements of image processing and crack identification.
- the temperature difference data obtained by taking the measured atmospheric temperature into the two classification functions of the crack development degree detection model.
- Measured temperature difference data Temperature difference data between the crack area and the road surface area in the crack area obtained by image processing the acquired infrared image of the crack area.
- Developmental index between 0 and 3, including a number of 0 and 3, the size of which reflects the degree of development of the fracture. The larger the number, the more serious the development of the fracture. The description is indicated by the letter m.
- Development level A number in 1, 2 or 3 whose size reflects the degree of development of the fracture. The larger the number, the more serious the development of the fracture. surroundings
- the temperature information of pavement and crack is used to detect the degree of crack development.
- the temperature of the pavement changes greatly and is easily interfered by environmental factors.
- the requirements for the experimental environment are higher than the crack recognition based on ordinary images.
- We determine the environmental requirements of the experiment based on the law of changes in the temperature of the road surface. The environmental requirements ensure the reliability of the final model.
- the uncooled focal plane temperature measuring type infrared thermal imager uses an infrared detector and an optical imaging objective lens to receive the infrared radiation energy distribution pattern of the measured object and reflect it on the photosensitive element of the infrared detector, thereby obtaining an infrared thermal image, which is obtained.
- the image corresponds to the heat distribution field on the surface of the object.
- an infrared camera converts invisible infrared energy emitted by an object into a visible thermal image.
- the different colors above the thermal image represent the different temperatures of the object being measured.
- Uncooled focal plane temperature measuring infrared camera Uncooled focal plane temperature measuring infrared camera.
- the height of the equipment from the road surface is about 1 ⁇ 2m. It is required to capture the requirements for crack recognition after the subsequent image processing. Because the collected photos can basically meet the requirements, here is not More details.
- the unprocessed crack image will contain noise information. If these noises are not removed, it will be difficult for subsequent image processing and result analysis. Therefore, image preprocessing is necessary. Preprocessing can remove some redundant information in the image and highlight the target we are interested in, so as to reduce the amount of image information and improve the image quality. First, the image needs to be noise-reduced.
- the acquired infrared image of the road surface crack is a color image containing brightness and color information. It is necessary to grayscale the crack image of the road surface, that is, convert the originally collected color image into a grayscale image, and remove the color information in the image.
- the general crack recognition algorithm is based on the grayscale features of the image, so the color information in the image is not practical for the recognition process. Meaning, removing the color information in the image can reduce the amount of calculation, so it is necessary to grayscale before the road surface crack image processing.
- the brightness of the road surface crack image collected by the traditional image-based method is not uniform, and the gray value of the crack part and the background part of the image have a large difference. This large difference will bring certain processing to the subsequent processing. Difficulties, such as the selection of thresholds in image segmentation.
- the present invention utilizes an infrared image for processing, and the image thereof only differs due to the difference in temperature. In the experimental environment described above, the image is collected without substantially having a temperature difference. Therefore, the present invention does not need to consider the unevenness of the gray scale. The impact.
- the noise model is first analyzed.
- the factors that affect people's acceptance of the target information are called noise signals.
- Noise is generally theoretically defined as unpredictable, and random errors can only be known using probabilistic statistics. Therefore, it is more appropriate to regard the noise in the image as a multi-dimensional random process.
- the random process can be used to describe the noise, that is, the probability distribution function and the probability density distribution function are used to represent, and the more mature noise reduction algorithm can be used. . .
- the crack target to be identified in the road crack image has less information, and the image clarity and contrast are also reduced due to interference of many factors during the image acquisition and transmission. Therefore, after filtering and denoising the image, the graphics are further enhanced, so that the crack target we are interested in is more prominent, providing a basis for the subsequent segmentation recognition algorithm.
- Image segmentation is the process of classifying pixels in an image into specific regions with unique properties and extracting the objects of interest. Since people study images, they all have a goal, which is the area of interest in the image, and often these target areas have some specific properties.
- the purpose of this step of the present invention is to find a cracked area and a non-cracked area of the road surface.
- the average RGB value of each area can be matched with the colorbar to obtain the temperature value of the area.
- the temperature data can then be processed.
- the temperature difference is plotted on the ordinate and the light intensity is plotted on the abscissa.
- the temperature difference is approximately the same as the atmospheric temperature
- the temperature difference between the severe crack and the road surface is different.
- the temperature of the road surface rises as a whole, and the difference between the road surface temperature and the atmospheric temperature is increased.
- the temperature is 35 °C
- the asphalt road temperature can reach 55 ° when the light intensity is 150,000 Lux. C ; however, the effect of illuminance on the asphalt pavement and the temperature of its surface is almost synchronous, therefore, The degree has little effect on the temperature difference between the crack and the road surface.
- the road surface is sufficiently clean and dry
- we lock the influence factors of the temperature difference to the illuminance and atmospheric temperature by querying relevant data, and then through experiments, it is analyzed that the illuminance has no effect on the temperature difference between the crack and the road surface.
- the illuminance has no effect on the temperature difference between the crack and the road surface. It should be noted that in the experiment, the road surface after rain is selected, and the crack temperature is found to be lower than that of the road surface (as opposed to the complete dry condition), and the temperature difference will also change greatly as the road surface dries, and the change is more complicated.
