Crack detection method applied to bridge cable bent tower and related equipment

Abstract

The embodiment of the invention belongs to the technical field of bridge health condition detection, and relates to a crack detection method applied to a bridge cable bent tower and related equipment. According to the method, the mooring type flying-climbing robot is controlled to move on the surface of the cable bent tower at a constant distance, the image of the surface area of the cable bent tower is collected through the camera shooting equipment of the mooring type flying-climbing robot, in addition, crack detection operation is conducted on the area image, and crack data of the crack area are further obtained according to the crack detection result; the high-precision flexible traversal measurement of the bridge cable bent tower crack is realized, the accuracy of effective data detection is improved, and the authenticity and reliability of the detection data are further ensured.

Description

Technical Field

[0001] The present application relates to the technical field of bridge health status detection using artificial intelligence, and in particular to a crack detection method and related equipment applied to bridge pylons. Background Art

[0002] With the increasing level of infrastructure construction in my country, the number and span of bridges built are growing, and the structure is becoming more and more complex. Traditional detection methods are seriously affected by subjective factors, and the detection results are often recorded in paper forms and illustrations, which is not conducive to managers to judge and control the overall damage of bridges. In addition, there are few practitioners in bridge detection in my country, and the training cycle is long. It may be difficult to cope with the arduous task of bridge inspection. Since the 21st century, with the rapid development of sensor technology, drone flight control technology, and image processing technology, it has become possible to use drones instead of traditional manual methods for high-altitude operations. More and more researchers in various countries are turning their attention to using drones to detect bridges. In the current situation where the demand for bridge maintenance and inspection tasks is increasing and the inspection environment is becoming more and more dangerous, the existing non-destructive testing technologies have different limitations. The crack detection technology based on drone images can cover the entire bridge pylon in the detection area, and will not cause missed detection due to the spatial randomness of the cracks. There is no requirement for the geometric shape and development stage of the cracks. At the same time, drone detection equipment can be reused and can be widely used in various bridges.

[0003] However, the applicant found that in the traditional bridge inspection drone solution, the quadcopter is in a hovering state when shooting, and then relies on an auxiliary laser rangefinder and corresponding algorithms for correction. The flight altitude of the drone under the hovering condition is not stable, which makes it difficult to meet the strict requirements of accuracy required for measuring cracks in bridge towers, seriously affecting the authenticity and reliability of the detection data. It can be seen that the traditional bridge inspection method has the problem of low data accuracy. Summary of the invention

[0004] The purpose of the embodiments of the present application is to propose a crack detection method and related equipment applied to bridge pylons to solve the problem of low data accuracy in traditional bridge inspection methods.

[0005] In order to solve the above technical problems, the present application provides a crack detection method applied to bridge pylons, which adopts the following technical solution:

[0006] Receiving a crack detection request carrying N crack detection areas sent by a user terminal, where N is a positive integer greater than or equal to 1;

[0007] Starting the tethered flying and climbing robot, and controlling the tethered flying and climbing robot to move to a first detection area of ​​the N crack detection areas;

[0008] Controlling a camera device mounted on the tethered flying and climbing robot to collect image data of the first detection area to obtain a first area image;

[0009] Performing a crack detection operation on the first region image to obtain a crack detection result;

[0010] If the crack detection result is that cracks exist, a depth acquisition operation is performed on the cracks to obtain first crack data corresponding to the first detection area, and the stay-type flying and climbing robot is controlled to move to the next detection area of ​​the N crack detection areas to perform crack detection operations;

[0011] If the crack detection result is that there is no crack, controlling the flying and climbing robot to move to the next detection area of ​​the N crack detection areas to perform a crack detection operation;

[0012] After the crack detection operation on all the crack detection areas is completed, the crack detection results of the bridge pylons are obtained.

[0013] Furthermore, the first crack data includes crack area data and crack depth data, and the step of performing a depth acquisition operation on the crack to obtain the first crack data corresponding to the first detection area specifically includes the following steps:

[0014] Performing an area calculation operation on the crack position of the first regional image to obtain the crack area data;

[0015] The crack depth data is obtained by scanning and detecting the crack position in the first detection area using ultrasonic equipment.

[0016] Furthermore, the step of scanning the crack positions in the first detection area to obtain the crack depth data specifically includes the following steps:

[0017] The crack position is measured using an improved ultrasonic level measurement method to obtain crack depth data.

[0018] Furthermore, the step of performing an area calculation operation on the crack position of the first regional image to obtain the crack area data specifically includes the following steps:

[0019] Performing crack image grayscale processing on the first region image to obtain a grayscale region image;

[0020] Perform crack image binarization processing on the grayscale region image to obtain a binary region image;

[0021] Performing expansion and corrosion operation processing on the binary region image to obtain an eroded and expanded region image;

[0022] Performing edge detection on the corrosion expansion region image according to the Canny edge detection method to obtain pixel values ​​of the crack edge portion;

[0023] The crack area data of the first region image is calculated according to the pixel value.

[0024] Furthermore, the step of calculating the crack area data of the first regional image according to the pixel value specifically includes the following steps:

[0025] Performing a correction operation on the pixel value according to a preset size calibration;

[0026] The crack area data of the first region image is calculated according to the corrected pixel value.

