CN118670275B - Building crack check out test set - Google Patents
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- CN118670275B CN118670275B CN202411155896.0A CN202411155896A CN118670275B CN 118670275 B CN118670275 B CN 118670275B CN 202411155896 A CN202411155896 A CN 202411155896A CN 118670275 B CN118670275 B CN 118670275B
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01B—MEASURING LENGTH, THICKNESS OR SIMILAR LINEAR DIMENSIONS; MEASURING ANGLES; MEASURING AREAS; MEASURING IRREGULARITIES OF SURFACES OR CONTOURS
- G01B11/00—Measuring arrangements characterised by the use of optical techniques
- G01B11/02—Measuring arrangements characterised by the use of optical techniques for measuring length, width or thickness
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- E—FIXED CONSTRUCTIONS
- E01—CONSTRUCTION OF ROADS, RAILWAYS, OR BRIDGES
- E01D—CONSTRUCTION OF BRIDGES, ELEVATED ROADWAYS OR VIADUCTS; ASSEMBLY OF BRIDGES
- E01D19/00—Structural or constructional details of bridges
- E01D19/10—Railings; Protectors against smoke or gases, e.g. of locomotives; Maintenance travellers; Fastening of pipes or cables to bridges
- E01D19/106—Movable inspection or maintenance platforms, e.g. travelling scaffolding or vehicles specially designed to provide access to the undersides of bridges
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- G—PHYSICS
- G01—MEASURING; TESTING
- G01W—METEOROLOGY
- G01W1/00—Meteorology
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Abstract
本发明涉及工程检测技术领域，具体涉及一种建筑裂缝检测设备，包括无人机、爬壁车、环境识别模块、裂缝识别模块和控制器，爬壁车设置在无人机的顶部，环境识别模块用于桥梁所处周围的环境参数，裂缝识别模块用于获取桥梁裂缝数据，控制器，被配置为：若桥梁所处周围的环境参数属于第一目标类型，则以第一检测模式进行检测；若桥梁所处周围的环境参数属于第二目标类型，则以第二检测模式进行检测，其中桥梁所处周围的环境参数至少包括天气信息。本发明提供的建筑裂缝检测设备，能够根据天气信息，合理选择检测模式，使得在尽可能保证检测结果准确度的情况下，提高无人机检测时的安全性。
The present invention relates to the field of engineering detection technology, and specifically to a building crack detection device, including an unmanned aerial vehicle, a wall climbing vehicle, an environment recognition module, a crack recognition module and a controller, wherein the wall climbing vehicle is arranged on the top of the unmanned aerial vehicle, the environment recognition module is used for the environmental parameters around the bridge, the crack recognition module is used to obtain bridge crack data, and the controller is configured to: if the environmental parameters around the bridge belong to the first target type, then the detection is performed in a first detection mode; if the environmental parameters around the bridge belong to the second target type, then the detection is performed in a second detection mode, wherein the environmental parameters around the bridge at least include weather information. The building crack detection device provided by the present invention can reasonably select the detection mode according to the weather information, so as to improve the safety of the unmanned aerial vehicle detection while ensuring the accuracy of the detection result as much as possible.
Description
Technical Field
The invention relates to the technical field of engineering detection, in particular to building crack detection equipment.
Background
Along with the continuous development of highway traffic industry in China, the number of bridges is increased, and during the use period of the bridges, the bridges inevitably suffer from various traffic loads and environmental factors such as random traffic loads, strong wind, rain and snow, earthquakes, freezing and other environmental factors, and meanwhile, the bridges can suffer from damage caused by heavy traffic, even overload, impact damage and other artificial factors, and the damage has various manifestations such as concrete carbonization, damage cracking, reinforcement corrosion, support void, deformation and the like, wherein the damage of the bridges is mostly caused by cracks, so that in the health detection of bridge structures, the detection, identification and extraction of the bridge cracks and the analysis and prediction of crack expansion are particularly important to ensure the operation and maintenance safety of the structures.
Because the bridge is mostly erected in the air, a plurality of areas needing to be detected cannot be directly reached or can be reached by personnel or detection equipment, but cannot be detected in a close way, and the unmanned aerial vehicle has the advantages of being flexible, capable of working aloft and acquiring image information outside the sight distance of the personnel, the unmanned aerial vehicle is widely applied to bridge detection along with the rapid development of unmanned aerial vehicle technology, and the unmanned aerial vehicle is adopted for bridge crack detection, so that the working efficiency during detection can be improved, and the life safety of inspection personnel can be ensured.
In the related art, for example, a reference with an authorized bulletin number of CN108051450B discloses a bridge health detection system and a method based on an unmanned aerial vehicle, where the unmanned aerial vehicle-based bridge health detection system collects detected crack data obtained by the unmanned aerial vehicle, and compares the detected crack data with historical data of a database as a basis to obtain a crack change trend, so that a worker can predict future development of a crack conveniently, and perform maintenance in time, so that maintenance efficiency is improved, and normal use of a bridge to be detected is ensured.
When the bridge is erected at the valleys, the bridge is often covered by fog due to the special geographical environment of the valleys, so that the existence of the fog can interfere with the detection result of the unmanned aerial vehicle when the unmanned aerial vehicle detects cracks on the bridge.