- the present invention avoids the effects of crack moisture by selecting the experiment in a completely dry condition.
- the present invention only considers the relationship between the temperature difference between the crack and the road surface and the atmospheric temperature. At the same time, it can be seen from the above figure that for different degrees of cracks, the temperature difference is different at different temperatures, so we can reflect the degree of crack development by detecting the temperature difference between the crack and the road surface.
- the data is linearly classified using a support vector machine. Taking the atmospheric temperature as the abscissa, the crack and the road surface temperature difference as the ordinate to draw points, there are three levels of 1, 2, 3, the greater the number, the more serious the development degree, as shown in Figure 8, the classification function diagram, you can get the following two A classification function, as shown in equation (1) ( 2 ).
- ⁇ ( °C ) is the atmospheric temperature
- ⁇ CO is the temperature difference between the asphalt pavement and the crack
- 23 is the linear classification function coefficient
- the value range is 0.02 ⁇ 0.03
- b 23 linear classification function constant term the value range is 0.60 ⁇ 0.85.
- ⁇ ( °C ) is the atmospheric temperature
- ⁇ ( °C ) is the temperature difference between the asphalt pavement and the crack
- ⁇ 12 is the linear classification function coefficient
- the value range is 0.0075 ⁇ 0.0100
- b 12 linear classification function constant term value The range is 0.4 ⁇ 0.65.
- test results are judged according to the following
- test environment meets the requirements, that is, the experimental environment described above: During the sunny day, between 8 am and 4 pm, the road surface is clean and completely dry. Then, the infrared thermal imager is used for data acquisition, and the temperature at the time of collecting each picture is recorded, and the temperature difference between the crack and the road surface is obtained after the treatment, and the development degree grading thresholds ⁇ 12 and ⁇ are calculated according to the temperature.
- the traditional crack analysis technology based on image analysis only pays attention to the crack itself. It is necessary to detect the presence or absence of cracks, location and geometry, and the transition between the crack and the road surface is basically not considered.
- the invention mainly needs to obtain the temperature difference between the road surface area and the crack area. It is necessary to pay attention to the two areas of the crack and the road surface. Therefore, the influence of the transition area can be considered. After the crack and the road surface area are obtained by image segmentation, the transition of the two area boundaries is required. The area is cut off because the temperature of the pavement to the crack temperature is gradual. The temperature of the transition zone between the crack and the pavement is between the crack center temperature and the pavement temperature. The transition zone is the interference zone for the pavement temperature, as shown in Figure 9. The following measures can be taken to exclude the interference zone.
- the transition interference zone is small relative to the road surface area, so the road surface area can be cut off at a fixed width when the exclusion is performed, where the width can be set to w width (0.5 cm ⁇ W ⁇ 2.5cm), actually Taking the method based on the maximum width ratio of the crack region, that is, after detecting the crack region, the crack region is doubled up and down, and the remaining region is defined as the pavement region without the interference region, and the width of the road region is assumed to be divided by the image. for/). Finally, the determined width is D, and the following formula (3) is used. This method can better eliminate the influence of the transition interference zone.
- the exclusion of the transition interference area needs to be more detailed. It can be excluded by the method based on the width ratio of the crack area.
- a is the ratio of the upper and lower parts of the crack width to eliminate the transition interference zone, 0.1 ⁇ « ⁇ 0.2.
- Asphalt pavement refers to various types of pavements paved with road asphalt materials in mineral materials. Asphalt binder improves the ability of paving granules to resist road and natural factors against road damage, making the road surface smooth, dust-free, impervious, and durable.
- the road structure is a band structure built in nature, environmental factors and loads. The effect is the main reason for the damage of the pavement structure.
- the physical characteristics of the pavement area are relatively consistent. Therefore, in the infrared image, the temperature value of the pavement area is basically the same, so it is convenient to handle, and no separate consideration is needed, that is, the pavement is obtained in the image segmentation. After the area is returned to the infrared image to calculate the RGB average of the entire area.
- the crack area is usually elongated, such as a two-meter-long crack, which may be only a few millimeters wide.
- the cracks are half-meter long and the development is more serious. According to the average RGB value, it may need to be repaired.
- the half-meter-long area is more developed, and the average RGB value is calculated.
- the development degree index obtained by the temperature difference will be lower than the previous one, and even shows that it is not repaired. It is obviously unscientific. Therefore, it is necessary to calculate the RGB value for the fracture area and give the area with more serious development.
- the temperature difference between the seam area and the road surface will be an array. As shown in Fig. 10, different section cracks can obtain different temperature difference values.
- the effective crack region is divided into the P segments by the straight line along the y-axis direction, and each segment length is arbitrary;
- the effective crack region is divided into the p segments by the straight line along the X-axis direction of the image, each length Any; for other types of cracks, not segmented or divided into segments according to the geometric center of the crack, each segment corresponds to the central angle.
- the crack region divided by the image can be divided into "( « ⁇ 2) segments for consideration.
- Each segment can be processed according to the technical route described above, that is, image graying and noise reduction are first performed. Then the image is enhanced and then the image is segmented, and the crack region is obtained and then divided into “segments for subsequent processing, including calculating the average RGB value for each segment, and then matching the average RGB value with the color value in the legend to determine the segment.
