Quality control method, quality control system, and quality control program

Abstract

To improve traceability relating to fluctuation in a banking material in civil engineering work.SOLUTION: This quality control method causes a computer to acquire image data obtained by imaging the surface of a banking after scattering and position information indicating a position where the image data is obtained through the imaging, and store the image data and the position information in association in a storage device.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to a quality control method, a quality control system, and a quality control program.

Background Art

[0002] Conventionally, a system for evaluating the compaction state of the ground has been proposed (for example, Patent Document 1). The system includes an acceleration sensor that acquires the vibration acceleration of a vibration roller that compacts the ground, a GNSS that acquires the position information of the vibration roller, a compaction number calculation unit that divides the ground into a plurality of regions and calculates the number of compactions of each region by the vibration roller, a compaction index value calculation unit that calculates an index value indicating the compaction state of the ground for each region by performing frequency analysis on the vibration acceleration, a database that stores the calculated index value in association with the number of compactions for each region, and an output processing unit that can output the number of compactions and the index value stored in the database for each of the plurality of regions.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] If some problem occurs in the future, there is a risk that quality verification cannot be sufficiently performed retrospectively based only on information such as the ground compaction index value using vibration acceleration for the desired part.

[0005] The present invention aims to improve the traceability regarding the variation of embankment materials in civil engineering works.

Means for Solving the Problems

[0006] In order to solve the above problems, the present invention includes the following aspects. (Aspect 1) Obtaining image data of the surface of the embankment after spreading and position information indicating the position where the image data was captured, Associating the image data with the position information and storing it in a storage device, A quality control method executed by a computer. (Aspect 2) The image data is of a standard object colored with a predetermined color and further captured, The computer further executes correction of the image data based on the color of the captured standard object and a reference color. The quality control method according to Aspect 1. (Aspect 3) The predetermined color includes primary colors or white, gray, or black of a predetermined lightness. The quality control method according to Aspect 1. (Aspect 4) For the corrected image data, creating a histogram for at least any one of hue, saturation, and lightness, and storing it in the storage device in association with the position information, which is further executed by the computer. The quality control method according to Aspect 2. (Aspect 5) Detecting that the trend has changed in the shape of the histogram and storing it in the storage device in association with the position information, which is further executed by the computer. The quality control method according to Aspect 4. (Aspect 6) Extracting the contour from the corrected image data, estimating the particle size of the material forming the embankment based on the size of the extracted contour, and storing information regarding the estimated particle size in the storage device, which is further executed by the computer. The quality control method according to Aspect 2. (Aspect 7) Obtaining the acceleration applied to the compaction machine measured by a compaction machine equipped with an acceleration sensor and storing it in the storage device, which is further executed by the computer. The quality control method according to Aspect 1. (Aspect 8) The computer further executes to transmit the information stored in the storage device to the requesting terminal in response to a request from the terminal. The quality control method according to Aspect 1. (Aspect 9) A quality control system including one or more computers that execute the quality control method according to any one of Aspects 1 to 7. (Aspect 10) A quality control program for causing one or more computers to execute the quality control method according to any one of Aspects 1 to 7.

[0007] Note that the contents described in the means for solving the problems can be combined as much as possible without departing from the problems and technical ideas of the present invention. Further, the contents of the means for solving the problems can be provided as an apparatus such as a computer or a system including a plurality of apparatuses, a method executed by a computer, or a program to be executed by a computer. The program can also be executed on a network. Further, a recording medium holding the program may be provided.

Effects of the Invention

[0008] According to the present invention, regarding civil engineering work, the traceability regarding the variation of the embankment material can be improved.

Brief Description of the Drawings

[0009]

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DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, embodiments of the present invention will be described with reference to the drawings.

[0011] <Embodiment 1> FIG. 1 is a diagram showing an example of a quality control system. The quality control system 100 accumulates information on the state of the surface of the embankment in civil engineering work. The embankment is, for example, earth and sand piled up to form the ground in the construction of a road or the like, but is not particularly limited to such earthwork. Further, the quality control system 100 may evaluate the state of the embankment.