Disclosure of Invention
Based on the above, it is necessary to provide a construction crack detection device for solving the problem that the detection result is inaccurate in the current bridge crack detection process.
The above purpose is achieved by the following technical scheme:
a construction crack detection apparatus comprising:
Unmanned plane;
the wall climbing vehicle is arranged at the top of the unmanned aerial vehicle;
the environment recognition module is used for acquiring environment parameters around the bridge;
The crack identification module is used for acquiring bridge crack data;
a controller configured to:
If the environmental parameters around the bridge are of the first target type, detecting in a first detection mode;
if the environmental parameters around the bridge are of the second target type, detecting in a second detection mode;
The environmental parameters of the surrounding of the bridge at least comprise weather information;
the environment recognition module is used for recognizing the type of fog at the same time;
The controller is configured to detect in a first detection mode if an environmental parameter around the bridge is of a first target type, and specifically includes:
the first target type is fog, and when the part of the area to be detected is in the fog area, the detection is carried out according to a first detection program according to the fog type;
when the area to be detected is not in the fog area, detecting according to a second detection program;
Wherein the fog types at least comprise uphill fog, valley fog, advection fog and evaporation fog;
the controller is configured to detect according to a first detection program according to the type of fog when the area to be detected is partially in the fog area, and specifically comprises:
when the type of the fog is uphill fog, starting the unmanned aerial vehicle, moving to the lower part of a fog area, and when the wall climbing vehicle contacts the bridge wall surface, starting the wall climbing vehicle, advancing on the bridge wall surface according to a first preset path, and identifying cracks on the bridge through the crack identification module;
When the fog is advection fog/evaporation fog, starting the unmanned aerial vehicle, moving the unmanned aerial vehicle to the upper part of a fog area, starting the wall climbing vehicle when the wall climbing vehicle contacts the bridge wall surface, advancing the wall climbing vehicle on the bridge wall surface according to a second preset path, and identifying cracks on the bridge through the crack identification module;
When the fog is valley fog, starting the unmanned aerial vehicle, moving to a non-fog area, and when the wall climbing vehicle contacts the bridge wall surface, starting the wall climbing vehicle, advancing on the bridge wall surface according to a third preset path, and identifying cracks on the bridge through the crack identification module;
The environment recognition module is used for recognizing the type of the bridge wall surface at the same time, and the type of the bridge wall surface at least comprises one or the combination of at least two of a straight surface, an inclined surface and an arc surface;
The controller is configured to detect in a second detection mode if the environmental parameter around the bridge is of a second target type, and specifically includes:
when the type of the bridge wall surface is a straight surface, starting the unmanned aerial vehicle, flying according to a sixth preset path, and identifying the crack on the bridge through the crack identification module;
When the type of the bridge wall surface is an inclined surface, an arc surface or a combination of at least two of a straight surface, an inclined surface and an arc surface, starting the unmanned aerial vehicle, moving to a region to be detected, when the wall climbing vehicle contacts the bridge wall surface, starting the wall climbing vehicle, advancing on the bridge wall surface according to a seventh preset path, and identifying a crack on the bridge through the crack identification module.
Further, the environment recognition module is simultaneously used for recognizing the type of the bridge wall surface, and the type of the bridge wall surface at least comprises one or the combination of at least two of a straight surface, an inclined surface and an arc surface;
The controller is configured to detect according to a second detection program when the area to be detected is not in the fog area, and specifically includes:
When the type of the bridge wall surface is a straight surface, starting the unmanned aerial vehicle, flying according to a fourth preset path, and identifying the crack on the bridge through the crack identification module;
When the type of the bridge wall surface is an inclined surface, an arc surface or a combination of at least two of a straight surface, an inclined surface and an arc surface, starting the unmanned aerial vehicle, moving to the area to be detected, when the wall climbing vehicle contacts the bridge wall surface, starting the wall climbing vehicle, advancing on the bridge wall surface according to a fifth preset path, and identifying a crack on the bridge through the crack identification module.
Further, the area to be detected comprises a side wall surface, a bottom surface and a surface of the bridge pier.
Further, the building crack detection device further comprises a first acquisition module, wherein the first acquisition module is used for acquiring the concentration of fog;
the controller is further configured to:
when the wall climbing vehicle advances on the bridge, the rotating speed of the rotor wing on the unmanned aerial vehicle is adjusted in a proportional mode according to the concentration of the fog.
Further, the fracture data includes a length and a width of the fracture;
the controller is further configured to:
When the length of the crack is greater than or equal to a preset length and the width of the crack is greater than or equal to a first preset width, marking the crack as a first repair echelon;
when the length of the crack is greater than or equal to the preset length and the width of the crack is smaller than the first preset width, or the length of the crack is smaller than the preset length and the width of the crack is greater than or equal to the first preset width, marking the crack as a second repair echelon;
and marking the crack as a third repair ladder when the length of the crack is smaller than the preset length and the width of the crack is smaller than the first preset width.
Further, the controller is further configured to:
When the crack belongs to the first repair echelon or the second repair echelon or the third repair echelon and the width of the crack is smaller than the second preset width, marking the crack as needing to be widened;
wherein the second preset width is smaller than the first preset width.