[0027] In order to solve the above technical problems, the embodiment of the present application further provides a crack detection device applied to a bridge pylon, which adopts the following technical solution:

[0028] A request receiving module, configured to receive a crack detection request carrying N crack detection areas sent by a user terminal, where N is a positive integer greater than or equal to 1;

[0029] A robot control module, used for starting the tethered flying and climbing robot, and controlling the tethered flying and climbing robot to move to a first detection area of ​​the N crack detection areas;

[0030] An image acquisition module, used for controlling a camera device mounted on the tethered flying and climbing robot to acquire image data of the first detection area to obtain an image of the first area;

[0031] A crack detection module, used to perform a crack detection operation on the first region image to obtain a crack detection result;

[0032] A first result module is used for, if the crack detection result is that cracks exist, performing a depth acquisition operation on the cracks to obtain first crack data corresponding to the first detection area, and controlling the stay-type flying and climbing robot to move to the next detection area of ​​the N crack detection areas to perform a crack detection operation;

[0033] A second result module is used for controlling the flying and climbing robot to move to the next detection area of ​​the N crack detection areas to perform a crack detection operation if the crack detection result is that there is no crack;

[0034] The result confirmation module is used to obtain the crack detection result of the bridge pylon after completing the crack detection operation on all the crack detection areas.

[0035] Further, the first result module includes:

[0036] An area calculation submodule, used for performing an area calculation operation on the crack position of the first regional image to obtain the crack area data;

[0037] The detection submodule is used to perform a scanning detection operation on the crack position of the first detection area according to the ultrasonic equipment to obtain the crack depth data.

[0038] Furthermore, it is characterized in that the scanning submodule includes:

[0039] The distance measuring unit is used to measure the distance of the crack position according to the improved ultrasonic level measurement method to obtain the crack depth data.

[0040] In order to solve the above technical problems, the embodiment of the present application further provides a computer device, which adopts the following technical solution:

[0041] The method comprises a memory and a processor, wherein the memory stores computer-readable instructions, and the processor implements the steps of the crack detection method applied to bridge pylons as described above when executing the computer-readable instructions.

[0042] In order to solve the above technical problems, the embodiment of the present application further provides a computer-readable storage medium, which adopts the following technical solution:

[0043] The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the steps of the crack detection method applied to the bridge pylon as described above are implemented.

[0044] The present application provides a crack detection method applied to bridge pylons, comprising: receiving a crack detection request carrying N crack detection areas sent by a user terminal, wherein N is a positive integer greater than or equal to 1; starting the tethered flying and climbing robot, and controlling the tethered flying and climbing robot to move to a first detection area of ​​the N crack detection areas; controlling a camera device mounted on the tethered flying and climbing robot to collect image data of the first detection area to obtain a first area image; performing a crack detection operation on the first area image to obtain a crack detection result; if the crack detection result is that a crack exists, performing a depth acquisition operation on the crack to obtain first crack data corresponding to the first detection area, and controlling the tethered flying and climbing robot to move to the next detection area of ​​the N crack detection areas to perform a crack detection operation; if the crack detection result is that a crack does not exist, controlling the tethered flying and climbing robot to move to the next detection area of ​​the N crack detection areas to perform a crack detection operation; after completing the crack detection operation on all the crack detection areas, obtaining the crack detection result of the bridge pylon. Compared with the prior art, the present application controls a tethered flying-climbing robot to move at a constant distance on the surface of the tower, and collects images of the surface area of ​​the tower through the camera equipment of the tethered flying-climbing robot. In addition, crack detection operation is performed on the regional image, and crack data of the crack area is further obtained based on the crack detection results, so as to achieve high-precision and flexible traversal measurement of cracks in bridge towers and the accuracy of effective data detection, thereby ensuring the authenticity and reliability of the detection data. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the scheme in the present application, a brief introduction is given below to the drawings required for use in the description of the embodiments of the present application. Obviously, the drawings described below are some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0046] Figure 1 is an exemplary system architecture diagram to which the present application may be applied;

[0047] Figure 2 is a flow chart of the implementation of the crack detection method applied to bridge pylons provided in the first embodiment of the present application;

[0048] Figure 3 It is a structural schematic diagram of a specific implementation method of the mooring cable structure provided in Example 1 of the present application;

[0049] Figure 4 It is a structural schematic diagram of a specific implementation of the crawling structure Mecanum wheel provided in Example 1 of the present application;

[0050] Figure 5 It is a top view of the tethered flying and climbing robot platform provided in Example 1 of the present application;

[0051] Figure 6 It is a front view of the tethered flying and climbing robot platform provided in the first embodiment of the present application;

[0052] Figure 7 This is a force analysis diagram of the tethered flying and climbing robot provided in Example 1 of the present application;

[0053] Figure 8 is a schematic diagram of an embodiment of inclined crack depth detection provided in Embodiment 1 of the present application;

[0054] Fig. 9 is a structural schematic diagram of a crack detection device applied to a bridge pylon provided in Example 2 of the present application;

[0055] Fig.10 It is a structural diagram of an embodiment of a computer device according to the present application. DETAILED DESCRIPTION

[0056] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by technicians in the technical field of the present application; the terms used in the specification of the application herein are only for the purpose of describing specific embodiments and are not intended to limit the present application; the terms "including" and "having" and any variations thereof in the specification and claims of the present application and the above-mentioned drawings are intended to cover non-exclusive inclusions. The terms "first", "second", etc. in the specification and claims of the present application or the above-mentioned drawings are used to distinguish different objects, not to describe a specific order.