[0012] The quality management system 100 includes, for example, a heavy machine 1 for spreading, a server 2, and terminals 3 (3A, 3B). These components are communicably connected via a network 4. The heavy machine 1 is, for example, a bulldozer equipped with a position sensor 14, an imaging device 15, and a standard body 16 for color tone. The server 2 collects and stores the image data captured by the heavy machine 1, for example, via the network 4. The server 2 may also analyze the stored data to evaluate the quality of construction. The terminal 3 is a device used by the user and may be, for example, a PC (Personal Computer), or a so-called tablet or smartphone, or other computer. Via the terminal 3, the user can read the information stored in the server 2. The network 4 includes, for example, an IP (Internet Protocol) network, and the devices connected to the network 4 can communicate based on a predetermined communication protocol. A part of the network 4 may be a telephone network (fixed telephone network or mobile communication network), an ad hoc network, an intranet, a VPN (Virtual Private Network), a LAN (Local Area Network), a wireless LAN (Wireless LAN), a WAN (Wide Area Network), or the Internet as well.

[0013] FIG. 2 is a block diagram showing an example of the configuration of the heavy machine 1. The heavy machine 1 is, for example, a bulldozer equipped with a blade (earthmoving blade) and performs spreading of earth and sand. The heavy machine 1 also includes a processor 11, a storage device 12, a communication interface (IF) 13, a position sensor 14, an imaging device 15, and a standard body 16. The processor 11 is an arithmetic processing device such as a CPU (Central Processing Unit) and performs each process according to this embodiment by executing a program. The storage device 12 is, for example, a RAM (Random Access Memory) or a ROM (Read Main memory such as hard disk drives (HDDs) and solid state drives (SSDs) Auxiliary storage devices such as eMMC (embedded Multi-Media Card), flash memory, etc. The main storage device temporarily stores the program read by the processor 11 and information transmitted and received between the processor 11 and other computers, and secures a working area for the processor 11. The auxiliary storage device stores the program executed by the processor 11 and information transmitted and received between the processor 11 and other computers. The communication IF 13 is, for example, a network card or a communication module, and communicates with other computers based on a predetermined communication protocol. The position sensor 14 is, for example, a receiver that receives a signal from a satellite positioning system (GNSS: Global Navigation Satellite System), calculates coordinates indicating the position where the position sensor 14 is located, and outputs the coordinates as position information. That is, the processor 11 can obtain the position information of the heavy equipment 1 from the position sensor 14. The imaging device 15 is, for example, a digital camera that converts light into an electric signal using an imaging element using a CCD, a CMOS, or the like, creates and outputs image data. The lens of the imaging device 15 is not particularly limited, but is preferably a wide-angle lens. In addition, the lens of the imaging device 15 is preferably equipped with a halation cut filter such as a polarizing filter. While traveling and spreading the soil, the heavy equipment 1 captures an image of the surface of the soil after spreading using the imaging device 15. The captured image data is associated with location information as metadata such as Exif. The standard body 16 is also formed of one or more regions of a predetermined color tone. It is a color chart including a color sample of red, green, and blue (RGB) arranged thereon. The standard body 16 is arranged so as to be within the imaging range of the imaging device 15, and is reflected in the image output by the imaging device. The captured color chart is used as a reference when correcting the color of the image. The standard body may be a sample including at least one of a secondary color obtained by mixing two primary colors, white, black, or gray of a predetermined brightness.

[0014] FIG. 3 is a block diagram showing an example of the server 2. The server 2 includes a processor 21, a storage device 22, and a communication interface (IF) 23. The processor 21 is an arithmetic processing device such as a CPU, and performs each process according to the present embodiment by executing a program. The storage device 22 includes, for example, a main storage device such as a RAM or a ROM, and an auxiliary storage device such as an HDD, an SSD, an eMMC, or a flash memory. The main storage device temporarily stores a program read by the processor 21 and information transmitted and received to and from other computers, and secures a working area for the processor 21. The auxiliary storage device stores a program executed by the processor 21 and information transmitted and received to and from other computers. The communication IF 23 is, for example, a network card or a communication module, and communicates with other computers based on a predetermined protocol. The server 2 can communicate with the heavy machine 1 via the communication IF 23 under the control of the processor 21.