Further, the fracture data further includes a type of fracture;
the controller is further configured to:
When the crack belongs to a first repair echelon or a second repair echelon or a third repair echelon and the type of the crack is an active crack, marking the active crack as repairing by using a flexible material;
When the crack belongs to the first repair ladder or the second repair ladder or the third repair ladder and the type of the crack is a static crack, marking the movable crack as repaired using a rigid material.
The beneficial effects of the invention are as follows:
When the building crack detection equipment is used, the environment recognition module is used for acquiring the environment parameters around the bridge, when the environment parameters around the bridge belong to a first target type, the controller is used for controlling the unmanned aerial vehicle and the wall climbing vehicle to detect in a first detection mode, and when the environment parameters around the bridge belong to a second target type, the controller is used for controlling the unmanned aerial vehicle and the wall climbing vehicle to detect in a second detection mode, so that the detection mode can be reasonably selected according to weather information, and the safety of the unmanned aerial vehicle during detection is improved under the condition that the accuracy of a detection result is ensured as much as possible.
Drawings
Fig. 1 is a schematic perspective view of a construction crack detection device according to an embodiment of the present invention;
Fig. 2 is a schematic structural diagram of a bridge erected at a valley according to the present invention;
FIG. 3 is a flowchart of a control method of a construction crack detection apparatus according to an embodiment of the present invention;
Fig. 4 is a flowchart of a control method of the construction crack detection apparatus according to an embodiment of the present invention.
Wherein:
1. unmanned plane;
2. a wall climbing vehicle;
3. A bridge abutment;
4. bridge pier 41, straight face 42, inclined face 43 and cambered surface.
Detailed Description
The present invention will be further described in detail below with reference to examples, which are provided to illustrate the objects, technical solutions and advantages of the present invention. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The numbering of components herein, such as "first," "second," etc., is used merely to distinguish between the described objects and does not have any sequential or technical meaning. The terms "connected," "coupled," and "connected," as used herein, unless otherwise indicated, are defined as connected and coupled directly or indirectly. In the description of the present invention, it should be understood that the terms "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", etc. indicate orientations or positional relationships based on the orientations or positional relationships shown in the drawings, are merely for convenience in describing the present invention and simplifying the description, and do not indicate or imply that the device or element in question must have a specific orientation, be configured and operated in a specific orientation, and thus should not be construed as limiting the present invention.
In the present invention, unless expressly stated or limited otherwise, a first feature "up" or "down" a second feature may be the first and second features in direct contact, or the first and second features in indirect contact via an intervening medium. Moreover, a first feature being "above," "over" and "on" a second feature may be a first feature being directly above or obliquely above the second feature, or simply indicating that the first feature is level higher than the second feature. The first feature being "under", "below" and "beneath" the second feature may be the first feature being directly under or obliquely below the second feature, or simply indicating that the first feature is less level than the second feature.
As shown in fig. 1, the construction crack detection device provided by the embodiment of the invention comprises an unmanned aerial vehicle 1, a wall climbing vehicle 2, an environment recognition module, a crack recognition module and a controller, wherein the wall climbing vehicle 2 is arranged at the top of the unmanned aerial vehicle 1, the environment recognition module is used for acquiring environmental parameters around a bridge, the crack recognition module is used for acquiring bridge crack data, the controller is configured to detect in a first detection mode if the environmental parameters around the bridge are of a first target type as shown in fig. 3, and detect in a second detection mode if the environmental parameters around the bridge are of a second target type, wherein the environmental parameters around the bridge at least comprise weather information.
In this embodiment, the environment recognition module is configured to include a first camera and a first processor which are connected by wires, and both the first camera and the first processor are arranged on the unmanned aerial vehicle 1, so that when the unmanned aerial vehicle 1 is in use, an image of the surrounding environment where the bridge is located can be obtained in real time through the first camera, image data are transmitted to the first processor while the unmanned aerial vehicle 1 is guided to fly, the first processor processes the image of the surrounding environment where the bridge is located obtained by the first camera to obtain surrounding environment parameters of the bridge, and the crack recognition module can be configured to include a second camera and a second processor which are connected by wires and both the crack recognition module and the second processor are arranged on the wall climbing vehicle 2, so that when the unmanned aerial vehicle is in use, the bridge image can be obtained in real time through the second camera and then is input into the second processor in a wired mode, and the crack data on the bridge are obtained after the second processor processes the bridge image obtained by the second camera.
Illustratively, the first camera and the second camera may each use a dimensional image MV-VD500SM/SC, a resolution of 1280×1024, a pixel size of 2.2 μm×2.2 μm, and a sampling frequency set to 3 s/time, i.e. sampling every 3 s.
The first processor and the second processor can both adopt Mali-C71 ARM microprocessors, and the ARM microprocessors can be used for identifying the surrounding environment image of the bridge, gray correction, filtering and denoising the bridge image and identifying cracks.
It will be appreciated that the first processor and the second processor may also be provided as one and the same processor.
