Tunnel face warning system

Abstract

To provide a system provided with an alarm function capable of detecting instantaneous deformation on a tunnel excavation face in real time and with high accuracy.SOLUTION: A system includes camera means 2, a three-dimensional distance measuring sensor 3, an analysis unit 4, alarm means 5, and display means 6. The analysis unit 4 includes a learning model 41 and determination means 42. The camera means 2 captures images of a working face in real time. The three-dimensional distance measuring sensor 3 measures a three-dimensional position of the working face in real time so as to grasp an extrusion amount of a mirror face as a plane in real time. The analysis unit 4 calculates the extrusion amount from a previous position and a present position on a time series of the three-dimensional position of the working face inputted from the three-dimensional distance measuring sensor 3 at a timing when a change is detected from a difference between a previous image and a present image on a time series of the images of the working face inputted from the camera means 2, outputs a collapse timing and a collapse amount to determine whether or not the collapse amount exceeds a predetermined threshold, and outputs an alarm signal when it is determined that the collapse amount exceeds the predetermined threshold.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a technology for detecting risks such as collapse by grasping instantaneous deformation of the tunnel excavation surface (face) in real time. [Background technology]

[0002] In tunnel construction, the tunnel excavation face (face) may collapse during excavation work, which could lead to a serious disaster. It is known that before a collapse, the face may suddenly displace by several millimeters to several centimeters. Therefore, a crack detection device is known that detects the risk of face collapse during excavation work in real time and prevents disasters (see Patent Document 1). The crack detection device in Patent Document 1 focuses on the fact that when "mirror-sprayed concrete" is applied to a tunnel face, cracks of a few millimeters in width appear on the sprayed surface as a sign of impending collapse, and collapse occurs along the cracks a few minutes later, and is able to detect these cracks. Crack detection is performed by inputting the tunnel face image to be judged into a trained model that has learned the relationship between the tunnel face image and pixels corresponding to cracks contained in the tunnel face image. However, the crack detection device of Patent Document 1 has a problem in that it cannot obtain sufficient accuracy because it detects cracks using only the tunnel face image captured by the camera.

[0003] In recent years, LiDAR (Light Detection and Ranging) has been used in autonomous driving systems for automobiles and other vehicles. LiDAR measures distance by emitting laser light from a sensor and measuring the time it takes for the light to hit an object and bounce back. Therefore, a technology is known that uses a high-speed 3D laser scanner to detect slight sudden displacements, which are a sign of collapse, in real time at mountain tunnel excavation construction sites ( Non-patent document 1 (See Non-patent document 1This technology uses LiDAR as a high-speed 3D laser scanner, but because it only uses LiDAR to detect risks such as collapses, there is a problem that it does not achieve sufficient accuracy. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Publication No. 2021-184163 [Non-patent literature]

[0005] [Non-Patent Document 1] Takahiro Sanpei and Tomohiro Mizoguchi, "Basic Study on Real-time Detection of Sudden Displacement of Tunnel Face Using 3D Scanner", Proceedings of the 2017 Autumn Meeting of the Japan Society for Precision Engineering, pp.527-528 Summary of the Invention [Problem to be solved by the invention]

[0006] In view of the above circumstances, the present invention aims to provide a system that can detect instantaneous deformation of the tunnel excavation surface (face) in real time with high accuracy and that is equipped with an alarm function. [Means for solving the problem]

[0007] In order to solve the above problems, the tunnel face warning system of the present invention comprises a camera means for taking images of the tunnel face in real time, a three-dimensional ranging sensor for measuring the three-dimensional position of the tunnel face in real time, an analysis unit which calculates the amount of push-out from the previous and current positions in the time series of the three-dimensional position of the tunnel face input from the three-dimensional ranging sensor at the timing when a change is detected from the difference between the previous and current images in the time series of the tunnel face images input from the camera means, outputs the collapse timing and collapse amount, determines whether the collapse amount exceeds a predetermined threshold, and outputs an alarm signal if it is determined that the predetermined threshold has been exceeded, and an alarm means which inputs the alarm signal from the analysis unit and issues an alarm.

