CN216669780U - Steel aqueduct defect detecting and early warning system based on unmanned aerial vehicle image recognition - Google Patents
Steel aqueduct defect detecting and early warning system based on unmanned aerial vehicle image recognition Download PDFInfo
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Abstract
The utility model discloses a steel aqueduct defect detecting and early warning system based on unmanned aerial vehicle image recognition, which comprises an image acquisition and transmission module, an image recognition and analysis module and an early warning module. The image acquisition and transmission module comprises an unmanned aerial vehicle, a high-definition camera and wireless communication equipment; the image recognition and analysis module mainly comprises image recognition software, an image database and the like and is used for recognizing the defect form of the steel aqueduct and positioning the defect position; the early warning module is composed of a warning lamp, a warning sound, a mobile phone warning, e-mail triggering software and other warning modes and is used for timely warning detection personnel. The method and the device have the advantages that the steel aqueduct is patrolled and examined by the unmanned aerial vehicle according to the preset flight route, the image information of the key part of the steel aqueduct is collected in real time, the collected image information is processed and analyzed, and the defect position and the distribution condition which possibly exist in the steel aqueduct are rapidly identified and early warned.
Description
Technical Field
The utility model relates to the technical field of aqueduct quality detection, and particularly can be applied to the fields of rapid detection and intelligent management of large-scale steel aqueduct structures.
Background
At present, the types of the large aqueducts in China mainly comprise simply supported prestressed aqueducts, continuous rigid frame aqueducts, arch-type aqueducts and the like, and the structures of the aqueducts are basically the same as that of bridges. For a large-scale steel aqueduct, the whole structure is complex, and under the long-term action of environmental erosion, material aging and water load, the damage and accumulation of the structure can be caused, and catastrophic accidents can be caused under extreme conditions. Therefore, the method is indispensable to the detection of structural defects of the large steel aqueduct. In a new detection technology, a manual detection method is abandoned for detecting the structural defects of the large steel aqueduct based on an image recognition technology, and the structural defects of the steel aqueduct can be more accurately and rapidly measured by adopting machine vision and an image recognition analysis technology.
In addition, the introduction of the remote sensing system of the unmanned aerial vehicle promotes the development and the progress of the bridge rapid detection technology, the body of the unmanned aerial vehicle is small in size, the flight angle can be flexibly changed according to actual conditions, and diversified requirements such as remote shooting and local shooting are met. Meanwhile, the unmanned aerial vehicle remote sensing system can realize remote data transmission, related data can be stored in the machine body, the condition of data attenuation caused by long-distance transmission is avoided, the monitoring accuracy is guaranteed, and the probability of safety accidents is reduced.
SUMMERY OF THE UTILITY MODEL
In order to overcome the defects of the existing steel aqueduct defect damage identification, the utility model provides a novel large-scale steel aqueduct defect detection and early warning system which is simple, convenient, applicable, accurate, efficient and novel. By combining the unmanned aerial vehicle remote sensing system with the image recognition technology, a set of integrated rapid detection technology integrating image acquisition, image information processing, early warning and the like is formed, and the defects of high cost, low efficiency and the like of manual detection are overcome.
A steel aqueduct defect detecting and early warning system based on unmanned aerial vehicle image recognition comprises an image acquisition and transmission module, an image recognition and analysis module and an early warning module;
the image acquisition and transmission module comprises an unmanned aerial vehicle remote sensing system consisting of an unmanned aerial vehicle system, a waypoint tracking system, a holder detection system and a ground station system; the unmanned aerial vehicle remote sensing system is used for realizing image acquisition, image transmission, image storage and image removal;
the unmanned aerial vehicle system comprises an unmanned aerial vehicle, a high-definition camera and wireless communication equipment; the unmanned aerial vehicle realizes positioning cruise through the unmanned aerial vehicle remote sensing system, and the high-definition camera is installed on the unmanned aerial vehicle and is connected with the image recognition and analysis module through wireless communication equipment;
the image recognition and analysis module comprises an image recognition system and an image database; the image recognition system is connected with the wireless communication equipment, and processes, recognizes and analyzes the image after receiving the image information transmitted in real time; the image database is connected with the image recognition system and used for storing image analysis results and original image information;
the early warning module comprises an alarm lamp, an alarm sound, a mobile phone alarm and an electronic mail triggering alarm system, and is connected with the image recognition and analysis module.
Furthermore, a plurality of unmanned aerial vehicles are arranged according to a fixed air route, and each unmanned aerial vehicle corresponds to a fixed aqueduct number section.