More specifically, the controller refers to a device that can generate an operation control signal according to an instruction operation code and a timing signal. For example, the controller may be configured as a central processing unit (centralprocessing unit, CPU), a general purpose processor network processor (network processor, NP), a digital signal processor (DIGITAL SIGNAL pocessing, DSP), a microprocessor, a microcontroller, a programmable logic device (programmable logic device, PLD), or any combination thereof. The controller may also be other means for processing, such as a circuit, device, or software module.
More specifically, the weather information is set to include fog and no fog.
In the use process, the unmanned aerial vehicle 1 is started at first, the unmanned aerial vehicle 1 is operated to fly to a preset height, then the image of the periphery of the bridge is obtained through the first camera, then the image of the periphery of the bridge, which is obtained through the first processor, is processed to obtain the environmental parameter of the periphery of the bridge, when the environmental parameter of the periphery of the bridge is of a first target type, the unmanned aerial vehicle 1 and the wall climbing vehicle 2 are controlled by the controller to detect in a first detection mode, when the environmental parameter of the periphery of the bridge is of a second target type, the unmanned aerial vehicle 1 and the wall climbing vehicle 2 are controlled by the controller to detect in a second detection mode, and meanwhile, the bridge image is obtained through the second camera, and then the bridge image obtained through the second camera is processed to obtain the crack data on the bridge, so that the detection mode can be reasonably selected according to the weather information, and the safety of the unmanned aerial vehicle 1 in the detection process can be improved under the condition that the accuracy of the detection result is ensured as much as possible.
In some embodiments, as shown in FIG. 3, the weather information includes fog;
The controller is configured to detect in a first detection mode if the environmental parameter around the bridge belongs to a first target type, and specifically includes:
the first target type is fog, and when the part of the area to be detected is in the fog area, the detection is carried out according to a first detection program according to the fog type;
when the area to be detected is not in the fog area, detecting according to a second detection program;
wherein the fog types at least comprise uphill fog, valley fog, advection fog and evaporation fog.
Specifically, in this embodiment, after the first camera acquires the image data of the surrounding environment of the bridge, the image data is input into the first processor in a wired manner, and after the first processor processes the image of the surrounding environment of the bridge acquired by the first camera, the environmental parameters and the fog type of the surrounding environment where the bridge is located are obtained.
More specifically, when there is mist in the environment around the bridge, the range covered by the mist of different types and the influence on the flight trajectory of the unmanned aerial vehicle 1 are different, so that in order to ensure the accuracy of the detection result, the detection is performed according to the type of mist and according to the first detection program, wherein the ascending mist is the mist generated by the condensation of the moisture due to the ascending cooling of the moist air along the hillside, and is generally formed at the high position of the valley, the valley mist is the mist formed in particular in the valley, and is generally formed by condensation of the water vapor at night or early morning when the air in the valley is cooled, the position is not fixed, the advection mist is the mist formed by the cooling of the air below the dew point temperature when the warm moist air passes through the cold ground, and is generally formed by the rapid saturation of the air near the water surface when the warm air passes through the cold water surface, and is generally formed at the low position of the valley, and the detection program is configured to ensure the safety and the efficiency of the unmanned aerial vehicle 1 when the environment around the bridge is not present.
In a further embodiment, as shown in fig. 3, the controller is configured to detect, when the area to be detected is in the fog area, according to the type of fog according to the first detection procedure, and specifically includes:
When the type of fog is uphill fog, starting the unmanned aerial vehicle 1, moving to the lower part of a fog area, and when the wall climbing vehicle 2 contacts the wall surface of the bridge, starting the wall climbing vehicle 2, advancing on the wall surface of the bridge according to a first preset path, and identifying cracks on the bridge through a crack identification module;
When the fog is advection fog/evaporation fog, starting the unmanned aerial vehicle 1, moving to the upper part of a fog area, and when the wall climbing vehicle 2 contacts the bridge wall surface, starting the wall climbing vehicle 2, advancing on the bridge wall surface according to a second preset path, and identifying cracks on the bridge through a crack identification module;
When the fog is valley fog, the unmanned aerial vehicle 1 is started and moves to a non-fog area, when the wall climbing vehicle 2 contacts the bridge wall surface, the wall climbing vehicle 2 is started and moves on the bridge wall surface according to a third preset path, and meanwhile cracks on the bridge are identified through the crack identification module.
In particular, in this embodiment, when the type of fog is the fog of going up a slope, because it mainly forms in the eminence of valley, when waiting to detect regional part in the fog district, it mainly covers the upper end of waiting to detect regional, for the accuracy that improves unmanned aerial vehicle 1 landing point on the bridge wall, avoid appearing bumping the machine accident, set up to start unmanned aerial vehicle 1, and drive the below that wall climbing vehicle 2 moved to the fog district through unmanned aerial vehicle 1, then through the gesture of adjustment unmanned aerial vehicle 1, make unmanned aerial vehicle 1 can drive wall climbing vehicle 2 to the direction that is close to the bridge wall remove, when wall climbing vehicle 2 contacts the bridge wall, start wall climbing vehicle 2, and advance on the bridge wall according to first default route, discern the crack on the bridge through crack recognition module simultaneously, the in-process that wall climbing vehicle 2 produced lift makes wall climbing vehicle 2 can be by on the bridge wall with certain pressure, and then make wall climbing vehicle 2 and can produce the balanced wall climbing vehicle 2 and the suction of gravity of wall 2 can be produced on the bridge wall, make the best of the unmanned aerial vehicle 2 rotate the bridge and can obtain the image when the bridge wall of the bridge can be rotated by two, the suction face of bridge 2 can be obtained.