[0057] Reference to "embodiments" herein means that a particular feature, structure, or characteristic described in conjunction with the embodiments may be included in at least one embodiment of the present application. The appearance of the phrase in various locations in the specification does not necessarily refer to the same embodiment, nor is it an independent or alternative embodiment that is mutually exclusive with other embodiments. It is explicitly and implicitly understood by those skilled in the art that the embodiments described herein may be combined with other embodiments.

[0058] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the accompanying drawings.

[0059] like Figure 1As shown, the system architecture 100 may include terminal devices 101, 102, 103, a network 104 and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links or optical fiber cables, etc.

[0060] Users can use terminal devices 101, 102, 103 to interact with server 105 through network 104 to receive or send messages, etc. Various communication client applications can be installed on terminal devices 101, 102, 103, such as web browser applications, shopping applications, search applications, instant messaging tools, email clients, social platform software, etc.

[0061] Terminal devices 101, 102, 103 can be various electronic devices with display screens and supporting web browsing, including but not limited to smart phones, tablet computers, e-book readers, MP3 players (Moving Picture Experts Group Audio Layer III), MP4 (Moving Picture Experts Group Audio Layer IV) players, laptop computers, desktop computers, etc.

[0062] The server 105 may be a server that provides various services, such as a background server that provides support for web pages displayed on the terminal devices 101 , 102 , and 103 .

[0063] It should be noted that the crack detection method applied to the bridge pylon provided in the embodiment of the present application is generally executed by a server / terminal device, and accordingly, the crack detection device applied to the bridge pylon is generally arranged in the server / terminal device.

[0064] It should be understood that Figure 1 The number of terminal devices, networks and servers in the embodiment is only for illustration. Any number of terminal devices, networks and servers may be provided according to the implementation requirements.

[0065] Continue to refer Figure 2 , shows a flow chart of an embodiment of a crack detection method applied to a bridge pylon according to the present application. The crack detection method applied to a bridge pylon comprises: step S201, step S202, step S203, step S204, step S205, step S206 and step S207.

[0066] In step S201, a crack detection request carrying N crack detection areas sent by a user terminal is received, where N is a positive integer greater than or equal to 1.

[0067] In the embodiments of the present application, the user terminal refers to a terminal device used to execute the image processing method for preventing document abuse provided by the present application. The user terminal can be a mobile terminal such as a mobile phone, a smart phone, a laptop computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet computer), a PMP (portable multimedia player), a navigation device, etc., as well as a fixed terminal such as a digital TV, a desktop computer, etc. It should be understood that the examples of user terminals here are only for the convenience of understanding and are not used to limit the present application.

[0068] In step S202, the tethered flying robot is started and controlled to move to the first detection area of ​​the N crack detection areas.

[0069] In the embodiment of the present application, the tethered flying and climbing robot is mainly composed of a tethered cable structure (such as Figure 3 Shown) and crawling structure McNum wheel (e.g. Figure 4 As shown), the tethered flying and climbing robot can be a four-rotor drone, such as Figure 5 as well as Figure 6 As shown in the figure, the forces acting on the fuselage mainly include its own weight, the weight of the mooring ropes, the lift provided by the rotor, and the wind resistance. Figure 7 As shown, where:

[0070] G=(M u +M r )

[0071]

[0072] f=0.5ρv 2 A

[0073] Where G is the gravity affected by the drone, M u , M r are the weight of the UAV fuselage and the tethered cable respectively, F is the lift provided by the rotor, f is the wind resistance, ρ is the air density, v is the wind speed, and A is the frontal area perpendicular to the rotor.

[0074] In the embodiment of the present application, in the general mode, the tethered flying and climbing robot has the same functions as a normal ordinary drone, and can realize functions such as hovering, taking pictures, and cruising; when used for surface crack detection of bridge towers, the flight module controls the drone to fly to the tower until the four Mecanum wheels of the crawling module are in contact with the tower surface, at which time the crawling device motor starts, operates the Mecanum wheels to turn, and completes the image acquisition work. When crawling, the flight module motor provides pressure to the system to ensure that the device can move close to the tower surface, and in addition, it can also assist the crawling module to move.

[0075] In step S203, the camera device mounted on the tethered flying-climbing robot is controlled to collect image data of the first detection area to obtain an image of the first area.

[0076] In an embodiment of the present application, the tethered flying and climbing robot is adjusted according to actual conditions. After the system is powered on, the FPGA controls the camera to obtain images of the bridge cracks and transmits them to the host computer.

[0077] In the embodiment of the present application, the image acquisition system of the tethered flying and climbing robot includes: an image acquisition module, an FPGA image data processing module, and an image cache module, which are transmitted to the host computer through the Ethernet image transmission module. After the host computer obtains the defective image, it processes the defective image.

[0078] In the embodiment of the present application, the OV5640 image sensor is selected, and the sensor needs to be configured so that it can obtain the bridge crack image after the system is powered on.

[0079] In an embodiment of the present application, the camera is initialized and configured through an initialization module. After the initialization is completed, the image acquisition module is used to collect the output image data, the data output by the two image acquisition modules are spliced ​​through the image stitching module, and finally the spliced ​​image is handed over to the SDRAM cache.