Further, at least 2 high definition digtal cameras are installed below the unmanned aerial vehicle, and the high definition digtal cameras are of a rotary type.
Further, in the image recognition and analysis module, the image recognition system includes: the image processing device comprises an image obtaining and displaying module, an image restoration module, an image enhancement module, an image segmentation module, an image analysis module and an image compression coding module.
Further, in the image recognition and analysis module, the image database is composed of a plurality of collected sample pictures in the previous period.
The utility model discloses a non-contact nondestructive testing method based on a steel aqueduct structure defect rapid detection and early warning technology of unmanned aerial vehicle image recognition, which has the basic principle that an unmanned aerial vehicle is used for carrying out routing inspection on a steel aqueduct according to a preset flight route, image information of key parts of the steel aqueduct is collected in real time, the collected image information is processed and analyzed, and the position and distribution condition of defects possibly existing in the steel aqueduct are rapidly recognized and early warned.
Compared with the prior art, the utility model has the following advantages:
(1) high efficiency and high safety. Unmanned aerial vehicle can reach some dangerous and artifical positions that can not reach, can carry out repeated sampling to key detail part, has saved detection time greatly, has also reduced the safety risk in the testing process simultaneously.
(2) The flexibility is high. The detection scheme of the unmanned aerial vehicle is flexible and variable, can be modified at the background, has various alternative schemes, and can be adjusted in a targeted manner according to the actual situation of the scene.
(3) The cost is low. The unmanned aerial vehicle detection does not need to invest a large amount of manpower and material resources, and a flight team can finish the acquisition of the structural defect picture.
The utility model can be suitable for the fields of rapid detection and intelligent management of large-scale steel aqueduct structures.
Drawings
FIG. 1 is a schematic diagram of unmanned aerial vehicle inspection and image transmission;
FIG. 2 is a schematic diagram of a composition structure of an unmanned aerial vehicle remote sensing system;
FIG. 3 is a schematic diagram of image recognition and processing;
FIG. 4 is a schematic diagram of rapid detection and early warning of steel aqueduct defects in a specific embodiment;
FIG. 5 is a schematic diagram of an exemplary embodiment of an image capture system;
fig. 6 is a schematic view of a cruising route of an unmanned aerial vehicle;
FIG. 7 is a flowchart of an image recognition and analysis process.
Detailed Description
The following describes in detail specific embodiments of the present invention. It should be understood that the detailed description and specific examples, while indicating the present invention, are given by way of illustration and explanation only, not limitation.
The large-scale steel aqueduct defect detecting and early warning system mainly comprises three modules: the device comprises an image acquisition and transmission module, an image identification and analysis module and an early warning module.
In a specific embodiment, the image acquisition and transmission module consists of an unmanned aerial vehicle, a high-definition camera and high-definition wireless image transmission equipment; the image recognition and analysis module mainly comprises image recognition software and an image database and is used for recognizing the defect form of the steel aqueduct and positioning the defect position; the early warning module is composed of a warning lamp, a warning sound, a mobile phone warning, e-mail triggering software and other warning modes and is used for timely warning detection personnel.
In the image acquisition and transmission module, the unmanned aerial vehicle rapid inspection system is composed of an unmanned aerial vehicle remote sensing system, a wireless communication link and an image identification system, and the transmission process of the image is mainly realized through the wireless communication link. The schematic diagram of unmanned aerial vehicle inspection and image transmission is shown in fig. 1.
The unmanned aerial vehicle remote sensing system mainly completes four works of image acquisition, image transmission, image storage and image clearing. The wireless communication link realizes automatic acquisition of the routing inspection data of the large steel aqueduct through a 4G or WiFi wireless private network communication device suitable for real-time transmission of the unmanned aerial vehicle. And after receiving the image information transmitted in real time, the image recognition system realizes the display of the real-time image of the steel aqueduct and processes, recognizes and analyzes the image. Meanwhile, the analysis result and the original image are stored and backed up.
As shown in fig. 2, the unmanned aerial vehicle remote sensing system comprises an unmanned aerial vehicle system, a waypoint tracking system, a holder detection system and a ground station system.
The unmanned aerial vehicle system provides carriers for image acquisition equipment and the like, and the stability and the cruising ability of the unmanned aerial vehicle system ensure the feasibility of detection.
The waypoint tracking system mainly controls the unmanned aerial vehicle to accurately reach a target position, and if the unmanned aerial vehicle deviates from a steel aqueduct by a large target angle, the detection system cannot accurately position the steel aqueduct.