- the temperature value, finally, the length of the segment crack region and the temperature difference ⁇ between the crack region and the road surface temperature are obtained, that is, the following array is obtained, as shown in the formula (5):
- ⁇ is the temperature difference between the segment and the road surface after the fracture zone is divided into sections.
- a weighted average calculation can be performed according to the following formula (7) to obtain a final measured temperature difference AJ of the effective road surface region and the effective crack region.
- the invention divides the crack development degree into three levels of 1, 2 and 3 by machine learning, and the SVM supports the vector machine as a classification method.
- the kernel function maps the linearly inseparable samples in the low-dimensional space to the linearly separable sample space in the high-dimensional space, and calculates the inner product by the kernel function to obtain a linear classifier.
- kernel functions such as linear kernel functions, polynomial kernel functions, radial basis kernel functions, Sigmoid kernel functions, and composite kernel functions.
- the linear kernel function is first used to classify the 1 and 2 grades, and the classification function / 12 is obtained , and then the 2, 3 grades are classified according to the data to obtain the classification function / 23 , and there is a certain error in the machine learning classification.
- the classification data some of the cracks belonging to the lighter development degree are assigned to the category of more serious development degree, and the cracks with the heavier development degree are assigned to the lighter category.
- the average development index of the weights there will be a small number of fracture development index, because there is a certain error in the classification of these, although the development index of the decimals does not necessarily make the fracture development index more Precise, but can reflect the relative size, 2.
- the development index of the 6 is divided into cracks with a developmental index of 3, and 2. 4 is divided into cracks with a developmental index of 2, which is the previous classification. Method, but the 2.6 developmental degree index and the 2.4 developmental degree index calculated by the weighting are actually not much different, which has a very important influence on the decision-making of managers. Therefore, in the calculation of the crack development degree index, the invention can take a decimal point to indicate the calculated relative relationship.
- the developmental index of 2.4 is not necessarily more serious than the developmental index of 2.3, because as mentioned above, there is an error in the classification itself. 2 This integer part may already be inaccurate. .
- the invention determines an error term according to the variance of the weighted average calculation method in the calculation process, that is, the development degree index can take one decimal place and is expressed as: m ⁇ r.
- the data is linearly classified using a support vector machine.
- the atmospheric temperature is plotted on the abscissa, and the temperature difference between the crack and the road surface is plotted on the ordinate.
- ⁇ ( °C ) is the atmospheric temperature
- ⁇ CO is the temperature difference between the asphalt pavement and the crack
- 23 is the linear classification function coefficient
- the value range is 0.02 ⁇ 0.03
- b 23 linear classification function constant term the value range is 0.60 ⁇ 0.85.
- T a n T + b n ( 2)
- ⁇ ( °C ) is the atmospheric temperature
- ⁇ CO is the temperature difference between the asphalt pavement and the crack
- ⁇ 12 is the linear classification function coefficient
- the value range is 0.0075 ⁇ 0.0100
- b 12 linear classification function constant term the value range is 0.4 ⁇ 0.65.
- test results are judged according to the following
- the result calculated by the formula can take one decimal place.
- the composition of the pavement disease detection system generally includes a vehicle carrying vehicle, a fast road surface image acquisition system, a disease location system, and a back-end data processing system.
- the vehicle carries various equipments and is responsible for providing power and communication coordination data collection at the normal speed.
- the motor vehicle is integrated into a variety of sensors and processing systems, generally including road image digital image acquisition, forward-looking road digital video acquisition and positioning system. It is a multi-sensor fusion combined positioning system based on geographic information, including road surface flatness detector, road rutting detector, communication coordination, power supply, etc.
- the background data processing is an intelligent processing center based on massive database. It mainly completes the detection and identification of diseases. .
- the camera In the process of image acquisition, the camera is generally mounted on a normal road surface inspection vehicle, and when the vehicle is normally driven, the imaging and image information of the entire road surface is completed.
- the imaging and image information of the entire road surface is completed.
- Road disease detection methods based on image processing generally first need to obtain image acquisition, then image preprocessing, and then extract the target.
- the general detection steps can be as shown in Figure 11:
- the crack detection technology based on ordinary images has been relatively mature, mainly to detect the existence of cracks and the geometry and type of cracks.
- the crack detection based on ordinary images is affected by uneven illumination, but after the introduction of infrared images It is not affected by such factors, and the gray image of the infrared image achieves the effect of graying out the ordinary image. Therefore, based on the existing mature image-based crack detection technology, an infrared image based on the image is proposed. Asphalt pavement crack detection technology.
- the asphalt pavement crack detection technology based on infrared image firstly gradates the acquired infrared image, and then uses the existing mature image based asphalt pavement crack detection technology.