[0080] In the embodiment of the present application, in the image acquisition and processing system, the present application adopts SDRAM memory, which has large storage capacity, fast read and write speed, and is suitable for real-time image cache. The SDRAM control module includes an SDRAM command control module, an SDRAM state control module, and an SDRAM data read and write module, wherein:

[0081] (1) SDRAM state control module: FPGA uses a state machine to control the various states of SDRAM, such as read, write, and refresh. The following figure shows the internal state transition diagram of the SDRAM controller.

[0082] (2) SDRAM command control module: Assign values ​​to CKE, CS, RAS, CAS, WE and address lines according to the status output by the status control module. Different combinations of CKE, CS, RAS, CAS, and WE signals represent different control commands of SDRAM;

[0083] (3) SDRAM data read / write module: responsible for reading data from SDRAM and writing data to SDRAM;

[0084] (4) FIFO control module: SDRAM is mainly used to cache real-time image data. Since the clock of the image acquisition module is inconsistent with the clock of the image processing module, and the clock of the Gigabit Ethernet receiving module and the HDMI sending module at the image receiving end are inconsistent, a FIFO needs to be added to avoid design problems caused by cross-clock domains.

[0085] In step S204, a crack detection operation is performed on the first region image to obtain a crack detection result.

[0086] In step S205, if the crack detection result shows that cracks exist, a depth acquisition operation is performed on the cracks to obtain first crack data corresponding to the first detection area, and the flying robot is controlled to move to the next detection area of ​​the N crack detection areas to perform crack detection operations.

[0087] In an embodiment of the present application, after the crack image is detected, the crack image is processed by a host computer and the crack area is calculated. Then, the depth information of the crack is obtained according to the improved ultrasonic level measurement method, uploaded to the display and processed to obtain the depth information of the current crack.

[0088] In step S206, if the crack detection result is that no crack exists, the flying climbing robot is controlled to move to the next detection area of ​​the N crack detection areas to perform a crack detection operation.

[0089] In step S207, after the crack detection operation on all crack detection areas is completed, the crack detection results of the bridge pylons are obtained.

[0090] In the embodiment of the present application, when collecting bridge crack data, the camera and ultrasonic detection equipment are driven to rotate by controlling the turntable structure to achieve all-round scanning and detection of the bridge tower. The bridge is scanned by a camera, and after post-image processing and analysis, the crack detection of the bridge is achieved. The machine vision camera has a short shutter time, a high frame rate, and stable performance. It can also take clear pictures when mounted on a flying and climbing robot, and can perform efficient image acquisition. In order to reduce the impact of ambient light intensity on the image collected by the camera and avoid greatly reducing the image quality, a fill light is added at a reasonable position to improve the picture quality and improve the quality of machine vision acquisition. After obtaining the bridge crack image, the depth information of the corresponding crack position is collected through the improved ultrasonic flat measurement method, and then the data is received and processed on the host computer to achieve accurate measurement of the defect depth.

[0091] In an embodiment of the present application, a crack detection method applied to a bridge pylon is provided, comprising: receiving a crack detection request carrying N crack detection areas sent by a user terminal, wherein N is a positive integer greater than or equal to 1; starting a tethered flying and climbing robot, and controlling the tethered flying and climbing robot to move to a first detection area of ​​the N crack detection areas; controlling a camera device mounted on the tethered flying and climbing robot to collect image data of the first detection area to obtain a first area image; performing a crack detection operation on the first area image to obtain a crack detection result; if the crack detection result is that a crack exists, performing a depth acquisition operation on the crack to obtain first crack data corresponding to the first detection area, and controlling the tethered flying and climbing robot to move to the next detection area of ​​the N crack detection areas to perform a crack detection operation; if the crack detection result is that a crack does not exist, controlling the tethered flying and climbing robot to move to the next detection area of ​​the N crack detection areas to perform a crack detection operation; after completing the crack detection operation on all crack detection areas, the crack detection result of the bridge pylon is obtained. Compared with the prior art, the present application controls a tethered flying-climbing robot to move at a constant distance on the surface of the tower, and collects images of the surface area of ​​the tower through the camera equipment of the tethered flying-climbing robot. In addition, crack detection operation is performed on the regional image, and crack data of the crack area is further obtained based on the crack detection results, so as to achieve high-precision and flexible traversal measurement of cracks in bridge towers and the accuracy of effective data detection, thereby ensuring the authenticity and reliability of the detection data.

[0092] In some optional implementations of this embodiment, the above step S205 may further perform the following steps:

[0093] Performing an area calculation operation on the crack position of the first region image to obtain crack area data;

[0094] A scanning operation is performed on the crack position of the first detection area to obtain crack depth data.

[0095] In the embodiments of the present application, the crack detection operation mainly collects the depth information of the corresponding crack position by using the principle of the improved ultrasonic flat measurement method for detecting crack depth.

[0096] In some alternative implementation manners of this embodiment, the step of scanning the crack position in the first detection area to obtain crack depth data specifically includes the following steps:

[0097] Perform a ranging operation on the crack position according to the improved ultrasonic flat measurement method to obtain crack depth data.

[0098] In the embodiments of the present application, the cracks in the bridge tower are usually inclined cracks. The present application designs a depth measurement scheme for inclined cracks. Specifically:

[0099] First, establish a model of the concrete inclined crack as shown in Figure 8 , Figure 8 where θ, m, and h respectively represent the inclination angle, extension length, and crack depth of the inclined crack.

