JP2018185228A - Mobile flaw detector - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a mobile flaw detector capable of accurately and efficiently detecting flaws and defects inherent in a structure while travelling.SOLUTION: The mobile flaw detector comprises: a reception part for receiving a positioning signal from a positioning satellite; a laser scanner; an infrared camera; and a calculation part. The calculation part is configured to generate a piece of pixel point group data in which a piece of temperature information is added to a three-dimensional point group by using a piece of three-dimensional point group data, which includes a position of a moving body and a piece of three-dimensional position information of an ambient structure which is generated using the distance and the azimuth of the ambient structure captured by the laser scanner and a piece of infrared image data corresponding to the three-dimensional point group, and detect defects of the structure from three-dimensional coordinate values and the temperature information of the pixel point group data.SELECTED DRAWING: Figure 3

Description

  The present invention relates to a mobile flaw detector that measures the position and temperature of a structure or the like around a road while moving by a vehicle and detects scratches or defects inherent in the structure.

Conventionally, it is known that measuring the surface temperature of a structure is effective as a method for detecting scratches and defects inherent in the structure such as a building.
The surface temperature of concrete structures installed outdoors rises due to the effects of direct sunlight during the day, but the parts with defects such as cracks and delamination inside the structure are compared with the surroundings. The portion is characterized in that the temperature does not increase and a temperature difference occurs on the surface of the structure.
Using this feature, a technique for detecting flaws and defects inherent in a structure by visualizing the surface temperature distribution as a thermography (infrared thermography) using an infrared camera is disclosed (for example, patents). Reference 1).

Japanese Patent No. 1515973 Japanese Patent No. 5522789 Japanese Patent No. 4344869

“Evolution of Mobile Mapping System (MMS)” Mitsubishi Electric Technical Report Vol. 90 ・ No. 2.2016

According to infrared thermography, it is possible to estimate the presence of “internal defects” that are difficult to appear on the surface shape, such as floating, peeling, and cavities generated in a concrete structure.
However, there is a possibility that the temperature difference occurs not only on the surface of the structure, but also on the temperature difference on the surface of the structure, there may be causes other than defects. Therefore, it is necessary to comprehensively determine the presence or absence of defects by measuring the temperature multiple times or using other measurement methods such as visual observation or sounding method. The detection operation for detecting defects is complicated and takes time.

  For this reason, when detecting structures or structures in the vicinity of roads, such as tunnel walls, road side walls, or scratches or defects inherent in roads, stop traffic and restrict traffic. Therefore, there is a problem that the detection work is carried out and the influence on general traffic vehicles is large.

  The present invention has been made to solve such problems, and is inherent in structures around roads such as tunnels and bridges without affecting traffic such as traffic restrictions. The object is to provide a mobile flaw detector capable of efficiently and accurately detecting flaws and defects.

A mobile flaw detection apparatus according to the present invention is a mobile flaw detection apparatus that is mounted on a moving body and detects a defect in a surrounding structure, and includes a receiving unit that receives a positioning signal from a positioning satellite, a laser scanner, It has an outside camera and a calculation unit,
The calculation unit is configured to calculate the position of the surrounding structure generated by using the position of the moving body calculated from the positioning signal received by the reception unit and the distance and direction to the surrounding structure acquired by the laser scanner. Temperature information in the three-dimensional point group using three-dimensional point group data including three-dimensional position information and infrared image data of the surrounding structure acquired by the infrared camera corresponding to the three-dimensional point group The pixel point group data to which is added is generated, and the defect of the surrounding structure is detected using the three-dimensional point group of the pixel point group data and the temperature information.

  According to the mobile flaw detection apparatus according to the present invention, the diagnosis of the structure around the road can be performed more accurately and efficiently than before by integrating the three-dimensional shape information and temperature information of the surface of the structure around the road. Can do.