It can be appreciated that the first preset path may be configured as an S-shaped track along an upper-lower or lower-upper or left-right or right-left direction, so as to fully detect the region to be detected.
When the fog type is advection fog/evaporation fog, because it mainly forms in the low department of valley, when waiting to detect regional part in the fog district, advection fog/evaporation fog mainly covers the lower extreme in waiting to detect regional, for the accuracy that improves unmanned aerial vehicle 1 landing point on the bridge wall, avoid appearing bumping the machine accident, set up to start unmanned aerial vehicle 1, and drive wall climbing car 2 through unmanned aerial vehicle 1 and remove to the top in fog district, then through the gesture of adjustment unmanned aerial vehicle 1, make unmanned aerial vehicle 1 can drive wall climbing car 2 and remove to the direction that is close to the bridge wall, when wall climbing car 2 contacts the bridge wall, start wall climbing car 2, and advance on the bridge wall according to the second preset route, discern the crack on the bridge through crack identification module simultaneously.
It is understood that the second preset path may be configured as an S-shaped track along an upper-lower or lower-upper or left-right or right-left direction, so as to fully detect the region to be detected.
When the type of fog is the valley fog, because its fashioned position is indefinite, when waiting to detect regional part in the fog district, in order to improve the accuracy of unmanned aerial vehicle 1 landing point on the bridge wall, avoid appearing bumping the machine accident, set up to start unmanned aerial vehicle 1, and drive wall climbing car 2 through unmanned aerial vehicle 1 and remove to non-fog district, then through the gesture of adjustment unmanned aerial vehicle 1, make unmanned aerial vehicle 1 can drive wall climbing car 2 and remove to the direction that is close to the bridge wall, when wall climbing car 2 contacts the bridge wall, start wall climbing car 2, and advance on the bridge wall according to the second preset path, discern the crack on the bridge through crack recognition module simultaneously.
It can be appreciated that the third preset path may be configured as an S-shaped track along an upper-lower or lower-upper or left-right or right-left direction, so as to fully detect the region to be detected.
In other embodiments, as shown in fig. 3, the environment recognition module is simultaneously used for recognizing the type of the bridge wall surface, and as shown in fig. 2, the type of the bridge wall surface at least comprises one or a combination of at least two of a straight surface 41, an inclined surface 42 and an arc surface 43;
the controller is configured to detect according to a second detection program when the area to be detected is not in the fog area, and specifically comprises the following steps:
When the type of the bridge wall surface is the straight surface 41, starting the unmanned aerial vehicle 1, flying according to a fourth preset path, and identifying the crack on the bridge through a crack identification module;
when the type of the bridge wall surface is the combination of at least two of the inclined surface 42, the cambered surface 43 or the straight surface 41, the inclined surface 42 and the cambered surface 43, the unmanned aerial vehicle 1 is started and moves to the area to be detected, when the wall climbing vehicle 2 contacts the bridge wall surface, the wall climbing vehicle 2 is started and moves on the bridge wall surface according to a fifth preset path, and meanwhile, the crack on the bridge is identified through the crack identification module.
In this embodiment, when the type of the bridge wall surface is the straight surface 41, the distance between the second camera and the bridge wall surface can be kept unchanged by simply controlling the unmanned aerial vehicle 1, so that on one hand, when the sampling range is too large, the sampling precision is reduced due to the fact that the tiny cracks are easily ignored, and on the other hand, the detection efficiency is reduced due to the fact that the sampling range is too small.
It is understood that the second preset path may be configured as an S-shaped track along a top-to-bottom or top-to-bottom path, so as to fully detect the area to be detected.
More specifically, when the type of the bridge wall surface is the combination of at least two of the inclined surface 42, the cambered surface 43 or the straight surface 41, the inclined surface 42 and the cambered surface 43, the distance between the second camera and the bridge wall surface is kept unchanged, the flight track of the unmanned aerial vehicle 1 is complex, so that the problems of missed detection and repeated detection are easy to occur, collision accidents are easy to occur, the unmanned aerial vehicle 1 is started, the unmanned aerial vehicle 1 is driven to move to the area to be detected, then the unmanned aerial vehicle 1 is enabled to drive the wall climbing vehicle 2 to move towards the direction close to the bridge wall surface by adjusting the gesture of the unmanned aerial vehicle 1, when the wall climbing vehicle 2 contacts the bridge wall surface, the wall climbing vehicle 2 is started and moves on the bridge wall surface according to a fifth preset path, and meanwhile, cracks on the bridge are identified through the crack identification module.
It is understood that the fifth preset path may be configured as an S-shaped track along an upper-lower or lower-upper or left-right or right-left direction, so as to fully detect the region to be detected.
In a further embodiment, the area to be detected is provided to include the side wall surface, the bottom surface of abutment 3 and the surface of pier 4.