[0015] The processor 21 of server 2 receives the image data of the embankment surface from the heavy machine 1 via the communication IF 23 and stores it in the storage device 22. The image data is accumulated in the storage device 22 in association with the position information. Also, the image data may be associated with the position in the CIM (Construction Information Modeling / Management) model. Note that the processor 21 corrects the color of the entire image based on the standard body 16 imaged in the image data. That is, when the color tone of the image data varies depending on the imaging environment such as the weather and time, the RGB values of the entire image are changed according to the difference between the reference color (i.e., the color of the standard body 16 when imaged in the reference environment such as reference sunlight conditions) and the color of the standard body 16 imaged in the image data. Further, the processor 21 calculates at least one of the hue, saturation, and lightness of the material forming the embankment based on a plurality of image data and stores it in the storage device 22. Furthermore, the processor 21 may detect changes in the particle size, water content ratio, etc. of the material forming the embankment based on at least one of the hue, saturation, and lightness and record the corresponding position in an identifiable manner. For example, when a plurality of types of materials with different particle sizes are mixed, the image has multiple maxima in the hue histogram. Also, near the boundary of materials with different water content ratios, multiple maxima occur in the lightness histogram. Also, the processor 21 of server 2 transmits information on the analysis result of the image data, such as the image data itself at a desired position, information on the color attributes (i.e., hue, saturation, or lightness) of the image data, or information on changes in the particle size, water content ratio, etc. of the material, to the terminal 3 in response to a request from the terminal 3, for example.

[0016] The terminal 3 is, for example, a PC (Personal Computer), tablet, smartphone, or other computer. In addition to having the same configuration as the server 2, the terminal 3 is equipped with a user interface (UI) such as a touch panel, keyboard, pointing device, etc. The terminal 3 receives the user's operation via the UI and outputs information to the user.

[0017] FIG. 4 is a process flow diagram showing an example of the operation of the system. The heavy machine 1 starts traveling in response to an operator's operation or autonomously, and executes the processes as shown in FIG. 4. Further, the heavy machine 1 performs spreading and images the surface of the embankment after spreading with the imaging device 15 (FIG. 4: S1). FIG. 5 is a schematic diagram for explaining the imaging range. The heavy machine 1 travels along the path indicated by the arrow, for example, to perform spreading, and images the ground surface on the side opposite to the traveling direction of the heavy machine 1 with the imaging device 15. Further, regarding the surface of the embankment 501, each of the regions 502 partitioned by a broken line, for example, is preferably recorded in the image data. The region 502 is, for example, a rectangle of a predetermined size and is arranged without gaps in a mesh pattern. Further, while the heavy machine 1 is traveling, it may intermittently capture still images or may capture so-called moving images. Regarding the surface of the embankment 501, each of the regions 502 partitioned by a broken line, for example, is preferably recorded in the image data. The region 502 is, for example, a rectangle of a predetermined size and is arranged without gaps in a mesh pattern. Further, while the heavy machine 1 is traveling, it may intermittently capture still images or may capture so-called moving images.

[0018] FIG. 6 is a diagram showing an example of the captured image. In the image data 601, the surface 602 of the embankment and the reference object 16 are imaged. Further, the image data 601 may include the periphery of the region 502 in the imaging range so that the region 603 not including the reference object 16 corresponds to the region 502 shown in FIG. 5.

[0019] After S1, the processor 11 acquires position information from the position sensor 14 and stores the image data generated in S1 in the storage device 12 in association with the position information (FIG. 4: S2). Note that the imaging device 15 may include the position sensor 14 and add it to the image data as Exif information.

[0020] After S2, the processor 11 determines whether the travel of the heavy machine 1 has ended (FIG. 4: S3). In this step, the processor 11 determines that the travel has ended, for example, when it receives an end instruction based on an operator's operation or when the engine of the heavy machine 1 is stopped. Further, the processor 11 may detect that the spreading has ended when it determines that the travel within the designed embankment range has ended based on the position information output by the position sensor 14.