1 is a schematic diagram showing a configuration of a mobile flaw detection apparatus 100 according to Embodiment 1. FIG. It is the schematic which shows the structure of the measurement stand 10 concerning Embodiment 1. FIG. 1 is a block diagram illustrating a configuration of a mobile flaw detection apparatus 100 according to Embodiment 1. FIG. 3 is a flowchart showing a structure three-dimensional model generation method according to Embodiment 1; 3 is a functional configuration diagram of a structure three-dimensional model generation unit 22 according to Embodiment 1. FIG. It is a flowchart figure of the structure three-dimensional model generation process (S300) which concerns on the form 1 of a child. FIG. 5 is a flowchart showing a detection process for detecting a defect on the surface of a structure according to the first embodiment. It is a figure which shows an example of the temperature distribution of the roadway wall surface based on Embodiment 1. FIG. It is a figure which shows an example of the temperature distribution of the roadway wall surface based on Embodiment 2. FIG.

Embodiment 1 FIG.
FIG. 1 is a schematic diagram showing a configuration of a mobile flaw detector 100 according to the first embodiment.
The mobile flaw detection apparatus 100 includes a measurement platform 10 and a computer 20, and both the measurement platform 10 and the computer 20 are mounted on a vehicle 200.

FIG. 2 is a schematic diagram illustrating a configuration of the measurement gantry 10 according to the first embodiment.
The measurement platform 10 includes a laser scanner 11, an infrared camera 12, an IMU (Internal Measurement Unit) 13, several sets of GPS receivers 14, and a GPS antenna 15.

FIG. 3 is a block diagram showing the configuration of the mobile flaw detection apparatus 100 according to the first embodiment.
The computer 20 includes a position locating unit 21 for calculating the self-position and posture of the vehicle 200 based on information input from the IMU 13 and the GPS receiver 14 of the measurement gantry 10, the laser scanner 11 of the measurement gantry 10, and infrared rays. Based on information input from the camera 12 and the position locator 21, a structure 3D model generator 22 that generates a structure 3D model with temperature information, and a memory that stores the structure 3D model with temperature information The apparatus 23 includes a structure flaw defect detection processing unit 24 that detects flaws and defects inside the structure from the structure three-dimensional model stored in the storage device 23.

The laser scanner 11 is a device that measures the distance to a structure existing around the vehicle 200 and outputs the data to the three-dimensional point cloud data generation unit 21. The laser scanner 11 is also called a laser radar or LRF (laser range finder).
The laser scanner 11 repeatedly emits the laser in a short cycle while repeatedly swinging the laser emission surface at 180 degrees on the left and right sides, and receives each laser that is reflected back to the feature (for example, road surface). Then, the laser scanner 11 calculates the “measurement time” at which the laser is emitted or received, the “azimuth” at which the laser is emitted, and the “distance” based on the time from when the laser is emitted until it is received as the “distance azimuth point”. To measure.
Data indicating a plurality of distance azimuth points measured by the laser scanner 11 is a distance azimuth point group 291 described later.

The infrared camera 12 is a device that measures the surface temperature of a structure existing around the vehicle 200 and outputs the temperature data (temperature information) to the three-dimensional point cloud data generation unit 22 for each pixel of the camera.
The infrared camera 12 repeatedly performs imaging at a predetermined timing (time interval, travel distance interval, etc.).
Data including a plurality of “camera images” captured by the infrared camera 12 and “imaging time” of each camera image is infrared image data 293 described later.

  The IMU 13 is a device that acquires inertial signal data and outputs the inertia signal data to the position location unit 21. The inertia signal data includes inertia acceleration information and angular velocity information. The inertia signal data is associated with the measurement time.

The GPS receiver 14 is a device that receives a signal output from a satellite such as a GPS satellite by the GPS antenna 15 and outputs a GPS signal processing result to the position location unit 21.
The data including the observation information obtained by the GPS receiver 14 and the three-axis angular velocities measured by the IMU 13 is position data 292 to be described later.