In other embodiments, when the rotation speed of the rotor wing of the unmanned aerial vehicle 1 is unchanged and the concentration of fog is increased, on one hand, the suction force generated by the rotor wing of the unmanned aerial vehicle 1 is limited, so that fog between the wall climbing vehicle 2 and the bridge wall surface cannot be sucked completely, a second camera cannot acquire clear bridge images, the accuracy of crack data is affected, on the other hand, the bridge wall surface is more moist, and further the wall climbing vehicle 2 is easy to slip in the travelling process, and the detection efficiency is affected;
A controller, further configured to:
When the wall climbing vehicle 2 runs on the bridge, the rotating speed of the rotor wing on the unmanned aerial vehicle 1 is adjusted in a direct proportion according to the concentration of the fog.
Specifically, in this embodiment, the first acquisition module may be set as a haze meter and connected to the controller in a wired manner, where the haze meter measures optical transmittance, the lower the optical transmittance is, the more dense the haze is, the lower the visibility is, and when the concentration of the haze represented by the haze meter is increased, the rotation speed of the rotor wing on the unmanned aerial vehicle 1 is increased in a proportional manner through the controller, so that on one hand, the rotor wing of the unmanned aerial vehicle 1 can generate enough suction force to suck the haze between the wall climbing vehicle 2 and the bridge wall surface completely, thereby avoiding affecting the definition of the bridge image acquired by the second camera, and on the other hand, the wall climbing vehicle 2 and the bridge wall surface have larger contact force, and avoiding skidding.
In other embodiments, as shown in fig. 3, the environment recognition module is used to recognize the type of the bridge wall surface, where the type of the bridge wall surface at least includes one or a combination of at least two of a straight surface 41, an inclined surface 42 and an arc surface 43;
The controller is configured to detect in a second detection mode if the environmental parameter around the bridge belongs to a second target type, and specifically includes:
when the type of the bridge wall surface is the straight surface 41, starting the unmanned aerial vehicle 1, flying according to a sixth preset path, and identifying the crack on the bridge through a crack identification module;
When the type of the bridge wall surface is the combination of at least two of the inclined surface 42, the cambered surface 43 or the straight surface 41, the inclined surface 42 and the cambered surface 43, the unmanned aerial vehicle 1 is started and moves to the area to be detected, when the wall climbing vehicle 2 contacts the bridge wall surface, the wall climbing vehicle 2 is started and moves on the bridge wall surface according to a seventh preset path, and meanwhile, the crack on the bridge is identified through the crack identification module.
In this embodiment, when the type of the bridge wall surface is the straight surface 41, the distance between the second camera and the bridge wall surface can be kept unchanged by simply controlling the unmanned aerial vehicle 1, so that on one hand, when the sampling range is too large, the sampling precision is reduced due to the fact that the tiny cracks are easily ignored, and on the other hand, the detection efficiency is reduced due to the fact that the sampling range is too small.
It is understood that the sixth preset path may be configured as an S-shaped track along a top-to-bottom or bottom-to-top path, so as to fully detect the area to be detected.
More specifically, when the type of the bridge wall surface is the combination of at least two of the inclined surface 42, the cambered surface 43 or the straight surface 41, the inclined surface 42 and the cambered surface 43, the distance between the second camera and the bridge wall surface is kept unchanged, the flight track of the unmanned aerial vehicle 1 is complex, so that the problems of missed detection and repeated detection are easy to occur, and the collision accident is easy to occur, in order to avoid the occurrence of the problems, the unmanned aerial vehicle 1 is set to be started, the unmanned aerial vehicle 1 is used for driving the wall climbing vehicle 2 to move to the area to be detected, and then the attitude of the unmanned aerial vehicle 1 is adjusted, so that the unmanned aerial vehicle 1 can drive the wall climbing vehicle 2 to move towards the direction close to the bridge wall surface, when the wall climbing vehicle 2 contacts the bridge wall surface, the wall climbing vehicle 2 is started and advances on the bridge wall surface according to a seventh preset path, and meanwhile, cracks on the bridge are identified through the crack identification module.
It is understood that the seventh preset path may be set to follow an S-shaped trajectory of up and down or down and up or left and right or right and left, so that the region to be detected can be sufficiently detected.
In other embodiments, as shown in FIG. 4, the fracture data is configured to include the length and width of the fracture;
A controller, further configured to:
When the length of the crack is greater than or equal to the preset length and the width of the crack is greater than or equal to the first preset width, marking the crack as a first repair echelon;
When the length of the crack is greater than or equal to the preset length and the width of the crack is smaller than the first preset width, or the length of the crack is smaller than the preset length and the width of the crack is greater than or equal to the first preset width, marking the crack as a second repair echelon;
And marking the crack as a third repair echelon when the length of the crack is smaller than the preset length and the width of the crack is smaller than the first preset width.