[0021] If it is determined that the process has not ended (S3: NO), the processor 11 returns to the process of S1 and repeats the process. On the other hand, if it is determined that the process has ended (S3: YES), the processor 11 transmits the captured image data to the server 2 via the communication IF13 (Fig. 4: S4). In this step, it is assumed that the image data captured during the scattering operation is transmitted collectively. Note that the transmission of the image data may be repeated after S2.

[0022] Also, the processor 21 of the server 2 receives the image data via the network 4 and the communication IF23 (Fig. 5: S5). The image data is temporarily stored in the storage device 22, for example.

[0023] After S5, the processor 21 corrects the received image data (Fig. 5: S6). In this step, each value of RGB of the image is changed according to the relationship between the reference color and the color of the standard body 16 imaged in the image data. That is, as shown in Fig. 6, the standard body 16 includes regions corresponding to red (R), green (G), and blue (B), and it is relatively easy to detect the imaged region in the image data. Then, the processor 21 shifts the value of each color for the entire image so that the color of each region where the standard body 16 is imaged becomes a predetermined reference value. At this time, for each of RGB, a value obtained by subtracting the average value of the corresponding region of the imaged standard body 16 from the predetermined reference value may be added to all pixels. Also, for each of RGB, the ratio of the predetermined reference value to the average value of the corresponding region of the imaged standard body 16 may be multiplied by all pixels. In this way, by providing the standard body 16 with regions colored with each of the three primary colors of light, correction for each color can be easily performed on the image data. Note that it is not limited to the three primary colors of light, and may be a predetermined color such as a secondary color, white, black, or gray of a predetermined brightness. For example, in the case of gray, the above process can be performed by decomposing each of RGB. Also, by using the three primary colors of light, the correction process for each corresponding RGB is simplified.

[0024] After S6, the processor 21 analyzes the corrected image data (Fig. 5: S7). In this step, for at least any one of hue, saturation, and lightness, for example, a histogram is created. Fig. 7 is a diagram for explaining the analysis of the image data shown in Fig. 6. (A) of Fig. 7 shows the histogram of hue. That is, in (A) of Fig. 7, the horizontal axis represents the angle from 0 degrees to 360 degrees of the hue circle, and the vertical axis represents the relative frequency. (B) of Fig. 7 shows the histogram of saturation. That is, in (B) of Fig. 7, the horizontal axis represents the saturation value, and the vertical axis represents the relative frequency. (C) of Fig. 7 shows the histogram of lightness. That is, in (C) of Fig. 7, the horizontal axis represents the lightness value, and the vertical axis represents the relative frequency. Also, the graph is smoothed by a filter using, for example, a moving average.

[0025] In S7, the processor 21 may be configured to automatically detect that the number and position of the peaks in the histogram have changed on the surface of the embankment. Fig. 8 is a diagram for explaining the analysis of the image data in which two types of materials are imaged. When different materials are included on the surface of the embankment, for example, two peaks appear in the histogram of hue. That is, they do not gather into one group in terms of the angle of the hue circle, and multiple peaks appear. Fig. 9 is a diagram for explaining the analysis of the image data in which a portion with a high water content ratio is imaged. When a locally high water content ratio region is included on the surface of the embankment, for example, two peaks appear in the histogram of lightness. That is, multiple peaks appear in the histogram of lightness. The processor 21 may detect such a change on the surface of the embankment and store it in the storage device 22 in association with the position where the change appears. The number and position of such peaks can be specified, for example, after performing predetermined smoothing on the graph.

[0026] In S7, the particle size of the material may be determined based on the image data. FIG. 10 is a diagram for explaining the characteristics of image data obtained by imaging materials with different particle sizes. FIG. 10 includes graphs of hue, saturation, and lightness, and shows the average values and standard deviations for each of the materials with different particle sizes (intermediate soil, mountain sand, crushed sand, coarse aggregate). In particular, the intermediate soil is characterized by the values of the average and standard deviation compared to other materials. For example, the average and standard deviation may be calculated for at least any one of hue, saturation, and lightness to estimate the particle size of the material.