- Figure 1 is a roadmap of the invention
- FIG. 1 Schematic diagram of the data collection environment
- Fig. 3 Schematic diagram of infrared thermal image of pavement crack: (1) (2) transverse cracks of different development degrees; (3) reticular cracks; (4) cracks repaired by asphalt;
- FIG. 1 image processing process diagram
- Figure 6 shows the relationship between illumination and temperature difference
- Figure 7 shows the relationship between atmospheric temperature and temperature difference
- Figure 8 Support vector machine linear classification map
- FIG. 10 Schematic diagram of different temperature differences obtained for different crack sections
- Figure 12 shows the roadmap for the implementation of the test method. detailed description
- the predictive relationship model needs to be used under certain environmental conditions to ensure accuracy. First, it is necessary to ensure that the environmental conditions are stable during data collection. During the day, the maximum temperature of the road surface appears almost simultaneously with the daily maximum temperature. During the sunrise and sunset every day. The temperature at each depth of the road surface is always approximately equal. The lowest temperature of the road surface also appears almost simultaneously with the daily minimum temperature. At the same time, as the depth increases, the time at which the daily minimum temperature occurs at different depths gradually lags behind, that is, solar radiation. Although it has a variation law similar to the road surface temperature, the influence on the road surface temperature is characterized by hysteresis and accumulation.
- the experimental environment can be summarized as follows:
- the road surface is clean. In order to ensure that the infrared rays of the cracks and road surface can be detected by the thermal imager, it is necessary to ensure that the road surface is clean, and soil, impurities and oil stains will have an impact.
- a temperature-type infrared thermal imager is used to photograph the cracked area of the asphalt pavement, and an infrared thermal image of the crack region is obtained.
- the infrared camera uses an infrared detector and an optical imaging objective to receive the infrared radiation energy distribution pattern of the target to be reflected on the photosensitive element of the infrared detector, thereby obtaining an infrared thermal image, the thermal image and the surface heat of the object.
- the distribution field corresponds.
- an infrared camera converts invisible infrared energy emitted by an object into a visible thermal image.
- the different colors above the thermal image represent the different temperatures of the object being measured.
- Invention of the current mainstream infrared imaging device Uncooled focal plane micro-thermal infrared thermal imager. At the same time, it is necessary to record the temperature and crack development degree when collecting crack images. The temperature is directly measured by a thermometer, and the degree of crack development is manually measured according to the crack width.
- the true development index of the crack mainly referring to the traditional classification method, that is, the crack For the classification of light, medium and heavy grades, and considering the factors such as the humidity and depth of the crack, experts are required to score the fracture development index, and the detection model is established based on the actual data.
- Image preprocessing is first carried out, mainly to gradation, noise reduction and image enhancement of the infrared image; then image segmentation is performed to obtain the crack region and the non-crack region of the road surface; and then the region is performed in the initial infrared image according to the two region positions. Positioning; Finally, calculate the RGB average value of the crack area and the road surface area of the infrared image, and match the RGB average value with the RGB value of the colorbar legend in turn, and the temperature represented by the most consistent position is the temperature of the area, and the crack area and The pavement area is matched with the legend to obtain the respective temperatures, and the temperature difference between the crack and the road surface is obtained.
- the image segmentation technology in image processing can identify the crack region directly after the crack region and the road surface region, and can also analyze the transition interference zone directly between the crack and the road surface according to the foregoing.
- the final temperature difference data can be obtained directly to calculate the temperature difference between the entire crack area and the road surface area.
- the crack area can also be divided into sections, and the method of even distribution can be adopted, and then the section with higher development index is given higher.
- the weight is calculated according to the method of giving different weights to calculate the temperature difference between the final crack area and the road surface area.
- the above detection model can be used to detect the actual degree of crack development, and it is necessary to collect the infrared image of the cracked road surface and the current atmospheric temperature.
- the index of crack development can be roughly divided into three levels of 1, 2, and 3. Only one of the three numbers can be taken. The larger the value, the more serious the development.
- the calculation can also use the developmental index with a decimal number as mentioned above, the value range is 0 ⁇ 3, and the specific development degree index can be expressed as a form of a woman.