In this embodiment, the preset length and the first preset width are both set to be the set widths, and may be changed according to actual needs, and the preset length may be set to be 50cm, and the first preset width may be set to be 5mm, where when the length of the crack is greater than or equal to 50cm and the width of the crack is greater than or equal to 5mm, the condition of the crack is indicated to be very serious, in order to avoid further expansion of the crack due to self factors and environmental factors, the crack is marked as a first repair team, when the length of the crack is greater than or equal to 50cm and the width of the crack is less than 5mm, or the length of the crack is less than 50cm and the width of the crack is greater than or equal to 5mm, the condition of the crack is indicated to be a second repair team, and when the length of the crack is less than 50cm and the width of the crack is less than or equal to 5mm, the condition of the crack is indicated to be generally, in order to avoid further expansion of the crack due to self factors and environmental factors, the crack is marked as a third repair team, and repair personnel can repair the first repair team and repair personnel in time according to the first repair team and second repair team.
In a further embodiment, as shown in fig. 4, the controller is further configured to:
When the crack belongs to the first repair echelon or the second repair echelon or the third repair echelon and the width of the crack is smaller than the second preset width, marking the crack as needing to be widened;
wherein the second preset width is smaller than the first preset width.
In this embodiment, the second preset width is set to be a set width and can be changed according to actual requirements, and exemplarily, the second preset width can be set to be 2mm, when the width of the crack is smaller than 2mm, the filler is difficult to fill into the crack, so as to avoid influencing repair work, and the crack is marked in a detection stage, so that a widened tool is brought in advance during subsequent maintenance, and the maintenance efficiency is avoided being influenced.
In other embodiments, as shown in fig. 4, the fracture data is set to also include the type of fracture;
A controller, further configured to:
when the crack belongs to the first repair echelon, the second repair echelon or the third repair echelon and the type of the crack is an active crack, marking the active crack as repairing by using a flexible material;
When the crack belongs to the first repair ladder or the second repair ladder or the third repair ladder and the type of crack is a static crack, marking the movable crack as repaired using a rigid material.
In particular, in this embodiment, whether the current crack is a movable crack or a static crack can be determined by comparing with the historical data, and in the case of the movable crack, since the current crack may continue to expand, in order to avoid affecting the repair effect, the crack is marked in a detection stage so as to be ready for flexible material in advance to adapt to deformation of the structure in the subsequent maintenance, wherein the flexible material may be set to include polyurethane foam or elastic epoxy resin, and in the case of the static crack, in order to avoid affecting the repair effect, the crack is marked in a detection stage so as to be ready for rigid material in advance in the subsequent maintenance, wherein the rigid material may be set to include epoxy resin or cement mortar.
The technical features of the above embodiments may be arbitrarily combined, and all possible combinations of the technical features in the above embodiments are not described for brevity of description, however, as long as there is no contradiction between the combinations of the technical features, they should be considered as the scope of the description.
The above examples merely represent a few embodiments of the present invention, which are described in more detail and are not to be construed as limiting the scope of the present invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention.
Claims (7)
1. A construction crack detection apparatus, comprising:
Unmanned plane;
the wall climbing vehicle is arranged at the top of the unmanned aerial vehicle;
the environment recognition module is used for acquiring environment parameters around the bridge;
The crack identification module is used for acquiring bridge crack data;
a controller configured to:
If the environmental parameters around the bridge are of the first target type, detecting in a first detection mode;
if the environmental parameters around the bridge are of the second target type, detecting in a second detection mode;
The environmental parameters of the surrounding of the bridge at least comprise weather information;
the environment recognition module is used for recognizing the type of fog at the same time;
The controller is configured to detect in a first detection mode if an environmental parameter around the bridge is of a first target type, and specifically includes:
the first target type is fog, and when the part of the area to be detected is in the fog area, the detection is carried out according to a first detection program according to the fog type;
when the area to be detected is not in the fog area, detecting according to a second detection program;
Wherein the fog types at least comprise uphill fog, valley fog, advection fog and evaporation fog;
the controller is configured to detect according to a first detection program according to the type of fog when the area to be detected is partially in the fog area, and specifically comprises:
when the type of the fog is uphill fog, starting the unmanned aerial vehicle, moving to the lower part of a fog area, and when the wall climbing vehicle contacts the bridge wall surface, starting the wall climbing vehicle, advancing on the bridge wall surface according to a first preset path, and identifying cracks on the bridge through the crack identification module;
When the fog is advection fog/evaporation fog, starting the unmanned aerial vehicle, moving the unmanned aerial vehicle to the upper part of a fog area, starting the wall climbing vehicle when the wall climbing vehicle contacts the bridge wall surface, advancing the wall climbing vehicle on the bridge wall surface according to a second preset path, and identifying cracks on the bridge through the crack identification module;
When the fog is valley fog, starting the unmanned aerial vehicle, moving to a non-fog area, and when the wall climbing vehicle contacts the bridge wall surface, starting the wall climbing vehicle, advancing on the bridge wall surface according to a third preset path, and identifying cracks on the bridge through the crack identification module;
the environment recognition module is used for recognizing the type of the bridge wall surface at the same time, wherein the type of the bridge wall surface comprises one or the combination of any two or three of a straight surface, an inclined surface and an arc surface;
The controller is configured to detect in a second detection mode if the environmental parameter around the bridge is of a second target type, and specifically includes:
when the type of the bridge wall surface is a straight surface, starting the unmanned aerial vehicle, flying according to a sixth preset path, and identifying the crack on the bridge through the crack identification module;
When the type of the bridge wall surface is an inclined surface, an arc surface or a combination of at least two of a straight surface, an inclined surface and an arc surface, starting the unmanned aerial vehicle, moving to a region to be detected, when the wall climbing vehicle contacts the bridge wall surface, starting the wall climbing vehicle, advancing on the bridge wall surface according to a seventh preset path, and identifying a crack on the bridge through the crack identification module.