The holder detection system is a key foundation for completing autonomous detection, and can automatically identify the truss and the nodes of the steel aqueduct by means of the holder detection system and acquire picture data at each position.
The main functions of the ground station system are to detect the unmanned aerial vehicle status and to transmit image information. In addition, the ground station can send control command to unmanned aerial vehicle.
In the image acquisition and transmission module, in the image acquisition process, the unmanned aerial vehicle cruises and shoots every 1h according to a fixed air route, and each unmanned aerial vehicle corresponds to a fixed aqueduct number section and numbers the camera. Specifically, the number of the cameras can be halved by using the rotary cameras, and meanwhile, the rotating angle can be manually adjusted to perform accurate positioning.
In an embodiment, the image recognition and analysis system comprises image recognition software in a computer and a data base, wherein the data base comprises a plurality of sample pictures collected in a previous period.
As shown in fig. 3, the image recognition and processing process includes: image acquisition and display, image restoration, image enhancement, image segmentation, image analysis and image compression coding. And comparing the image obtained by analysis with a sample picture in a database, and starting an early warning report system if the difference is larger than the information of the sample picture.
Specifically, the image is converted into digital information according to information such as probability density, size, color and the like of pixels in the image; then inputting the digital information into a computer for calculation and analysis to obtain the detailed characteristic data of the image, such as: area, length, etc.; and finally, obtaining a result by judging the set threshold value and adding other factors, and outputting information such as size, angle, offset and the like, thereby achieving the purpose of identifying unknown image target characteristics by a machine. Comparing the image information with the image information in the data bank, and if the error exceeds the set limit value, automatically starting an early warning report system.
The image recognition and processing mainly processes the dot matrix data representing the image through a computer, so that the digital image processing has the following characteristics:
(1) the potential of information screening in the digital image is large;
(2) the reproducibility is good;
(3) the processing precision is high;
(4) the application range is wide;
(5) the flexibility is high;
(6) the related subject field is wide.
In a specific embodiment, the early warning module is composed of a plurality of alarm forms such as an alarm lamp, an alarm sound, a mobile phone alarm and e-mail triggering software, and is used for locating the position of the structural defect to the detection personnel in time so as to ensure that the detection personnel can check the defect condition of the transition groove section at the corresponding position of the image in time and take maintenance measures.
In the utility model, an unmanned aerial vehicle remote sensing system and an image recognition technology are combined and applied to the structural defect detection of the large-sized steel aqueduct. The method is simple and easy to implement, saves manpower, and can realize real-time alarm. The application provides a new technology for guaranteeing the safe operation and maintenance of the aqueduct structure. Detailed Description
The utility model relates to a steel aqueduct structure defect rapid detection and early warning technology based on unmanned aerial vehicle image recognition, which is a non-contact nondestructive detection method.
The specific implementation steps of the steel aqueduct structure defect rapid detection and early warning method based on unmanned aerial vehicle image recognition are divided into three parts: image acquisition and transmission, image recognition and analysis and early warning report.
The schematic diagram of the steel aqueduct defect rapid detection and early warning in the specific embodiment is shown in fig. 4.
Step 1: image acquisition and transmission
The image acquisition system consists of an unmanned aerial vehicle platform, image acquisition equipment and a ground station, as shown in fig. 5.
The unmanned aerial vehicle adopts a fixed wing unmanned aerial vehicle, and the maximum takeoff weight is 12 kg. The cruising speed of the unmanned aerial vehicle is 20m/s, the cruising time reaches 1.5h, the highest flight altitude is 3000m, the voyage is long, the wind resistance is strong, the task load reaches 2kg, and the unmanned aerial vehicle is suitable for large-span structure detection work. The flight control part comprises a rudder, an aileron and an elevator, and is driven by an electric steering engine. The unmanned aerial vehicle is connected with the ground station by utilizing a PID control method and a communication interface based on a WiFi wireless communication mode. The ground station plans the flight path of the unmanned aerial vehicle and is used for setting flight parameters and displaying the flight state.
Image acquisition equipment is 2 image sensor of unmanned aerial vehicle below mounting: one part is arranged in front, and the shooting visual angle is 45 degrees with the vertical direction; the other part is arranged right below and shoots along the vertical direction. The lens of the image sensor has a diameter of 35mm, 4200 ten thousand pixels, and a resolution of 7952 × 5304. The image acquisition card can convert the signals obtained by the sensor into images in a JEPG format.