[0100] According to Figure 8 the model to perform flat measurement on the inclined crack, there are the following assumption conditions: ① The concrete quality at the crack location and its vicinity is basically the same. ② The first wave signal measured across the crack reaches the receiving transducer by bypassing the crack end. ③ The wave velocity of the diffracted longitudinal wave is basically the same as the longitudinal wave velocity of the nearby concrete.

[0101] First, it is necessary to measure which side the crack inclines to. Generally, transducers can be arranged symmetrically at 5 cm points on both the left and right sides of the crack with a ranging of 20 cm for detection, and the first wave travel times t_left and t_right are read. If t_left = t_right, the crack is perpendicular to the detection plane and is a vertical crack; if t_left < t_right, the crack inclines to the left; if t_left > t_right, the crack inclines to the right. Since the operation is simple, it will not be elaborated here.

[0102] Then, as shown in Figure 8 , place the two transducers symmetrically with respect to the crack at positions A, B and A', B'. The distances between the inner edges of the transducers are L and L' respectively. The transmitting transducer should be placed on the side where the crack inclines, and measure the travel time t1 of the sound wave propagating through and the travel time t2 of the sound wave propagating through . If the longitudinal wave velocity of the concrete is v u , then:

[0103]

[0104] According to the cosine theorem of a triangle, it can be obtained that:

[0105]

[0106] From formula (2), we can get:

[0107]

[0108] Same reason After calculation, we can get:

[0109]

[0110] By combining equations (3) and (4), we can immediately solve the crack inclination angle θ and extension length m.

[0111] According to trigonometric function, the crack depth h can be obtained as:

[0112] h=msinθ(5)

[0113] ① Determination of longitudinal wave velocity: The longitudinal wave velocity can be determined by the longitudinal wave velocity theoretical formula or the ultrasonic contralateral method. The longitudinal wave velocity theoretical formula is:

[0114]

[0115] Where: v u is the longitudinal wave velocity, E is Young's elastic modulus, ρ is the density of concrete, and v is Poisson's ratio.

[0116] ② Determination of actual sound path: When measuring horizontally, the distance is measured based on the inner edge of the transducer, which can improve the accuracy of distance measurement. However, in actual projects, it is found that the distance between the centers of the transducers is not the actual propagation distance of the ultrasonic wave. Therefore, in order to improve the accuracy of crack depth testing, the sound path will be corrected.

[0117] First, the acoustic time is measured at points near the oblique crack using the non-cross-crack detection method. L (the distance between the inner edges of the two transducers) is set to 50, 100, 150, 170, and 200 mm respectively. The acoustic time value t is measured. i The least square method is used to obtain the regression line equation of the distance measurement and the acoustic time, as shown in equation (7), where v is the ultrasonic wave velocity without crossing the gap, and |c| is the correction value.

[0118] L i =c+vt i (7)

[0119] The actual sound path after correction i for:

[0120] l i =L i +|c|(8)

[0121] l i =2a i .

[0122] In some optional implementations of this embodiment, the step of performing an area calculation operation on the crack position of the first region image to obtain crack area data specifically includes the following steps:

[0123] Performing crack image grayscale processing on the first region image to obtain a grayscale region image;

[0124] Perform crack image binarization processing on the grayscale region image to obtain a binary region image;

[0125] Performing expansion and corrosion operation processing on the binary region image to obtain an erosion and expansion region image;

[0126] Perform edge detection on the corrosion expansion area image according to the Canny edge detection method to obtain the pixel value of the crack edge part;

[0127] The crack area data of the first region image is calculated according to the pixel values.

[0128] In the embodiment of the present application, in order to obtain the area of ​​the measured bridge crack, the obtained crack image needs to be further processed to make the final calculated crack area more accurate.

[0129] In the embodiment of the present application, the grayscale processing of the crack image adopts the weighted average method. When calculating the grayscale value of the grayscale image, the three colors of the color image are weighted differently in combination with the factors of color perception. The weighted values ​​are based on the sensitivity of people to the three colors, from high to low, namely green, red, and blue, that is:

[0130] f(i,j)=0.30R(i,j)+0.59G(i,j)+0.11B(i,j)

[0131] In the embodiment of the present application, the size of the image is further compressed on the basis of the grayscale image, and the bridge wall and the crack can be separated after binarization to simplify the difficulty of subsequent calculations. Among them, the histogram bimodal method is used, which has a fast processing speed and is suitable for images with relatively regular grayscale distribution. The background and target in the image appear in the shape of different peaks on the grayscale image. When the grayscale value at the trough between two peaks is selected as the threshold of the binary image, the targets corresponding to adjacent peaks are divided.

[0132]

[0133] Among them, f(x,y) represents the grayscale value of the grayscale image, T is the binarization image threshold, and g(x,y) is the binarized image.

[0134] In an embodiment of the present application, a grayscale averaged histogram of a crack image obtained during camera shooting is used, and the binary threshold of the crack grayscale image is taken at the trough according to the bimodal method, so that the crack and the background can be segmented.

[0135] In the embodiment of the present application, after the bridge crack image is binarized, the crack and the bridge wall background are separated. However, due to factors such as light, the separated image has some irregular points or other noises in shape and size, which will affect the subsequent area calculation. This design uses morphological operations to suppress the noise in the binary crack image without affecting the crack area calculation. The binary crack image is first opened sequentially to remove the noise in the image, and then closed to reduce the impact of the open operation on the defect area calculation.