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Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109801282A (zh) * | 2019-01-24 | 2019-05-24 | 湖北大学 | 路面状况检测方法、处理方法、装置及系统 |
Families Citing this family (150)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
WO2016197079A1 (en) * | 2015-06-05 | 2016-12-08 | Schlumberger Technology Corporation | Wellsite equipment health monitoring |
US10551297B2 (en) * | 2017-09-22 | 2020-02-04 | Saudi Arabian Oil Company | Thermography image processing with neural networks to identify corrosion under insulation (CUI) |
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US10533937B1 (en) | 2018-08-30 | 2020-01-14 | Saudi Arabian Oil Company | Cloud-based machine learning system and data fusion for the prediction and detection of corrosion under insulation |
US10643324B2 (en) | 2018-08-30 | 2020-05-05 | Saudi Arabian Oil Company | Machine learning system and data fusion for optimization of deployment conditions for detection of corrosion under insulation |
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JP7285174B2 (ja) * | 2019-09-04 | 2023-06-01 | 株式会社トプコン | 壁面のひび割れ測定機および測定方法 |
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US11651278B2 (en) * | 2019-12-23 | 2023-05-16 | Saudi Arabian Oil Company | Pipeline sensor integration for product mapping |
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US11247184B2 (en) | 2019-12-30 | 2022-02-15 | Marathon Petroleum Company Lp | Methods and systems for spillback control of in-line mixing of hydrocarbon liquids |
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CN112270663B (zh) * | 2020-10-27 | 2023-11-24 | 北京京能东方建设工程有限公司 | 基于蜂巢网络环境的沥青路面过筛修复系统 |
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CN112541887B (zh) * | 2020-12-02 | 2024-05-03 | 中国华能集团有限公司南方分公司 | 一种火电厂多管道设备运行现场漏水缺陷检测方法 |
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CN112609547A (zh) * | 2020-12-11 | 2021-04-06 | 中山火炬职业技术学院 | 一种沥青路面各施工阶段层厚的监测方法 |
CN112560707B (zh) * | 2020-12-18 | 2022-10-21 | 中国民用航空总局第二研究所 | 基于激光光源的移动式道面检测方法及系统 |
CN112508944A (zh) * | 2020-12-27 | 2021-03-16 | 中信重工开诚智能装备有限公司 | 一种应用于煤矿井下供水管路的泄漏检测方法 |
CN112766251B (zh) * | 2020-12-30 | 2022-06-14 | 广东电网有限责任公司佛山供电局 | 变电设备红外检测方法、系统、储存介质及计算机设备 |
CN112767322B (zh) * | 2021-01-05 | 2023-06-13 | 成都圭目机器人有限公司 | 一种机场水泥道面fod风险评估方法和装置 |
CN112881432B (zh) * | 2021-01-12 | 2022-11-29 | 成都泓睿科技有限责任公司 | 一种带液玻璃瓶瓶口裂纹检测方法 |
CN112763349B (zh) * | 2021-01-21 | 2021-11-26 | 北京航空航天大学 | 一种复合材料结构冲击损伤的监测方法 |
CN112834457B (zh) * | 2021-01-23 | 2022-06-03 | 中北大学 | 基于反射式激光热成像的金属微裂纹三维表征系统及方法 |
CN112669316B (zh) * | 2021-01-29 | 2023-05-30 | 南方电网调峰调频发电有限公司 | 电力生产异常监控方法、装置、计算机设备和存储介质 |
WO2022193008A1 (en) * | 2021-03-15 | 2022-09-22 | Iris R&D Group Inc. | System and method for automatic monitoring of pavement condition |
US11655940B2 (en) | 2021-03-16 | 2023-05-23 | Marathon Petroleum Company Lp | Systems and methods for transporting fuel and carbon dioxide in a dual fluid vessel |
US11578836B2 (en) | 2021-03-16 | 2023-02-14 | Marathon Petroleum Company Lp | Scalable greenhouse gas capture systems and methods |
CN112927223A (zh) * | 2021-03-29 | 2021-06-08 | 南通大学 | 一种基于红外热成像仪的玻璃幕墙检测方法 |
CN113077562B (zh) * | 2021-04-09 | 2021-12-14 | 北京市燃气集团有限责任公司 | 一种燃气管网智能巡检方法与系统 |
CN113203743B (zh) * | 2021-05-20 | 2023-12-12 | 中铁二十一局集团第四工程有限公司 | 一种基于红外热成像分析的路基裂缝检测识别及修复方法 |
CN113252724B (zh) * | 2021-05-21 | 2022-05-31 | 山东中坚工程质量检测有限公司 | 一种外墙保温性能的检测方法 |
CN113177611B (zh) * | 2021-05-24 | 2022-11-01 | 河北工业大学 | 基于力学指标和人工神经网络的路面病害快速巡检方法 |