FIG. 4 is a flowchart showing the structure three-dimensional model generation method according to the first embodiment. Below, the structure three-dimensional model generation method in Embodiment 1 is demonstrated based on FIG.
The road three-dimensional model generation method corresponding to the structure three-dimensional model according to the first embodiment is described in detail in Patent Document 2, and only main parts are described here.

The vehicle 200 acquires a distance azimuth point group 291, position data 292, and infrared image data 293 on the road (road surface) (S <b> 100).
The structure three-dimensional model generation unit 22 generates a three-dimensional point group 198 indicating three-dimensional coordinate values of many points on the road and surrounding structures based on the distance / azimuth point group 291 and the position data 292 acquired in S100. (S200).
The structure 3D model generation unit 22 generates the structure 3D model 194 based on the 3D point group 198 generated in S200 and the infrared image data 293 acquired in S100 (S300).

FIG. 5 is a functional configuration diagram of the structure three-dimensional model generation unit 22 according to the first embodiment.
A functional configuration of the structure three-dimensional model generation unit 22 according to the first embodiment will be described below with reference to FIG.

  The structure three-dimensional model generation unit 22 includes an infrared image processing unit 110 (an example of an image range extraction unit), a pixel point group generation unit 120, a three-dimensional model generation unit 140 (an example of a model storage unit), and a three-dimensional point group. A generation unit 180 and a processing area 190 are provided.

The distance direction point group 291, the position data 292, and the infrared image data 293 acquired by the vehicle 200 are temporarily stored in the processing area 190.
In addition, parameter data 299 indicating the measurement conditions of the vehicle 200 (such as the mounting position / orientation of the laser scanner 11 and the infrared camera 12) is also temporarily stored in the processing area 190.

  The three-dimensional point group generation unit 180 is based on the distance and azimuth point group 291, the position data 292, and the parameter data 299, and indicates a three-dimensional point group 198 (coordinates) indicating the three-dimensional coordinate values of the road and the structures around the road. An example of a point cloud) is generated using a CPU (Central Processing Unit).

  Based on the infrared image data 293 and the parameter data 299, the infrared image processing unit 110 is a range in which roads and structures around the road are reflected, and a range in which the resolution of the image is high is processed from each camera image. The image 191 is extracted using the CPU.

  For example, the infrared image processing unit 110 extracts a predetermined range on the viewpoint side of the camera image (a predetermined range before the viewpoint) as the processing range image 191.

The pixel point group generation unit 120 is based on the processing range image 191, the three-dimensional point group 198, and the parameter data 299, and the portion of the road and the structure around the road reflected in the pixels of the processing range image 191 (an example of a specific point). Are calculated for each pixel using the CPU.
The pixel point group generation unit 120 includes a pixel point group 192 that indicates the three-dimensional coordinate values of the roads and the structures around the roads of the plurality of pixels of the processing range image 191 and the temperature information of the plurality of pixels of the processing range image 191. Is generated using a CPU. The temperature information is information on the temperature of each pixel to be imaged that is measured by the infrared camera 12.

  The three-dimensional model generation unit 140 generates data including the pixel point group 192 as a structure three-dimensional model 194 (an example of a regional model) using the CPU.

The processing area 190 stores data used by the structure three-dimensional model generation unit 22 in a storage medium.
Distance azimuth point group 291, position data 292, infrared image data 293, parameter data 299, three-dimensional point group 198, processing range image 191, pixel point group 192, and structure three-dimensional model 194 are stored in processing area 190. This is an example of data.

The parameter data 299 includes, for example, the following information.
(1) Image storage directory: a directory name (a storage destination of the infrared image data 293) in which camera images acquired by the measurement vehicle 200 are stored with an acquisition time tag.
(2) Infrared camera image ID: information for identifying a camera image.
(3) Processing range: A pixel range used for generating a three-dimensional model of a structure in an infrared camera image.
(4) Laser three-dimensional point cloud directory: a directory storing the three-dimensional point cloud 198 of roads and structures around the road.
(6) Infrared camera acquisition time / position / posture: Information indicating the position / posture of the infrared camera when an infrared camera image is taken.
(7) Infrared camera mounting offset: Information indicating the position and orientation of the infrared camera from the center of the vehicle body.
(8) Laser mounting offset: Information indicating the laser position and orientation from the center of the vehicle body.