[0136] In the embodiment of the present application, the present application adopts the edge detection principle and uses the Canny edge detection method to perform edge detection on the binary crack image after morphological operation and extract the pixel value of the crack edge part. Among them, the Canny edge detection method is a multi-level edge detection algorithm with high accuracy in identifying image edges. First, the input image is Gaussian smoothed to reduce the error rate; secondly, the gradient amplitude and direction are calculated to estimate the edge strength and direction at each point; then, according to the gradient direction, the gradient amplitude is non-maximum suppressed; finally, double threshold processing and connection edges are used.

[0137] In the embodiment of the present application, since the expansion and corrosion operation is used in image processing, the edge of the crack is enlarged. According to the expansion and corrosion principle, the enlarged part is approximately half of the original edge area. Therefore, when calculating the actual pixels of the crack, it is necessary to subtract half of the edge pixel value to obtain a more accurate measurement result.

[0138] In some optional implementations of this embodiment, the step of calculating the crack area data of the first region image according to the pixel values ​​specifically includes the following steps:

[0139] Correct the pixel value according to the preset size calibration;

[0140] The crack area data of the first region image is calculated according to the corrected pixel values.

[0141] In the embodiment of the present application, size calibration in an image refers to establishing a correspondence between the pixel coordinate system of an image and the physical coordinate system in the actual scene, so as to precisely measure the target in the actual scene. In the bridge crack image detection system, it is necessary to calibrate the size of the crack image, determine the correspondence between the crack image and the actual crack through size calibration, and then obtain the actual area of ​​the crack through the image pixels and the correspondence. Since the size of the crack image obtained by the camera changes, it is related to factors such as the focal length of the camera lens, the installation position of the camera, and the shooting angle. At the same time, when the flying and climbing robot collects images, errors will occur due to problems such as its own position and mechanical structure. Therefore, when performing image size calibration, it is necessary to ensure that the calibration process is the same as the conditions for actually obtaining the crack image to ensure the accuracy of the calibration and reduce the measurement error.

[0142] In an embodiment of the present application, a circle of standard grid paper of known size is fixed on the bridge wall to be measured, and some areas of known size are selected on the grid paper and filled with black. The actual area after filling is assumed to be S. Then, through the crack image detection system, the image of the black filled domain on the bridge wall grid paper is obtained by using a camera, and the number of pixels A in the black area and the number of pixels B on the defect edge in the image are obtained by image processing. Then, the actual area s represented by a single image pixel in the actual measurement can be obtained by calculation, that is, the pixel equivalent, and the size calibration of the image is completed. When processing the image, the crack edge will be affected. When performing morphological operations, half of an edge pixel is inside the crack and the other half is outside the crack. Therefore, in order to make the crack area measurement result more accurate, a method of removing half of the target edge pixel number is used for correction, as shown in the following formula;

[0143] s=S / (AB / 2+1)

[0144] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0145] The basic technologies of artificial intelligence generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, mechatronics, etc. Artificial intelligence software technology mainly includes computer vision technology, robotics technology, biometrics technology, speech processing technology, natural language processing technology, and machine learning / deep learning. A person of ordinary skill in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through computer-readable instructions, and the computer-readable instructions can be stored in a computer-readable storage medium. When the program is executed, it may include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium may be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0146] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0147] Embodiment 2

[0148] Further references Fig. 9 , as a response to the above Figure 2 In order to realize the method shown in the figure, the present application provides an embodiment of a crack detection device applied to a bridge pylon, and the device embodiment is similar to Figure 2 Corresponding to the method embodiment shown, the device can be specifically applied to various electronic devices.

[0149] like Fig. 9 As shown, the crack detection device 200 applied to the bridge pylon of this embodiment includes:

[0150] The request receiving module 210 is configured to receive a crack detection request carrying N crack detection areas sent by a user terminal, where N is a positive integer greater than or equal to 1;

[0151] A robot control module 220 is used to start the tethered flying and climbing robot and control the tethered flying and climbing robot to move to a first detection area of ​​the N crack detection areas;

[0152] The image acquisition module 230 is used to control the camera device mounted on the tethered flying and climbing robot to acquire image data of the first detection area to obtain an image of the first area;

[0153] A crack detection module 240 is used to perform a crack detection operation on the first region image to obtain a crack detection result;

[0154] The first result module 250 is used to perform a depth acquisition operation on the cracks to obtain first crack data corresponding to the first detection area if the crack detection result is that cracks exist, and control the flying and climbing robot to move to the next detection area of ​​the N crack detection areas to perform crack detection operations;

[0155] The second result module 260 is used to control the flying robot to move to the next detection area of ​​the N crack detection areas to perform crack detection operations if the crack detection result is that there is no crack;

[0156] The result confirmation module 270 is used to obtain the crack detection results of the bridge pylon after completing the crack detection operations on all crack detection areas.