EP4357746A1 (en) * | 2021-06-16 | 2024-04-24 | Konica Minolta, Inc. | Gas concentration feature quantity estimation device, gas concentration feature quantity estimation method, program, and gas concentration feature quantity inference model generation device |
CN113375065B (zh) * | 2021-07-01 | 2022-05-24 | 北京化工大学 | 管道泄漏监测中趋势信号的消除方法及装置 |
DE102021207204A1 (de) * | 2021-07-08 | 2023-01-12 | Zf Friedrichshafen Ag | System und Verfahren zum Schätzen der Tiefe mindestens eines zumindest zum Teil mit Wasser gefüllten Schlaglochs und entsprechendes Fahrerassistenzsystem |
CN113606502B (zh) * | 2021-07-16 | 2023-03-24 | 青岛新奥燃气设施开发有限公司 | 一种基于机器视觉判断操作人员执行管道漏气检测的方法 |
CN113592798B (zh) * | 2021-07-21 | 2023-08-15 | 山东理工大学 | 一种道路病害智能辨识方法、系统、终端及介质 |
CN113849901B (zh) * | 2021-07-28 | 2024-05-03 | 上海机电工程研究所 | 针对接触换热系数辨识的改进自适应优化方法及系统 |
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US11447877B1 (en) | 2021-08-26 | 2022-09-20 | Marathon Petroleum Company Lp | Assemblies and methods for monitoring cathodic protection of structures |
CN113838078B (zh) * | 2021-09-06 | 2023-06-30 | 中国矿业大学(北京) | 采煤塌陷地裂缝的识别与提取方法、装置及存储介质 |
CN113503974B (zh) * | 2021-09-09 | 2021-11-23 | 江苏沃泰冶金设备有限公司 | 基于pid的热成像检测系统、方法及瓦斯灰输送装置 |
CN113963285B (zh) * | 2021-09-09 | 2022-06-10 | 山东金宇信息科技集团有限公司 | 一种基于5g的道路养护方法及设备 |
CN113884464B (zh) * | 2021-09-27 | 2024-04-26 | 西安空天能源动力智能制造研究院有限公司 | 一种基于红外热像仪的涂层波段发射率外场测量方法 |
US11598689B1 (en) | 2021-10-24 | 2023-03-07 | Philip Becerra | Method of detecting and identifying underground leaking pipes |
CN113777028B (zh) * | 2021-11-11 | 2022-01-18 | 成都理工大学 | 测量凝胶类堵漏材料与岩石壁面粘附强度的装置和方法 |
CN114049336A (zh) * | 2021-11-18 | 2022-02-15 | 国网重庆市电力公司电力科学研究院 | Gis套管温度异常检测方法、装置、设备及可读存储介质 |
CN114037633B (zh) * | 2021-11-18 | 2022-07-15 | 南京智谱科技有限公司 | 一种红外图像处理的方法及装置 |
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CN114577399A (zh) * | 2022-01-18 | 2022-06-03 | 潍柴动力股份有限公司 | 发动机漏气检测方法及检测装置 |
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TWI833168B (zh) * | 2022-02-23 | 2024-02-21 | 南亞科技股份有限公司 | 異常診斷方法 |
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US11686070B1 (en) | 2022-05-04 | 2023-06-27 | Marathon Petroleum Company Lp | Systems, methods, and controllers to enhance heavy equipment warning |
CN114878796B (zh) * | 2022-07-12 | 2022-09-16 | 唐山陆达公路养护有限公司 | 基于道路养护的评估监测平台 |
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US11953161B1 (en) | 2023-04-18 | 2024-04-09 | Intelcon System C.A. | Monitoring and detecting pipeline leaks and spills |
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CN117949143A (zh) * | 2024-03-26 | 2024-04-30 | 四川名人居门窗有限公司 | 一种门窗渗漏检测及反馈系统及方法 |
CN118038283A (zh) * | 2024-04-15 | 2024-05-14 | 贵州黔通工程技术有限公司 | 一种沥青路面隐伏病害检测方法及设备 |
Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
SU894372A1 (ru) * | 1978-06-30 | 1981-12-30 | Центральный Ордена Трудового Красного Знамени Научно-Исследовательский Институт Черной Металлургии Им.И.П.Бардина | Измеритель скорости распространени трещины в металле |
CN201449248U (zh) * | 2009-03-18 | 2010-05-05 | 河海大学 | 一种土体裂隙发育监测仪 |
RU2511275C2 (ru) * | 2012-07-16 | 2014-04-10 | Федеральное государственное унитарное предприятие "Научно-исследовательский институт физических проблем им. Ф.В. Лукина" | Наноструктурный ик-приемник (болометр) с большой поверхностью поглощения |
CN103983513A (zh) * | 2014-05-22 | 2014-08-13 | 中国矿业大学 | 一种采用红外辐射观测煤岩裂隙发育过程的装置及方法 |
CN103983514A (zh) * | 2014-05-22 | 2014-08-13 | 中国矿业大学 | 一种煤岩裂隙发育红外辐射监测试验方法 |
CN104764528A (zh) * | 2015-04-03 | 2015-07-08 | 中国矿业大学 | 一种煤岩裂隙发育过程中的热红外信息去噪方法 |
CN106018096A (zh) * | 2016-07-20 | 2016-10-12 | 中国矿业大学 | 煤岩破裂过程中裂隙发育区的红外辐射监测定位方法 |
Family Cites Families (62)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