The unmanned plane performs cruise shooting around the aqueduct along fixed route timing, and the schematic diagram of the cruise route is shown in fig. 6.
Step 2: image recognition and analysis
Before image analysis, a database of the aqueduct project is established, and the unmanned aerial vehicle system is operated to take pictures and sample the complete aqueduct according to a preset air route. And carrying out a series of operations of restoration, enhancement, segmentation, analysis and compression coding on the obtained sample image to obtain a sample data base of the steel aqueduct structure.
In the working process of the unmanned aerial vehicle, the unmanned aerial vehicle acquires images transmitted to a computer, image restoration and image enhancement are firstly carried out on the images, and due to factors such as poor focusing and environmental influence, the original images may have defects. For common image degradation, an image restoration technology can be adopted to process and repair the image degradation; if the visual image of the image needs to be completely changed and important features are highlighted, an image enhancement technology needs to be adopted to visually improve the image quality.
After the image is processed, the defect parts in the image can be analyzed and detected to obtain attribute characteristic information data of the defect parts, and the description of the image is established. And finally, compressing the image information and encoding the attribute characteristic information data. The main object is to make data easy to store and flow without spoiling image quality, thereby solving the problem of large storage volume.
Comparing the image information with the image information in the data bank, and if the error exceeds the set limit value, automatically starting an early warning report system. And meanwhile, the defects in the image are further analyzed and distinguished, and the positions of the defects are determined through the coded information, so that a more accurate defect identification result is obtained. The image can be calculated by a boundary tracking method to obtain defect information, and after the defect image is coded, the corresponding defect pixel points are interpolated and coded to obtain the accurate positioning of the defect.
And step 3: early warning report
And calling an early warning report module, wherein the early warning report module is composed of a plurality of warning forms such as a warning lamp, a warning sound, a mobile phone warning and an e-mail triggering software, and is used for positioning the structure defect position to the detection personnel in time so as to ensure that the detection personnel can check the defect condition of the transition groove section at the corresponding position of the image in time and take maintenance measures.
A flowchart of the image recognition and analysis process is shown in fig. 7.
The foregoing shows and describes the general principles, essential features, and advantages of the utility model. It will be understood by those skilled in the art that the present invention is not limited to the embodiments described above, which are merely illustrative of the principles of the utility model, but that various changes and modifications may be made without departing from the spirit and scope of the utility model, which fall within the scope of the utility model as claimed. The scope of the utility model is defined by the appended claims and equivalents thereof.
Claims (5)
1. A steel aqueduct defect detecting and early warning system based on unmanned aerial vehicle image recognition is characterized by comprising an image acquisition and transmission module, an image recognition and analysis module and an early warning module;
the image acquisition and transmission module comprises an unmanned aerial vehicle remote sensing system consisting of an unmanned aerial vehicle system, a waypoint tracking system, a holder detection system and a ground station system; the unmanned aerial vehicle remote sensing system is used for realizing image acquisition, image transmission, image storage and image removal;
the unmanned aerial vehicle system comprises an unmanned aerial vehicle, a high-definition camera and wireless communication equipment; the unmanned aerial vehicle realizes positioning cruise through the unmanned aerial vehicle remote sensing system, and the high-definition camera is installed on the unmanned aerial vehicle and is connected with the image recognition and analysis module through wireless communication equipment;
the image recognition and analysis module comprises an image recognition system and an image database; the image recognition system is connected with the wireless communication equipment, and processes, recognizes and analyzes the image after receiving the image information transmitted in real time; the image database is connected with the image recognition system and used for storing image analysis results and original image information;
the early warning module comprises an alarm lamp, an alarm sound, a mobile phone alarm and an electronic mail triggering alarm system, and is connected with the image recognition and analysis module.
2. The system of claim 1, wherein a plurality of unmanned aerial vehicles are arranged according to a fixed route, and each unmanned aerial vehicle corresponds to a fixed aqueduct number segment.
3. The system of claim 2, wherein at least 2 high-definition cameras are mounted below the unmanned aerial vehicle, and the high-definition cameras are rotary.
4. The system for detecting and warning the defects of the steel aqueduct on the basis of unmanned aerial vehicle image recognition according to claim 1, wherein in the image recognition and analysis module, the image recognition system comprises: the image processing device comprises an image obtaining and displaying module, an image restoration module, an image enhancement module, an image segmentation module, an image analysis module and an image compression coding module.
5. The system of claim 1, wherein in the image recognition and analysis module, the image database comprises a plurality of previously collected sample pictures.
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