[0157] In this embodiment, a crack detection device 200 applied to a bridge pylon is provided, including: a request receiving module 210, for receiving a crack detection request carrying N crack detection areas sent by a user terminal, wherein N is a positive integer greater than or equal to 1; a robot control module 220, for starting a tethered flying and climbing robot, and controlling the tethered flying and climbing robot to move to a first detection area of ​​the N crack detection areas; an image acquisition module 230, for controlling a camera mounted on the tethered flying and climbing robot to acquire image data of the first detection area to obtain an image of the first area; a crack detection module 240, for performing crack detection on the image of the first area. Detection operation, to obtain crack detection results; the first result module 250, for if the crack detection result is that there are cracks, then the depth acquisition operation is performed on the cracks to obtain the first crack data corresponding to the first detection area, and the tethered flying climbing robot is controlled to move to the next detection area of ​​the N crack detection areas to perform crack detection operations; the second result module 260, for if the crack detection result is that there are no cracks, then the tethered flying climbing robot is controlled to move to the next detection area of ​​the N crack detection areas to perform crack detection operations; the result confirmation module 270, for after completing the crack detection operations on all crack detection areas, obtain the crack detection results of the bridge cable tower. Compared with the prior art, the present application controls the tethered flying climbing robot to move on the cable tower surface at a constant distance, and collects images of the cable tower surface area through the camera equipment of the tethered flying climbing robot, in addition, the crack detection operation is performed on the regional image, and the crack data of the crack area is further obtained according to the crack detection result, so as to realize the high-precision and flexible traversal measurement of the cracks of the bridge cable tower, the accuracy of effective data detection, and thus ensure the authenticity and reliability of the detection data.

[0158] In some optional implementations of this embodiment, the first result module includes:

[0159] An area calculation submodule, used to perform an area calculation operation on the crack position of the first region image to obtain crack area data;

[0160] The detection submodule is used to perform a scanning detection operation on the crack position of the first detection area according to the ultrasonic equipment to obtain crack depth data.

[0161] In some optional implementations of this embodiment, the scanning submodule includes:

[0162] The distance measuring unit is used to measure the crack position according to the improved ultrasonic level measurement method to obtain the crack depth data.

[0163] To solve the above technical problems, the present application also provides a computer device. Fig.10 , Fig.10This is a basic structural block diagram of the computer device in this embodiment.

[0164] The computer device 300 includes a memory 310, a processor 320, and a network interface 330 that are interconnected and communicated through a system bus. It should be noted that the figure only shows a computer device 300 having components 310-330, but it should be understood that it is not required to implement all the components shown, and more or fewer components can be implemented instead. Among them, those skilled in the art can understand that the computer device here is a device that can automatically perform numerical calculations and / or information processing according to pre-set or stored instructions, and its hardware includes but is not limited to microprocessors, application specific integrated circuits (Application Specific Integrated Circuit, ASIC), programmable gate arrays (Field-Programmable Gate Array, FPGA), digital processors (Digital Signal Processor, DSP), embedded devices, etc.

[0165] The computer device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The computer device may interact with a user through a keyboard, a mouse, a remote controller, a touch pad, or a voice control device.

[0166] The memory 310 includes at least one type of readable storage medium, and the readable storage medium includes flash memory, hard disk, multimedia card, card-type memory (for example, SD or DX memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 310 can be an internal storage unit of the computer device 300, such as a hard disk or memory of the computer device 300. In other embodiments, the memory 310 can also be an external storage device of the computer device 300, such as a plug-in hard disk equipped on the computer device 300, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card, etc. Of course, the memory 310 can also include both the internal storage unit of the computer device 300 and its external storage device. In this embodiment, the memory 310 is generally used to store an operating system and various application software installed on the computer device 300, such as computer-readable instructions for a crack detection method for a bridge pylon, etc. In addition, the memory 310 can also be used to temporarily store various data that have been output or are to be output.

[0167] The processor 320 may be a central processing unit (CPU), a controller, a microcontroller, a microprocessor, or other data processing chips in some embodiments. The processor 320 is generally used to control the overall operation of the computer device 300. In this embodiment, the processor 320 is used to run the computer-readable instructions stored in the memory 310 or process data, such as running the computer-readable instructions of the crack detection method applied to the bridge pylon.

[0168] The network interface 330 may include a wireless network interface or a wired network interface. The network interface 330 is generally used to establish a communication connection between the computer device 300 and other electronic devices.

[0169] The computer device provided in the present application controls a tethered flying and climbing robot to move at a constant distance on the surface of the tower, and collects images of the surface area of ​​the tower through the camera equipment of the tethered flying and climbing robot. In addition, crack detection operations are performed on the regional images, and crack data of the crack area is further obtained based on the crack detection results, so as to achieve high-precision and flexible traversal measurement of cracks in bridge towers and the accuracy of effective data detection, thereby ensuring the authenticity and reliability of the detection data.

[0170] The present application also provides another embodiment, namely, providing a computer-readable storage medium, wherein the computer-readable storage medium stores computer-readable instructions, and the computer-readable instructions can be executed by at least one processor to enable the at least one processor to perform the steps of the crack detection method applied to bridge towers as described above.

[0171] The computer-readable storage medium provided by the present application controls a tethered flying and climbing robot to move at a constant distance on the surface of a cable tower, and collects images of the surface area of ​​the cable tower through the camera equipment of the tethered flying and climbing robot. In addition, crack detection operations are performed on the regional images, and crack data of the crack area is further obtained based on the crack detection results, so as to achieve high-precision and flexible traversal measurement of cracks in bridge cable towers and the accuracy of effective data detection, thereby ensuring the authenticity and reliability of the detection data.

[0172] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application, or the part that contributes to the prior art, can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes a number of instructions for a terminal device (which can be a mobile phone, computer, server, air conditioner, or network device, etc.) to execute the methods described in each embodiment of the present application.

