WO2021056630A1 - 输电线路铁塔结构缺陷检测方法及装置 - Google Patents

输电线路铁塔结构缺陷检测方法及装置 Download PDF

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WO2021056630A1
WO2021056630A1 PCT/CN2019/111373 CN2019111373W WO2021056630A1 WO 2021056630 A1 WO2021056630 A1 WO 2021056630A1 CN 2019111373 W CN2019111373 W CN 2019111373W WO 2021056630 A1 WO2021056630 A1 WO 2021056630A1
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iron tower
tower structure
inspected
defect detection
modal
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PCT/CN2019/111373
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English (en)
French (fr)
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秦源汛
杨文义
何红太
桂菲菲
黄志勇
熊鹏
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北京国网富达科技发展有限责任公司
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Publication of WO2021056630A1 publication Critical patent/WO2021056630A1/zh

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/0002—Inspection of images, e.g. flaw detection
    • G06T7/0004—Industrial image inspection
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/50—Depth or shape recovery
    • G06T7/55—Depth or shape recovery from multiple images
    • G—PHYSICS
    • G01—MEASURING; TESTING
    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/8851—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges
    • G01N2021/8887—Scan or image signal processing specially adapted therefor, e.g. for scan signal adjustment, for detecting different kinds of defects, for compensating for structures, markings, edges based on image processing techniques
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/20—Special algorithmic details
    • G06T2207/20081—Training; Learning