[0008] By using a camera means and a 3D ranging sensor in combination, and capturing changes (signs of collapse) in the tunnel excavation surface (face) through analysis of images captured by the camera means, and calculating the specific amount of extrusion of the face using the 3D ranging sensor, instantaneous changes in the tunnel excavation surface (face) can be detected in real time with high accuracy. In addition, the alarm means can properly communicate the condition of the face to workers at the construction site, preventing the occurrence of serious disasters. Rotating lights and buzzers are preferably used as alarm means. Here, the previous image in the time series of face images input from the camera means does not mean the image one frame before in the group of images taken in real time by the camera means, but an image in a steady state with no change, such as the image at the start of measurement. Also, the collapse timing refers to the timing at which a change is detected from the difference between the previous image and the current image in the time series of face images, and specifically, an artificial intelligence model (AI model) analyzes the images and makes the judgment. Furthermore, the previous position on the time series of the three-dimensional position of the working face input from the three-dimensional distance sensor is the position of the working face when there is no change and it is steady, and the amount of push-out calculated from the previous position and the current position is the amount of movement (distance) of each point cloud. The amount of push-out is calculated from the amount of movement of the point cloud, and the amount of collapse (volume) is calculated from the area of ​​the moved point cloud and the amount of push-out. It is a sign of collapse, regardless of whether a collapse has actually occurred or not.

[0009] In the tunnel face warning system of the present invention, the analysis unit is preferably equipped with a learning model that identifies the timing of collapse in images of the face, and detects the timing of collapse from the difference between the previous image and the current image in the time series of images of the face. The analysis unit is equipped with an artificial intelligence model (AI model), i.e., a learning model, that predicts the timing of a collapse from changes in images of the face, making it possible to predict the timing of a collapse.

[0010] In the tunnel face warning system of the present invention, it is preferable that the analysis unit calculates the collapsed area of ​​the face based on the amount of push-out calculated from the previous and current positions on the time series of the three-dimensional position of the face input from the three-dimensional distance measuring sensor, determines whether or not it exceeds a predetermined area, and outputs a second warning signal. With this configuration, it is also possible to output a different warning if the collapsed area is large.

[0011] It is preferable that the tunnel face warning system of the present invention further comprises a display means for inputting and displaying a group of time-series images of the tunnel face including the timing of collapse and the amount of collapse from the analysis unit. By providing a display means, a user can visually check the condition of the face and the degree of danger. The display means may further display data input from the three-dimensional distance measuring sensor and data related to warnings. [Effects of the Invention]

[0012] The tunnel face warning system of the present invention has the advantage of being able to detect instantaneous deformation of the tunnel excavation surface (face) in real time with high accuracy, and also being able to notify those in the vicinity of the risk of collapse through the warning function. [Brief explanation of the drawings]

[0013] [Figure 1] Functional block diagram of the tunnel face warning system of the first embodiment [Figure 2] Configuration image of the tunnel face warning system in Example 1 [Figure 3] Schematic flow diagram of the tunnel face warning system of Example 1 [Figure 4] Display image [Figure 5] Functional block diagram of the tunnel face warning system of the second embodiment DETAILED DESCRIPTION OF THE INVENTION

[0014] An example of an embodiment of the present invention will be described in detail below with reference to the drawings. Note that the scope of the present invention is not limited to the following examples and illustrated examples, and many modifications and variations are possible. [Example]

[0015] Fig. 1 shows a configuration image diagram of a tunnel face warning system of Example 1. As shown in Fig. 1, the tunnel face warning system 1 includes a camera means 2, a three-dimensional distance measuring sensor 3, an analysis unit 4, a warning means 5, and a display means 6, and the analysis unit 4 includes a learning model 41 and a determination means 42. The camera means 2 captures images of the face in real time, and a wide range of known cameras can be applied. The three-dimensional ranging sensor 3 measures the three-dimensional position of the face in real time, and can grasp the amount of extrusion of the mirror surface in real time. A known LiDAR scanner is preferably used as the three-dimensional ranging sensor 3. The analysis unit 4 calculates the amount of extrusion from the previous and current positions in the time series of the three-dimensional position of the face input from the three-dimensional ranging sensor 3 at the timing when a change is detected from the difference between the previous and current images in the time series of face images input from the camera means 2, outputs the collapse timing and collapse amount, determines whether the collapse amount exceeds a predetermined threshold, and outputs an alarm signal if it is determined that the predetermined threshold has been exceeded.