  Next, a method for generating the three-dimensional point group 198 by the three-dimensional point group generation unit 180 of the structure three-dimensional model generation unit 22 will be described.

The three-dimensional point group generation unit 180 generates a three-dimensional point group 198 based on the distance / azimuth point group 291, the position data 292, and the parameter data 299.
The parameter data 299 includes the mounting offset of the laser scanner 11 in the vehicle body coordinate system. The mounting offset of the laser scanner 11 indicates in what position (tilt) the laser scanner 11 is installed at which position of the vehicle 200.
The three-dimensional point group 198 is data indicating a three-dimensional coordinate value corresponding to each distance direction point in the world coordinate system.

  Hereinafter, a method of generating the three-dimensional point group 198 will be described.

Based on the position data 292, the three-dimensional point group generation unit 180 determines the position and orientation value of the measurement vehicle 200 when measuring each distance direction point. The position / orientation value indicates a three-dimensional coordinate value (x, y, z) and a three-dimensional attitude angle (roll angle, pitch angle, yaw angle).
The three-dimensional point group generation unit 180 converts the distance / azimuth point group 291 into the world coordinate system based on the mounting offset of the laser scanner 11.
The three-dimensional point group generation unit 180 generates a three-dimensional point group 198 based on the position / orientation value and the distance / azimuth point group 291. Each three-dimensional point of the three-dimensional point group 198 is indicated by a coordinate value of a point separated from the position and orientation value by the distance indicated by the distance azimuth point in the azimuth indicated by the distance azimuth point. For the calculation of the three-dimensional point, the position and orientation value and the distance direction point at the same time are used.

  Details of the method of generating the three-dimensional point group 198 are disclosed in, for example, Patent Document 3.

  Next, a road three-dimensional model generation process (S300) for generating a road three-dimensional model 194 based on the three-dimensional point group 198 will be described.

  FIG. 6 is a flowchart of the road three-dimensional model generation process (S300) in the first embodiment. The road three-dimensional model generation process (S300) in the first embodiment will be described below with reference to FIG.

  First, an outline of the road three-dimensional model generation process (S300) will be described.

The image processing unit 110 selects one camera image (S311), and extracts a predetermined processing range from the selected image as a processing range image 191 (S312).
Based on the three-dimensional point group 198, the pixel point group generation unit 120 generates a pixel point group 192 indicating three-dimensional coordinate values and temperature information corresponding to each pixel of the processing range image 191 (S320).
If there is an unselected camera image (S330 “YES”), the process returns to S311.
If there is no unselected camera image (S330 “NO”), the process proceeds to S340.
The three-dimensional model generation unit 140 generates data including the pixel point group 192 as a road three-dimensional model 194 (S340).

  Next, details of the road three-dimensional model generation process (S300) will be described.

<S311>
The image processing unit 110 selects one camera image in order of imaging time from a large number of camera images included in the infrared image data 293.
Hereinafter, the camera image selected in S311 is referred to as “selected image”.
After S311, the process proceeds to S312.

<S312>
The image processing unit 110 extracts a predetermined processing range as a processing range image 191 from the selected image.
Each camera image includes features other than roads such as buildings, utility poles, and walls.
In addition, the resolution (resolution) of a portion where a range close to the infrared camera 12 (viewpoint) is reflected is high, but the resolution of a portion where a range far from the infrared camera 12 is reflected is low.
The predetermined processing range is such that the specifications of the infrared camera 12 (view angle, image resolution, etc.) and the mounting of the infrared camera 12 are such that a road portion reflected at a high resolution is extracted as a processing range image 191 from the selected image. It is determined based on an offset or the like. The specifications and attachment offset of the infrared camera 12 are information included in the parameter data 299. In order to save processing waste, the imaging range of each camera image is specified based on the specification of the infrared camera 12, the mounting offset, and the imaging point, and the processing range is determined so as to exclude the overlapping range of each camera image. Also good.