JPS5629129A (en) * | 1979-08-17 | 1981-03-23 | Sharp Corp | Measuring system of road surface temperature |
JPS62172249A (ja) * | 1986-01-25 | 1987-07-29 | Kajima Corp | 煙突の劣化診断方法及び装置 |
US4899296A (en) * | 1987-11-13 | 1990-02-06 | Khattak Anwar S | Pavement distress survey system |
US5272646A (en) * | 1991-04-11 | 1993-12-21 | Farmer Edward J | Method for locating leaks in a fluid pipeline and apparatus therefore |
JPH08184398A (ja) * | 1995-01-05 | 1996-07-16 | Mitsubishi Denki Bill Techno Service Kk | 埋設配管漏水箇所特定方法 |
JPH0961138A (ja) * | 1995-08-24 | 1997-03-07 | Mitsubishi Heavy Ind Ltd | ひび割れ抽出装置 |
JP3460896B2 (ja) | 1995-09-14 | 2003-10-27 | ローランド株式会社 | 電子楽器の楽音生成装置 |
JPH1010064A (ja) * | 1996-06-19 | 1998-01-16 | Constec:Kk | モルタル吹き付け法面の点検方法 |
AUPP107597A0 (en) * | 1997-12-22 | 1998-01-22 | Commonwealth Scientific And Industrial Research Organisation | Road pavement deterioration inspection system |
JP2000035372A (ja) * | 1998-07-16 | 2000-02-02 | Ishikawajima Inspection & Instrumentation Co | 赤外線を用いた発泡検査方法 |
JP3205806B2 (ja) * | 1999-04-02 | 2001-09-04 | 鹿島建設株式会社 | アスファルト表面層内部の水探知方法および装置 |
JP2001215164A (ja) * | 2000-02-02 | 2001-08-10 | Kansai Electric Power Co Inc:The | 霧状水滴を利用した真空・ガス漏れ検知装置 |
JP2004191258A (ja) * | 2002-12-12 | 2004-07-08 | Yoshitake Eda | 建築物の漏水路検知方法 |
US6874932B2 (en) * | 2003-06-30 | 2005-04-05 | General Electric Company | Methods for determining the depth of defects |
US7073979B2 (en) * | 2003-11-26 | 2006-07-11 | Aries Industries Incorporated | Method and apparatus for performing sewer maintenance with a thermal sensor |
CN100402753C (zh) * | 2003-12-10 | 2008-07-16 | 刘世俊 | 高级路面裂纹处理工艺方法 |
US7358860B2 (en) * | 2005-03-31 | 2008-04-15 | American Air Liquide, Inc. | Method and apparatus to monitor and detect cryogenic liquefied gas leaks |
CN101070947A (zh) | 2006-04-28 | 2007-11-14 | 王明根 | 管道接缝渗漏检测系统 |
CN1936414A (zh) * | 2006-08-12 | 2007-03-28 | 陈宜中 | 非导电材料供水管道检漏法 |
US8288726B2 (en) * | 2006-12-19 | 2012-10-16 | Weil Gary J | Remote sensing of subsurface artifacts by use of visual and thermal imagery |
JP5028681B2 (ja) * | 2009-03-17 | 2012-09-19 | 西日本高速道路エンジニアリング四国株式会社 | 構造物の損傷深さ判定方法とその装置及び構造物の損傷処置判定方法とその装置 |
CN101701919B (zh) * | 2009-11-20 | 2011-05-11 | 长安大学 | 一种基于图像的路面裂缝检测系统及检测方法 |
CN102135234A (zh) * | 2010-01-27 | 2011-07-27 | 捷达世软件(深圳)有限公司 | 水管泄漏监控系统及方法 |
US20110221906A1 (en) * | 2010-03-12 | 2011-09-15 | Board Of Regents, The University Of Texas System | Multiple Camera System for Automated Surface Distress Measurement |
CN101845787A (zh) * | 2010-04-09 | 2010-09-29 | 同济大学 | 基于双目视觉的水泥混凝土路面错台检测装置及方法 |
CN102155628A (zh) * | 2010-12-01 | 2011-08-17 | 广西大学 | 地下排水管道渗漏检测方法及装置 |
US9582928B2 (en) * | 2011-01-13 | 2017-02-28 | Samsung Electronics Co., Ltd. | Multi-view rendering apparatus and method using background pixel expansion and background-first patch matching |
CN102108666B (zh) * | 2011-01-17 | 2012-05-30 | 长安大学 | 一种沥青路面施工质量实时控制方法 |
CN102182137A (zh) * | 2011-02-25 | 2011-09-14 | 广州飒特电力红外技术有限公司 | 路面缺陷检测系统及方法 |
CN102374385A (zh) * | 2011-07-21 | 2012-03-14 | 王斌 | 一种管道漏水检测装置及方法 |
CN102537667B (zh) * | 2011-12-29 | 2013-09-25 | 杭州翰平电子技术有限公司 | 一种地下水管渗漏检测定位系统及方法 |
CN102621419A (zh) * | 2012-03-28 | 2012-08-01 | 山东省电力学校 | 基于激光和双目视觉图像对线路电气设备自动识别和监测方法 |
CN102636313B (zh) * | 2012-04-11 | 2014-12-03 | 浙江工业大学 | 基于红外热成像图像处理的渗漏源检测装置 |
CN103308521A (zh) * | 2012-08-29 | 2013-09-18 | 中国人民解放军第二炮兵工程大学 | 一种增强红外热波检测图像缺陷对比度的方法 |
CN102927448B (zh) * | 2012-09-25 | 2016-12-21 | 北京声迅电子股份有限公司 | 管道无损检测方法 |
CN103217256A (zh) * | 2013-03-20 | 2013-07-24 | 北京理工大学 | 基于红外图像的局部灰度-熵差的泄漏检测定位方法 |