[0016] The learning model 41 identifies the timing of collapse in the image of the tunnel face. The learning model 41 is used to detect the timing of collapse based on the difference between the previous and current images of the tunnel face in a time series. In other words, the learning model 41 is not used to compare the three-dimensional position of the tunnel face measured by the 3D ranging sensor 3, but is used only to compare the images of the tunnel face captured by the camera unit 2. The determination unit 42 determines whether the amount of collapse exceeds a predetermined threshold. The alarm unit 5 receives an alarm signal from the analysis unit 4 and issues an alarm, and is composed of, for example, a rotating light or buzzer. The display unit 6 receives and displays a group of time-series images of the tunnel face, including the collapse timing, and the amount of collapse from the analysis unit 4. A known display is preferably used. The display unit 6 can visualize the amount of collapse, the amount of extrusion, contours, and highlight weak areas of the tunnel face. A touch panel may also be used for not only displaying the data but also starting, stopping, changing settings, and other operations.

[0017] The camera means 2 compares the most recent image of the camera footage from start to finish, and the analysis unit 4 uses the learning model 41 to make judgments based on these, detecting the timing of collapse from the difference between the previous and current images in the time series of face images. The camera means 2 can also judge weathering, extract weak areas, and determine the presence or absence of spring water or cracks. Meanwhile, the 3D ranging sensor 3 measures the 3D position of the face in real time and inputs the data to the analysis unit 4. When the analysis unit 4 detects the timing of a collapse using the learning model 41, it calculates the amount of push-out from the previous and current positions on the time series of the 3D position of the face input from the 3D ranging sensor 3, and calculates the amount of collapse from the amount of push-out. The collapse amount is determined to exceed a predetermined threshold using the determination means 42, and if it is determined to exceed the predetermined threshold, an alarm signal is output, an alarm is issued using the alarm means 5, or a message is displayed on the display means 6. By using the camera means 2 and the 3D ranging sensor 3 in combination, it is possible to accurately determine the risk of collapse and issue an alarm.

[0018] Fig. 2 shows a configuration image diagram of the tunnel face warning system of Example 1. As shown in Fig. 2, the tunnel face warning system 1 is installed and used inside a tunnel excavation work site 7. The tunnel face warning system 1 is composed of a camera 20 and a device main body 10, and the camera 20 and the device main body 10 are connected by a communication cable 11. Alternatively, the camera 20 and the device main body 10 may be configured to communicate wirelessly. The camera 20 is equipped with a known camera as the camera means 2 and a LiDAR scanner as the three-dimensional distance measuring sensor 3. The device main body 10 is provided with a rotating light 50a and a buzzer (not shown) as the alarm means 5, and a buzzer sound is emitted from a speaker 50b. The device main body 10 is also provided with a display 60 as the display means 6.

[0019] FIG. 4 shows an image of the display. As shown in FIG. 4, an image 61 captured in real time of a tunnel excavation site 7 is displayed on the display 60. Here, a face 71 is the target of monitoring, and a monitoring range 72 is displayed. The display 60 may display not only images captured in real time but also images in a steady state, or may display three-dimensional position data of a point cloud in a steady state or three-dimensional position data of a point cloud measured in real time. The display 60 is a touch panel, and the monitoring range 72 can be changed by operating the cross key 62a, or the image can be enlarged or reduced by touching the enlarge button 62b or the reduce button 62c.

[0020] When using the tunnel face warning system of the first embodiment, first, as a prerequisite, the face positions and installation orientations of the camera means 2 and the three-dimensional distance measuring sensor 3 are registered. FIG. 3 shows a schematic flow diagram of the tunnel face warning system of the first embodiment. As shown in FIG. 3, first, steady-state image data of the tunnel face 71 is input from the camera means 2 to the analysis unit 4 (step S01). Next, three-dimensional position data of the point cloud of the tunnel face 71 in steady state is input from the three-dimensional ranging sensor 3 to the analysis unit 4 (step S02). The camera means 2 captures images of the tunnel face in real time, and inputs the image data of the tunnel face 71 from the camera means 2 to the analysis unit 4 (step S03). The input images are displayed on the display means 6. When the analysis unit 4 detects a change from the difference between the previous image and the current image in the time series using the learning model 41, that is, when a change is detected from the difference between the steady-state image data and the image data captured and input in real time (step S04), the three-dimensional position data of the point cloud of the tunnel face 71 is input to the analysis unit 4 (step S05). On the other hand, if no change is detected from the difference between the previous image and the current image in the time series, that is, if no change is detected from the difference between the image data in a steady state and the image data captured and input in real time (step S04), image data of the face 71 is input again from the camera means 2 (step S03).