  Returning to FIG. 6, the description of the road three-dimensional model generation process (S300) will be continued.

<S320>
Based on the three-dimensional point group 198, the pixel point group generation unit 120 generates a pixel point group 192 indicating the three-dimensional coordinate value and temperature information corresponding to each pixel of the processing range image 191.
By S320, a three-dimensional point group (pixel point group 192) having a resolution of the camera image level can be generated.

<S330>
The image processing unit 110 determines whether an unselected camera image remains.
If an unselected camera image remains (YES), the process returns to S311.
If no unselected camera image remains (NO), the process proceeds to S340.

<S340>
The three-dimensional model generation unit 140 generates data that combines the pixel point group 192 and the pixel interpolation point group 193 as a road three-dimensional model 194.
The three-dimensional model generation unit 140 outputs the generated road three-dimensional model 194 (display, printing, storage, etc.).
By S340, the road three-dimensional model generation process (S300) ends.

  The roads and roads on which the vehicle 200 has traveled are displayed by displaying each three-dimensional point (pixel point group 192) with temperature information constituting the structure three-dimensional model 194 on the basis of the respective temperature information and three-dimensional coordinate values. The temperature distribution of surrounding structures can be expressed.

  The structure three-dimensional point cloud data generation unit 22 adds temperature information based on the data obtained from the laser scanner 11 and the infrared camera 12 and the position and orientation of the vehicle obtained from the position location unit 21. Generate dimension point cloud data.

  The three-dimensional point cloud data to which the temperature information is given is stored in the storage device 23 together with the measurement time.

  Next, the structure flaw defect detection processing unit 24 performs a process of detecting a defect of the structure to be measured from the pixel point group 192 and the structure three-dimensional model 194 accumulated in the storage device 23.

Below, the processing method which detects the defect of a structure is demonstrated using FIG. 7, FIG.
FIG. 7 is a flowchart showing a detection process for detecting defects on the surface of the structure according to the first embodiment. FIG. 8 is a diagram showing an example of the temperature distribution on the wall surface beside the roadway.

<S241>
In FIG. 7, first, the structure flaw defect detection processing unit 24 extracts the pixel point group 192 and the structure three-dimensional model 194 of the inspection object specified in advance from the storage device 23 (S241 in FIG. 7).
The inspection object can be designated in advance by the user using the image of the infrared camera 12 displayed on the monitor. Or you may make it designate a range using a three-dimensional coordinate value. Further, the inspection target may be specified as a target area (target range) instead of being specified as a structure. In the example of FIG. 8, the wall surface 500 provided on the side of the road is used as the inspection object. The wall surface 500 is provided, for example, for sound insulation on an expressway, and is an inspection for detecting scratches or defects on the wall surface 500. The wall surface 500 may be an inner wall of the tunnel.

<S242>
Next, the structural flaw defect detection processing unit 24 creates a temperature distribution of the inspection object based on the temperature information given to the extracted pixel point group 192. Based on the created temperature distribution, the structure flaw defect detection processing unit 24 extracts a pixel point group that is hotter in a spot shape than the surroundings (S242).
Whether the temperature is high in a spot shape is determined by taking a difference from the temperature information of the surrounding pixel point group 192 and determining whether the difference is equal to or greater than a predetermined threshold. The threshold can be set each time from the monitor screen while the user looks at the image of the infrared camera.

<S243>
The structural flaw defect detection processing unit 24 groups the high temperature locations from the grouped status of the extracted high temperature locations (S243).
In the example of FIG. 8, the pixel point group 192 (white) represented by a white circle visually represents that the temperature is low from the temperature information given to the pixel point group 192, and the pixel point group 192 ( (Black) visually represents that the temperature is high based on the temperature information given to the pixel point group 192.
The structural flaw defect detection processing unit 24 groups a plurality of pixel point groups 192 (black) into groups A150, B151, and C152.