CN103321129A (zh) * | 2013-06-18 | 2013-09-25 | 中山市拓维电子科技有限公司 | 基于3g网络的红外热像的远程路面施工诊断系统及方法 |
CN103808760B (zh) * | 2013-12-12 | 2017-04-26 | 交通运输部公路科学研究所 | 混凝土结构红外热成像无损检测用热激励装置 |
CN103882891B (zh) * | 2014-01-16 | 2016-06-08 | 同济大学 | 利用红外热场快速预测地下连续墙侧壁渗漏的方法 |
CN103912791B (zh) * | 2014-01-26 | 2016-05-04 | 清华大学深圳研究生院 | 地下管网泄漏探测方法 |
US9857228B2 (en) * | 2014-03-25 | 2018-01-02 | Rosemount Inc. | Process conduit anomaly detection using thermal imaging |
US10576907B2 (en) * | 2014-05-13 | 2020-03-03 | Gse Technologies, Llc | Remote scanning and detection apparatus and method |
CN104776318A (zh) * | 2014-05-19 | 2015-07-15 | 白运福 | 一种地下水管漏水检测处理装置 |
CN104048969A (zh) * | 2014-06-19 | 2014-09-17 | 樊晓东 | 一种隧道病害的识别方法 |
CN104034733A (zh) * | 2014-07-02 | 2014-09-10 | 中国人民解放军国防科学技术大学 | 基于双目视觉监测与表面裂纹图像识别的寿命预测方法 |
CN104574393B (zh) * | 2014-12-30 | 2017-08-11 | 北京恒达锦程图像技术有限公司 | 一种三维路面裂缝图像生成系统和方法 |
CN104713885B (zh) * | 2015-03-04 | 2017-06-30 | 中国人民解放军国防科学技术大学 | 一种用于pcb板在线检测的结构光辅助双目测量方法 |
CN104749187A (zh) * | 2015-03-25 | 2015-07-01 | 武汉武大卓越科技有限责任公司 | 基于红外温度场和灰度图像的隧道衬砌病害检测装置 |
CN105113375B (zh) * | 2015-05-15 | 2017-04-19 | 南京航空航天大学 | 一种基于线结构光的路面裂缝检测系统及其检测方法 |
SE539312C2 (en) * | 2015-06-10 | 2017-06-27 | Conny Andersson Med Firma Ca Konsult | A method of determining the quality of a newly produced asphalt pavement |
CN105465613B (zh) * | 2015-11-19 | 2018-02-23 | 中建七局第二建筑有限公司 | 城市地下排水管道渗漏定位系统及其施工方法 |
CN205115977U (zh) * | 2015-11-23 | 2016-03-30 | 张先 | 道路桥梁沥青路面裂缝检测装置 |
CN105719283A (zh) * | 2016-01-18 | 2016-06-29 | 苏州科技学院 | 一种基于Hessian矩阵多尺度滤波的路面裂缝图像检测方法 |
CN105717163A (zh) * | 2016-01-29 | 2016-06-29 | 中国商用飞机有限责任公司 | 红外热像检测缺陷的方法 |
CN105527165B (zh) * | 2016-02-02 | 2018-07-24 | 山东省交通科学研究院 | 一种沥青路面裂缝荷载响应相对位移测试方法及测试装置 |
CN105938620B (zh) * | 2016-04-14 | 2018-12-25 | 北京工业大学 | 一种小口径管内焊缝表面缺陷识别装置 |
CN205786366U (zh) * | 2016-05-26 | 2016-12-07 | 国网浙江省电力公司宁波供电公司 | 一种电缆隧道水泥管片缺陷渗水特征红外热像实验装置 |
CN205748654U (zh) * | 2016-06-21 | 2016-11-30 | 国家电网公司 | 基于红外热成像的变压器实时监测系统 |
CN106124949B (zh) * | 2016-08-30 | 2019-08-13 | 国网山东省电力公司济南供电公司 | 一种基于热红外成像技术对绝缘子故障在线监测方法 |
CN206573258U (zh) | 2017-02-20 | 2017-10-20 | 广东工业大学 | 一种管道渗漏检测装置 |
CN206629279U (zh) | 2017-03-30 | 2017-11-10 | 中建地下空间有限公司 | 一种应用于综合管廊的移动巡检系统 |
WO2018216629A1 (ja) * | 2017-05-22 | 2018-11-29 | キヤノン株式会社 | 情報処理装置、情報処理方法、及びプログラム |
-
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-
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Patent Citations (7)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
SU894372A1 (ru) * | 1978-06-30 | 1981-12-30 | Центральный Ордена Трудового Красного Знамени Научно-Исследовательский Институт Черной Металлургии Им.И.П.Бардина | Измеритель скорости распространени трещины в металле |
CN201449248U (zh) * | 2009-03-18 | 2010-05-05 | 河海大学 | 一种土体裂隙发育监测仪 |
RU2511275C2 (ru) * | 2012-07-16 | 2014-04-10 | Федеральное государственное унитарное предприятие "Научно-исследовательский институт физических проблем им. Ф.В. Лукина" | Наноструктурный ик-приемник (болометр) с большой поверхностью поглощения |
CN103983513A (zh) * | 2014-05-22 | 2014-08-13 | 中国矿业大学 | 一种采用红外辐射观测煤岩裂隙发育过程的装置及方法 |
CN103983514A (zh) * | 2014-05-22 | 2014-08-13 | 中国矿业大学 | 一种煤岩裂隙发育红外辐射监测试验方法 |
CN104764528A (zh) * | 2015-04-03 | 2015-07-08 | 中国矿业大学 | 一种煤岩裂隙发育过程中的热红外信息去噪方法 |
CN106018096A (zh) * | 2016-07-20 | 2016-10-12 | 中国矿业大学 | 煤岩破裂过程中裂隙发育区的红外辐射监测定位方法 |
Cited By (1)
Publication number | Priority date | Publication date | Assignee | Title |
---|---|---|---|---|
CN109801282A (zh) * | 2019-01-24 | 2019-05-24 | 湖北大学 | 路面状况检测方法、处理方法、装置及系统 |
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