[0027] The particle size may be determined by estimating the individual particle diameters from the image. FIG. 11 is a diagram for explaining the estimation of the particle diameter. The processor 21 performs binarization on the image data after correction in S6 with a predetermined threshold value as shown in the second row from the top of FIG. 11. Further, from the material imaged in the binarized image data, the contour is extracted as shown in the third row from the top of FIG. 11. The contour extraction can be executed using an existing edge detection method. Also, the processor 21 integrates the number of pixels within the extracted contour and calculates an estimated value as the particle diameter when each region is hypothetically a perfect circle. In this way, a histogram as shown in the lower row of FIG. 11 can be created. The horizontal axis of the histogram represents the particle diameter, and the vertical axis represents the relative frequency. When the created histogram was compared and verified with the actual soil particle size distribution, it was found that the estimation was satisfactory. Based on the particle size distribution obtained in this way, or by combining the particle size distribution with other analysis results, the particle size of the material may be determined.

[0028] After S7, the processor 21 stores the image data and the like in the storage device 22 in association with the position information indicating the position where the image data was captured (Fig. 4: S8). In this step, the image data received in S5 or the image data corrected in S6 may be stored so that it can be confirmed in the future. Also, in this step, the information representing the analysis result created in S7 is stored. For example, a histogram is stored for at least one of hue, saturation, and brightness in association with the position information. Further, information indicating that the number or position of the peaks has changed or information indicating a change in particle size such as the average particle size may be further stored for these histograms.

[0029] After S8, the recording process of the material is terminated. Note that the processes from S6 to S8 are performed for each of the plurality of received image data.

[0030] <Effect> According to the above processing, by storing the image of the material in association with the position information, the traceability of the embankment material can be improved. In particular, by calculating at least one of hue, saturation, and brightness, it becomes possible to identify the position where there is some change in the material.

[0031] <Embodiment 2> Fig. 12 is a diagram showing an example of a quality management system according to the second embodiment. The quality management system 100 accumulates information on the state of the surface of the embankment and stores it in association with information on the compaction of the ground. Note that the same reference numerals are given to the configurations common to the first embodiment, and the description thereof is omitted.

[0032] The quality management system 100 includes, for example, a first heavy machine 1 for spreading, a server 2, terminals 3 (3A, 3B), a second heavy machine 5 for compacting, a laser scanner 6, and a measuring device 7. Further, at least some of these components are communicably connected via a network 4. The first heavy machine 1 is the same as the heavy machine 1 in the above-described first embodiment. The server 2 collects, for example, image data captured by the first heavy machine 1, data such as acceleration measured by the second heavy machine 5, three-dimensional coordinates of the embankment surface measured by the laser scanner 6, information measured by the measuring device 7, etc. via the network 4, and accumulates them in association with position information. Further, the server 2 may analyze the accumulated data to evaluate the quality of the construction. The terminals 3 and the network 4 are the same as those in the first embodiment. Note that the measuring device 7 is a self-propelled device that outputs a measured value measured using a sensor or some data calculated using the measured value. The measuring device 7 outputs, for example, the water content ratio of the embankment material, wet density, dry density, etc.

[0033] The second heavy machine 5 is a compaction machine such as a vibrating roller for compacting the embankment, for example. The second heavy machine 5 includes a position sensor 51, an acceleration sensor 52, and a computer. The position sensor 51, similar to the position sensor 14 of the first heavy machine 1, receives, for example, a GNSS signal, calculates coordinates indicating the position where the position sensor 51 is located, and outputs it as position information. The acceleration sensor 52 is connected to the compaction wheel of the second heavy machine 5 and detects, for example, the vertical vibration acceleration of the second heavy machine 5. Further, the second heavy machine 5 may calculate an acceleration response value, a ground deformation coefficient (ground stiffness), etc. using a computer, and store these values, the number of compaction passes, etc. in association with the position information. Also, the information stored in the computer is transmitted to the server 2 via a communication device provided in the computer. Based on the received information, the server 2 can evaluate the compaction quality in association with the position of the embankment. The server 2 can transmit, for example, the ground stiffness as a heat map to the terminal 3.

[0034] The laser scanner 6 irradiates laser light and receives the reflected light to measure the three-dimensional coordinates of the ground surface. Further, when the second compactor 5 performs a plurality of rotational pressures on the same location, the laser scanner 6 may measure the three-dimensional coordinates of the ground surface before and after the compaction. The values measured by the laser scanner 6 are transmitted to the server 2 via the second compactor 5 or the terminal 3, or via a communication device provided in the laser scanner 6. The server 2 can calculate the amount of ground settlement due to compaction.