<S244>, <S245>
Next, the structural flaw defect detection processing unit 24 acquires the three-dimensional coordinate values of the grouped pixel point group 192 (black) and the three-dimensional coordinate values of the surrounding pixel point group 192 (S244), and performs grouping. It is determined whether there is a change in shape between the pixel point group 192 (black) and the surrounding pixel point group 192 (S245).
For example, in the example of FIG. 8, the group C152 in which the pixel point group 192 (black) is gathered is the location of the protrusion 160 provided on the wall surface 500, which is the third order of the grouped pixel point group 192 (black). It is determined from the original coordinate value and the three-dimensional coordinate value of the surrounding pixel point group 192.
On the other hand, in the group A150 and the group B151, the shape of the high temperature portion and the shape of the periphery of the high temperature portion are both flat, and the shape does not change, indicating that the group of pixel points 192 (group A, group B) ( (Black) three-dimensional coordinate values and the three-dimensional coordinate values of the surrounding pixel point group 192.

<S246>
Next, the structural flaw defect detection processing unit 24 extracts a group of pixel point groups 192 (black) whose shape does not change as internal defect part candidates based on the three-dimensional coordinate values of the pixel point groups (S246).
In the example of FIG. 8, group A150 and group B151 are extracted as internal defect portion candidates.
As mentioned above, in concrete structures installed outdoors, the surface temperature rises due to the influence of direct sunlight during the daytime, but there are cracks and delamination inside the structure. Compared with, the temperature of the portion does not increase and a temperature difference is generated on the surface of the structure. The group A150 in FIG. 8 and the group B151 have a temperature difference despite no change in the surface shape. For this reason, the group A150 and the group B151 are extracted as “internal defect portion candidates” on the assumption that there is a high possibility that defects such as cracks and delamination have occurred inside the structure.

<S247>
The structural flaw defect detection processing unit 24 displays the three-dimensional model 194 using the group A150 and the pixel point group 192 included in the group B151 on the monitor unit (not shown) (S247). The user can check the group A 150 and the group B 151 on the monitor unit.

<S248>
Next, the structural flaw defect detection processing unit 24 extracts a group of pixel point groups 192 (black) whose shape has changed based on the three-dimensional coordinate value of the pixel point group as a determination pending portion (S248).
In the example of FIG. 8, the group C152 is extracted as a determination suspension part.
If there is any deposit on the surface of the structure, there is a high possibility that the deposit causes a temperature difference, not an internal defect. In the example of FIG. 8, it can be considered that the protrusion 160 is exposed to sunlight and the surface temperature of the structure is increased. Therefore, the structure flaw defect detection processing unit 24 extracts the group C not as an internal defect portion candidate but as a determination pending portion.

<S249>
The structural flaw defect detection processing unit 24 displays the three-dimensional model 194 using the pixel point group 192 included in the group C152 on the monitor unit (not shown) (S249).
The user can check the group C152 on the monitor unit. If it is possible to determine that the defect is not a defect by confirmation on the monitor unit, the user can delete the group C from the determination pending portion. In addition, if the user determines that the possibility of a defect is high by confirmation on the monitor unit, the group C can be changed from “determination pending part” to “internal defect part candidate” by operation of the monitor or the like. .

  In this way, the structural flaw defect detection processing unit 24, based on the three-dimensional coordinate value and temperature information given to each of the pixel point groups, a portion in which a defect such as a crack or peeling is generated inside “ It can be extracted as “internal defect part candidate”.

  The computer 20 including the structural flaw defect detection processing unit 24 is mounted on the vehicle 200. As a result, scratches and defects inherent in structures around roads, such as walls, tunnels, and bridges, can be efficiently handled without affecting conventional vehicles such as traffic restrictions. It is an object of the present invention to provide a mobile flaw detection apparatus that can accurately detect a problem.