[0021] The analysis unit 4 calculates the amount of push-out from the previous and current positions on the time series of the three-dimensional position of the working face 71 input from the three-dimensional distance measurement sensor 3, i.e., the three-dimensional position data of the point cloud in a steady state, and the three-dimensional position data of the point cloud measured and input in real time (step S06).The analysis unit 4 calculates the amount of collapse from the amount of push-out, and outputs the collapse timing and amount of collapse to the determination means 42 (step S07). The determination means 42 determines whether the amount of collapse has exceeded a predetermined threshold and determines the risk of collapse (step S08). If the result of the determination is that the predetermined threshold has been exceeded, i.e., if it is determined that the alarm level has been exceeded (step S09), an alarm signal is output (step S10). On the other hand, if it is not determined that the alarm level has been exceeded (step S09), image data of the working face 71 is again input from the camera means 2 (step S03).

[0022] Based on the output alarm signal, the alarm means 5 issues an alarm using a rotating light 50a or a buzzer. The display means 6 displays the output data on the timing and amount of collapse on a screen, and also displays the result of the determination made by the determination means 42. In this way, by using the camera means 2 and the three-dimensional distance measuring sensor 3 in combination, instantaneous deformation of the face can be detected in real time with high accuracy.

[0023] In addition to the above-described flow, the collapsed area of ​​the face may be calculated from the amount of extrusion and an alarm signal may be output. Specifically, the collapsed area of ​​the face 71 is further calculated based on the amount of extrusion calculated in step S06, and in step S07, the collapsed area is output in addition to the collapse timing and amount. Then, in step S08, it is determined whether the collapsed area exceeds a predetermined area, and if it is determined that the predetermined area has been exceeded, that is, if it is determined that the alarm level has been exceeded (step S09), a second alarm signal is output in step S10. [Example]

[0024] Fig. 5 shows a configuration image diagram of a tunnel face warning system of Example 2. As shown in Fig. 5, a tunnel face warning system 1a includes a camera means 2, a three-dimensional distance measuring sensor 3, an analysis unit 4, and a warning means 5, and the analysis unit 4 includes a learning model 41 and a determination means 42. Unlike the tunnel face warning system 1 of the first embodiment, the tunnel face warning system 1a is configured not to be provided with a display means 6. By adopting such a configuration, the system can be constructed at lower cost and can be used as a simple warning system. [Industrial Applicability]

[0025] The present invention is useful for a system that grasps instantaneous changes in the excavated surface of a tunnel in real time and detects risks such as collapse. [Explanation of symbols]

[0026] 1,1a Tunnel face warning system 2 Camera Means 3 3D distance measurement sensor 4 Analysis Unit 5 Alarm means 6 Display Means 7 Tunnel excavation construction site 10. Device body 11 Communication cables 20 Camera 41 Learning Model 42 Judgment means 50a Rotating Light 50b speaker 60 displays 61 images 62a D-pad 62b Enlarge button 62c Zoom out button 71 Face 72 Monitoring scope

Claims

1. a camera means for capturing images of the working face in real time; a three-dimensional distance measuring sensor that measures the three-dimensional position of the working face in real time; an analysis unit that is equipped with a learning model that identifies the timing of collapse of the image of the face, and that, at the timing that the collapse timing is detected from the difference between the previous image and the current image in the time series of the image of the face input from the camera means, calculates the amount of push-out from the amount of movement of the point cloud between the previous position and the current position in the time series of the three-dimensional position of the face input from the three-dimensional distance measuring sensor, calculates the amount of collapse from the area of ​​the moved point cloud and the amount of push-out, outputs the collapse timing and the amount of collapse, determines whether the amount of collapse has exceeded a predetermined threshold, and outputs an alarm signal if it is determined that the amount of collapse has exceeded the predetermined threshold; an alarm means for receiving the alarm signal from the analysis unit and issuing an alarm; A tunnel face warning system comprising:

2. The tunnel face warning system described in claim 1, characterized in that the analysis unit calculates the collapse area of ​​the face based on the area of ​​the point cloud calculated from the movement of the point cloud between the previous position and the current position on the time series of the three-dimensional position of the face input from the three-dimensional ranging sensor, determines whether the collapse area exceeds a predetermined area, and outputs a second warning signal.

3. A tunnel face warning system as described in claim 1 or 2, further comprising a display means for inputting and displaying a group of images of the face in time series including the timing of collapse and the amount of collapse from the analysis unit.

Images