[0035] Further, the measuring device 7 is obtained by providing a position sensor 71, a moisture meter 72, a wet density meter 73, and a control device 74 to an existing self-propelled vehicle. The measuring device 7 drives the wheels to self-propel based on the position information output by the position sensor 71. For example, the measuring device 7 travels following the second compactor 5 on the same travel route as the second compactor 5. Further, the moisture meter 72 measures the water content ratio w (%). Further, the wet density meter 73 measures the wet density γt (g / cm 3 ). Further, the control device 74 calculates the dry density γd using the water content ratio w and the wet density γt, for example, based on the following formula. γd = γt / (1 + w / 100) Further, the control device 74 transmits the information held by the measuring device 7 to the server 2 via the second compactor 5 or the terminal 3, or via a communication device provided in the measuring device 7. The server 2 stores information such as the water content ratio in association with the position information.

[0036] Further, in the present embodiment, for example, the user can operate the terminal 3 to confirm the information regarding the filling material up to the evaluation of compaction in association with the position information. In this way, not only can the quality be evaluated after compaction, but it becomes possible to trace back and verify the information on the construction site regarding the filling material in the future.

[0037] <Others> The above-described embodiments are illustrative, and the present invention is not limited to the above-described configurations. For example, the imaging device 15 and the reference object 16 may be mounted on a drone such as a multicopter instead of the heavy machine 1. The drone follows the heavy machine 1 in flight, for example, and the imaging device 15 images the surface of the earth and sand after spreading and the reference object 16.

[0038] The subject of the present invention also includes a computer program that executes the above-described processing and a computer-readable recording medium on which the program is recorded. By causing a computer to execute the program recorded on the recording medium, the above-described processing becomes possible.

[0039] Note that a computer-readable recording medium refers to a recording medium that stores information such as data and programs by an electrical, magnetic, optical, mechanical, or chemical action and can be read by a computer. Examples of removable recording media from a computer include flexible disks, magneto-optical disks, optical disks, magnetic tapes, memory cards, and the like. Examples of recording media fixed to a computer include HDDs, SSDs, ROMs, and the like.

Explanation of Reference Numerals

[0040] 100: Quality Management System 1: Heavy Machine (First Heavy Machine) 14: Position Sensor 15: Imaging Device 16: Reference Object 2: Server 3: Terminal 4: Network 5: Second Heavy Machine 51: Position Sensor 52: Acceleration Sensor 6: Laser Scanner 7: Measuring Device

Claims

1. Obtaining image data in which the surface of the embankment after spreading is imaged, and position information indicating the position where the image data is imaged, Associating the image data with the position information and storing the associated data in a storage device, A quality control method executed by a computer.

2. The image data is obtained by further imaging a standard object colored with a predetermined color, The computer further executes correction of the image data based on the color of the imaged standard object and a reference color. The quality control method according to claim 1.

3. The predetermined color includes a primary color, white, black, or gray of a predetermined lightness. The quality control method according to claim 1.

4. The computer further creates a histogram for at least one of hue, saturation, and lightness with respect to the corrected image data, associates the histogram with the position information, and stores the associated data in the storage device. The quality control method according to claim 2.

5. The computer further detects that the shape of the histogram has changed, associates the detected change with the position information, and stores the associated data in the storage device. The quality control method according to claim 4.

6. The computer further extracts a contour from the corrected image data, estimates the particle size of the material forming the embankment based on the size of the extracted contour, and stores information regarding the estimated particle size in the storage device. The quality control method according to claim 2.

7. The computer further obtains the acceleration applied to a compaction machine measured by the compaction machine equipped with an acceleration sensor and stores the acceleration in the storage device. The quality control method according to claim 1.

8. The computer further transmits the information stored in the storage device to the requesting terminal in response to a request from the terminal. The quality control method according to claim 1.

9. A quality control system including one or more computers that execute the quality control method according to any one of claims 1 to 7.

10. A quality control program for causing one or more computers to execute the quality control method according to any one of claims 1 to 7.

Images