[0173] Obviously, the embodiments described above are only some embodiments of the present application, rather than all embodiments. The preferred embodiments of the present application are given in the accompanying drawings, but they do not limit the patent scope of the present application. The present application can be implemented in many different forms. On the contrary, the purpose of providing these embodiments is to make the understanding of the disclosure of the present application more thorough and comprehensive. Although the present application is described in detail with reference to the aforementioned embodiments, for those skilled in the art, it is still possible to modify the technical solutions recorded in the aforementioned specific implementation methods, or to perform equivalent replacement of some of the technical features therein. Any equivalent structure made using the contents of the specification and drawings of this application, directly or indirectly used in other related technical fields, is similarly within the scope of patent protection of this application.

Claims

1. A crack detection method applied to bridge pylons, characterized in that: The method is applied to a tethered flying and climbing robot, and comprises the following steps: Receiving a crack detection request carrying N crack detection areas sent by a user terminal, where N is a positive integer greater than or equal to 1; Starting the tethered flying and climbing robot, and controlling the tethered flying and climbing robot to move to a first detection area of ​​the N crack detection areas; Controlling a camera device mounted on the tethered flying and climbing robot to collect image data of the first detection area to obtain a first area image; Performing a crack detection operation on the first region image to obtain a crack detection result; If the crack detection result is that cracks exist, a depth acquisition operation is performed on the cracks to obtain first crack data corresponding to the first detection area, and the stay-type flying and climbing robot is controlled to move to the next detection area of ​​the N crack detection areas to perform crack detection operations; If the crack detection result is that there is no crack, controlling the flying and climbing robot to move to the next detection area of ​​the N crack detection areas to perform a crack detection operation; After the crack detection operation on all the crack detection areas is completed, the crack detection results of the bridge pylons are obtained.

2. The crack detection method for bridge pylons according to claim 1, characterized in that: The first crack data includes crack area data and crack depth data. The step of performing a depth acquisition operation on the crack to obtain the first crack data corresponding to the first detection area specifically includes the following steps: Performing an area calculation operation on the crack position of the first regional image to obtain the crack area data; The crack depth data is obtained by scanning and detecting the crack position in the first detection area using ultrasonic equipment.

3. The crack detection method applied to bridge pylons according to claim 2 is characterized in that: The step of performing a scanning and detection operation on the crack position of the first detection area according to the ultrasonic equipment specifically includes the following steps: The crack position is measured using an improved ultrasonic level measurement method to obtain crack depth data.

4. The crack detection method for bridge pylons according to claim 2, characterized in that: The step of performing an area calculation operation on the crack position of the first region image to obtain the crack area data specifically includes the following steps: Performing crack image grayscale processing on the first region image to obtain a grayscale region image; Perform crack image binarization processing on the grayscale region image to obtain a binary region image; Performing expansion and corrosion operation processing on the binary region image to obtain an eroded and expanded region image; Performing edge detection on the corrosion expansion region image according to the Canny edge detection method to obtain pixel values ​​of the crack edge portion; The crack area data of the first region image is calculated according to the pixel value.

5. The crack detection method for bridge pylons according to claim 4, characterized in that: The step of calculating the crack area data of the first regional image according to the pixel value specifically comprises the following steps: Performing a correction operation on the pixel value according to a preset size calibration; The crack area data of the first region image is calculated according to the corrected pixel value.

6. A crack detection device for bridge pylons, characterized in that: include: A request receiving module, configured to receive a crack detection request carrying N crack detection areas sent by a user terminal, where N is a positive integer greater than or equal to 1; A robot control module, used for starting the tethered flying and climbing robot, and controlling the tethered flying and climbing robot to move to a first detection area of ​​the N crack detection areas; An image acquisition module, used for controlling a camera device mounted on the tethered flying and climbing robot to acquire image data of the first detection area to obtain an image of the first area; A crack detection module, used to perform a crack detection operation on the first region image to obtain a crack detection result; A first result module is used for, if the crack detection result is that cracks exist, performing a depth acquisition operation on the cracks to obtain first crack data corresponding to the first detection area, and controlling the stay-type flying and climbing robot to move to the next detection area of ​​the N crack detection areas to perform a crack detection operation; A second result module is used for controlling the flying and climbing robot to move to the next detection area of ​​the N crack detection areas to perform a crack detection operation if the crack detection result is that there is no crack; The result confirmation module is used to obtain the crack detection result of the bridge pylon after completing the crack detection operation on all the crack detection areas.

7. The crack detection device for bridge pylons according to claim 6, characterized in that: The first result module includes: An area calculation submodule, used for performing an area calculation operation on the crack position of the first regional image to obtain the crack area data; The detection submodule is used to perform a scanning detection operation on the crack position of the first detection area according to the ultrasonic equipment.

8. The crack detection device for bridge pylons according to claim 7, characterized in that: The scanning submodule comprises: The distance measuring unit is used to measure the distance of the crack position according to the improved ultrasonic level measurement method to obtain the crack depth data.

9. A computer device, comprising a memory and a processor, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, the steps of the crack detection method applied to a bridge pylon as claimed in any one of claims 1 to 5 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-readable instructions, and when the computer-readable instructions are executed by a processor, the steps of the crack detection method applied to a bridge pylon as claimed in any one of claims 1 to 5 are implemented.

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