Embodiment 2. FIG.
In the second embodiment, a description will be given of a detection method for detecting a scratch or a defect when a structure to be inspected has a sunlit portion and a shaded portion.
FIG. 9 is a diagram showing an example of the temperature distribution on the roadway wall surface according to the second embodiment. In the second embodiment, a scratch or a defect inherent in the structure is detected when the sunlit portion 400 and the shaded portion 410 are on the surface.
In the first embodiment, as described above, the pixel point group that is higher in the spot shape than the surroundings is extracted from the temperature information of the pixel point group (S242 in FIG. 7). Whether the temperature is high or not is determined by, for example, comparison with a predetermined threshold value. However, when there are a sunlit portion and a shaded portion to be inspected, it is difficult to accurately determine whether the temperature is high with one threshold value.
Therefore, in the second embodiment, the structural flaw defect detection processing unit 24 sets threshold values for the shaded portion and the sunny portion, and determines whether the temperature is high using the threshold values set for each. To.
Thereby, the defect diagnosis of the structure around the road can be performed more accurately and efficiently by integrating the three-dimensional shape information and the temperature information on the surface of the structure around the road.

  In addition, when the shaded part and the sunlit part are already known, by adding further information on the sunshade or the shade to the three-dimensional coordinate value and temperature information added to the pixel point group 192, the target part is the sunlit part. It can be judged whether it is in the shade or in the shade. If it is unclear whether it is a shaded part or a sunny part, the temperature information in a given area is averaged, and the temperature difference is used to determine whether the area is sunny or shaded. Also good.

DESCRIPTION OF SYMBOLS 10 Measurement stand, 11 Laser scanner, 12 Infrared camera, 13 IMU, 14 GPS receiver, 15 GPS antenna, 20 Computer, 21 Positioning part, 22 Structure three-dimensional model generation part, 23 Storage device, 24 Structure damage Defect detection processing unit, 100 mobile flaw detector, 110 infrared image processing unit, 120 pixel point group generation unit, 140 three-dimensional model generation unit, 150 group A, 151 group B, 152 group C, 153 group D, 160 projection , 180 3D point group generation unit, 190 processing area, 191 processing range image, 192 pixel point group, 192 (black) pixel point group (high temperature display), 192 (white) pixel point group (low temperature display), 192 (gray) ) Pixel point group (medium temperature display), 194 Three-dimensional model of structure, 198 Three-dimensional point group, 200 Vehicle, 291 Releasing orientation point cloud 292 position data, 293 infrared image data, 299 parameter data 400 sunlit portion, 410 shaded portions, the wall surface of the peripheral 500 road, 600 road, 610 road white line

Claims (4)

A mobile flaw detector that is mounted on a moving body and detects defects in surrounding structures,
A receiver for receiving a positioning signal from a positioning satellite;
A laser scanner,
An infrared camera,
A calculation unit;
With
The calculation unit is configured to calculate the position of the surrounding structure generated by using the position of the moving body calculated from the positioning signal received by the reception unit and the distance and direction to the surrounding structure acquired by the laser scanner. 3D point cloud data including 3D position information;
Infrared image data of the surrounding structure acquired by the infrared camera corresponding to the three-dimensional point group;
To generate pixel point cloud data with temperature information added to the three-dimensional point cloud,
A mobile flaw detector characterized by detecting a defect of a surrounding structure using a three-dimensional coordinate value of the pixel point group data and temperature information. The calculation unit uses the temperature information to extract a set of pixel point groups that are hotter than the surrounding temperature,
Using the three-dimensional coordinate values, the shape of the set of the extracted pixel point group is compared with the shape of the pixel point group around the extracted pixel point group, and a defect in the surrounding structure is detected. The mobile flaw detector according to claim 1, wherein:   The calculation unit corresponds to the extracted pixel point group when the shape of the set of the extracted pixel point group and the shape of the pixel point group around the extracted pixel point group are both flat. The mobile flaw detection apparatus according to claim 2, wherein the structure is determined to be defective. The mobile flaw detector according to claim 1, wherein the defect is a defect inside the structure.

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