2. The building crack detection device according to claim 1, wherein the environment recognition module is simultaneously used for recognizing the type of the bridge wall surface, and the type of the bridge wall surface comprises one or a combination of any two or three of a straight surface, an inclined surface and an arc surface;
The controller is configured to detect according to a second detection program when the area to be detected is not in the fog area, and specifically includes:
When the type of the bridge wall surface is a straight surface, starting the unmanned aerial vehicle, flying according to a fourth preset path, and identifying the crack on the bridge through the crack identification module;
When the type of the bridge wall surface is an inclined surface, an arc surface or a combination of at least two of a straight surface, an inclined surface and an arc surface, starting the unmanned aerial vehicle, moving to the area to be detected, when the wall climbing vehicle contacts the bridge wall surface, starting the wall climbing vehicle, advancing on the bridge wall surface according to a fifth preset path, and identifying a crack on the bridge through the crack identification module.
3. The construction crack detection apparatus according to claim 2, wherein the area to be detected includes a side wall surface, a bottom surface, and a surface of the bridge pier.
4. The construction crack detection apparatus according to claim 1 or 2, further comprising a first acquisition module for acquiring a concentration of mist;
the controller is further configured to:
when the wall climbing vehicle advances on the bridge, the rotating speed of the rotor wing on the unmanned aerial vehicle is adjusted in a proportional mode according to the concentration of the fog.
5. The construction crack detection device according to claim 1, wherein the crack data comprises a length and a width of a crack;
the controller is further configured to:
When the length of the crack is greater than or equal to a preset length and the width of the crack is greater than or equal to a first preset width, marking the crack as a first repair echelon;
when the length of the crack is greater than or equal to the preset length and the width of the crack is smaller than the first preset width, or the length of the crack is smaller than the preset length and the width of the crack is greater than or equal to the first preset width, marking the crack as a second repair echelon;
and marking the crack as a third repair ladder when the length of the crack is smaller than the preset length and the width of the crack is smaller than the first preset width.
6. The building crack detection device of claim 5, wherein the controller is further configured to:
When the crack belongs to the first repair echelon or the second repair echelon or the third repair echelon and the width of the crack is smaller than the second preset width, marking the crack as needing to be widened;
wherein the second preset width is smaller than the first preset width.
7. The construction crack detection device of claim 5, wherein the crack data further comprises a type of crack;
the controller is further configured to:
When the crack belongs to a first repair echelon or a second repair echelon or a third repair echelon and the type of the crack is an active crack, marking the active crack as repairing by using a flexible material;
When the crack belongs to the first repair ladder or the second repair ladder or the third repair ladder and the type of the crack is a static crack, marking the movable crack as repaired using a rigid material.
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| CN116972852A (en) * | 2023-08-02 | 2023-10-31 | 河南水利与环境职业学院 | Bridge health monitoring method |
| CN117928487A (en) * | 2023-12-27 | 2024-04-26 | 浙江工业大学 | Structural crack detection method based on machine vision and human-computer interaction |
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| EP3352129B1 (en) * | 2015-09-16 | 2019-10-02 | FUJIFILM Corporation | Soundness determination device, soundness determination method and soundness determination program |
| US10101196B2 (en) * | 2016-02-17 | 2018-10-16 | Qualcomm Incorporated | Device for UAV detection and identification |
| CN108051450B (en) * | 2017-11-01 | 2020-08-25 | 中铁大桥科学研究院有限公司 | Bridge health detection system and method based on unmanned aerial vehicle |
| KR102261323B1 (en) * | 2019-06-14 | 2021-06-07 | 주식회사 한국건설방재연구원 | Safety Inspection Check Method of Structure through Artificial Intelligence Analysis of Scan Image by Drone |
| US11151713B2 (en) * | 2019-09-18 | 2021-10-19 | Wipro Limited | Method and system for detection of anomalies in surfaces |
| US11656629B1 (en) * | 2020-12-08 | 2023-05-23 | Waymo Llc | Detection of particulate matter in autonomous vehicle applications |
| KR102456987B1 (en) * | 2022-03-15 | 2022-10-20 | 디엘이앤씨 주식회사 | Method for detecting crack of exterior wall |
| CN116485709A (en) * | 2023-02-01 | 2023-07-25 | 武汉科技大学 | A Bridge Concrete Crack Detection Method Based on YOLOv5 Improved Algorithm |
| CN117635921A (en) * | 2023-12-06 | 2024-03-01 | 中铁四局集团有限公司 | Bridge crack identification method and system based on image analysis |
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| CN116972852A (en) * | 2023-08-02 | 2023-10-31 | 河南水利与环境职业学院 | Bridge health monitoring method |
| CN117928487A (en) * | 2023-12-27 | 2024-04-26 | 浙江工业大学 | Structural crack detection method based on machine vision and human-computer interaction |
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