Definitions

  • the invention relates to the technical field of fault detection, in particular to a method and device for detecting structural defects of a transmission line iron tower.
  • Transmission towers play the role of supporting wires, ground wires and other accessories in overhead transmission lines, and are an important part of the power system. Certain minor damages and defects in the transmission tower structure will gradually develop into serious damages and defects with the long-term effects of the load. The damage and defects of the tower structure will directly affect the operation of the power system.
  • the structural defect detection of the transmission tower can improve the safety and reliability of the power system and avoid the occurrence of major power accidents.
  • the transmission tower has the characteristics of high tower body and strong flexibility shared by high towers and long-span structures. It is susceptible to environmental loads such as earthquakes, wind, and floating ice, and is more likely to cause dynamic collapse of the transmission tower structure under extreme conditions and vibration fatigue damage. damage. In the power grid, external forces and nature have the most serious impact on overhead transmission lines. It is not uncommon for transmission line towers to fall. In addition, once a tower falls, it will cause heavy losses to people's lives and property.
  • the detection methods for structural defects of transmission towers mainly include: manual inspection, online monitoring methods of visible light equipment, and detection methods of identification devices with vehicles such as unmanned aerial vehicles.
  • Manual inspection means that inspectors carry binoculars, use vehicles and walk to visually inspect the tower. This method is relatively mature and can detect some of the defects that have been formed or even more serious defects. Sometimes it can be used with skilled inspectors. Some unformed defects are detected, but the shortcomings of this method are also obvious. The labor cost is huge. For some mountainous areas or natural disasters and other emergency areas, manual inspections are often not possible, defects cannot be identified in advance, and taller towers cannot be identified. Defects of multiple blind spots.
  • the on-line monitoring method of visible light equipment does not require patrol personnel to conduct inspections on site and can obtain image data more intensively.
  • each on-line monitoring device of this method can only identify defects in a small area, and the installation cost is huge, making it difficult to install on a large scale .
  • the current drone tower inspections mainly use visible light and image recognition to identify tower defects. This method can only identify local defects such as missing pins, insulator bursts and other non-structural defects, which can cause tower collapses, partial fractures, etc. Large structural mechanical defects cannot be identified.
  • the embodiment of the present invention provides a method for detecting structural defects of a transmission line tower to improve the efficiency and accuracy of the detection of structural defects of a transmission line tower.
  • the method includes:
  • the modal parameters include: resonance peak frequency and modal shape;
  • the steel tower structure defect detection model is pre-trained and generated based on the modal parameter samples of the steel tower structure.
  • the embodiment of the present invention also provides a transmission line tower structure defect detection device, which is used to improve the efficiency and accuracy of the transmission line tower structure defect detection, and the device includes:
  • the image acquisition module is used to acquire multiple frames of images of the structure of the iron tower to be inspected;
  • the three-dimensional dynamic model generation module is used to determine the three-dimensional dynamic model of the iron tower structure to be inspected according to the multi-frame images of the iron tower structure to be inspected;
  • Modal parameter determination module used to determine the modal parameters of the steel tower structure to be tested according to the three-dimensional dynamic model of the steel tower structure to be tested;
  • the defect detection module is used to input the modal parameters of the steel tower structure to be tested into the pre-trained steel tower structure defect detection model, and output the defect type of the steel tower structure to be tested; the steel tower structure defect detection model is pre-trained according to the modal parameter samples of the steel tower structure generate.
  • the embodiment of the present invention also provides a computer device including a memory, a processor, and a computer program stored in the memory and capable of running on the processor, and the processor implements the above-mentioned method when the computer program is executed.
  • the embodiment of the present invention also provides a computer-readable storage medium, and the computer-readable storage medium stores a computer program for executing the above-mentioned method.
  • the technical solution provided by the embodiment of the present invention adopts: collecting multi-frame images of the structure of the iron tower to be inspected, and determining the three-dimensional dynamic model of the structure of the iron tower to be inspected according to the multi-frame images of the structure of the iron tower to be inspected; according to the three-dimensional dynamic model of the structure of the iron tower to be inspected, Determine the modal parameters of the iron tower structure to be inspected; input the modal parameters of the iron tower structure to be inspected into the pre-trained iron tower structure defect detection model, and output the defect type of the iron tower structure to be inspected, which realizes the establishment of the iron tower based on multi-frame images of the iron tower structure
  • the three-dimensional dynamic model of the structure based on machine learning to identify the defects of the tower structure, solves the problem of large-scale structural mechanics defect identification such as the collapse or partial fracture of the tower with a higher tower, and improves the efficiency and accuracy of the detection of the transmission line tower structure. It can be widely used in power systems.
  • FIG. 1 is a schematic flowchart of a method for detecting structural defects of a transmission line tower in an embodiment of the present invention
  • FIG. 2 is a schematic diagram of collecting images of the structure of a transmission line tower to be tested in an embodiment of the present invention
  • Figure 3 (a) is a schematic diagram of an application scenario of an abstract transmission wire model in an embodiment of the present invention
  • Figure 3(b) is a schematic diagram of another application scenario of the abstract transmission wire model in an embodiment of the present invention.
  • Fig. 4(a) is a schematic diagram of calculation results based on a pair of vertical cameras in an embodiment of the present invention
  • Figure 4(b) is a schematic diagram of the calculation result based on the horizontal camera pair in the embodiment of the present invention.
  • Fig. 5 is a schematic structural diagram of a device for detecting structural defects of a transmission line tower in an embodiment of the present invention.
  • an embodiment of the present invention provides a method for detecting structural defects of transmission line towers. As shown in FIG. 1, the method includes:
  • Step 101 Collect multiple frames of images of the structure of the iron tower to be inspected
  • Step 102 Determine the three-dimensional dynamic model of the iron tower structure to be inspected according to the multi-frame images of the iron tower structure to be inspected;
  • Step 103 Determine the modal parameters of the steel tower structure to be tested according to the three-dimensional dynamic model of the steel tower structure to be tested, where the modal parameters include: resonance peak frequency and modal shape;
  • Step 104 Input the modal parameters of the iron tower structure to be inspected into the iron tower structure defect detection model generated by pre-training, and output the defect type of the iron tower structure to be inspected; the iron tower structure defect detection model is pre-trained and generated according to the modal parameter samples of the iron tower structure.
  • the technical solution provided by the embodiment of the present invention is to collect multiple frames of images of the structure of the tower to be inspected, and determine the three-dimensional dynamic model of the structure of the tower to be inspected according to the multi-frame images of the structure of the tower to be inspected;
  • the three-dimensional dynamic model of the structure determines the modal parameters of the tower structure to be inspected;
  • the modal parameters of the tower structure to be inspected are input into the tower structure defect detection model generated by pre-training, and the defect type of the tower structure to be inspected is output, and the structure is based on the tower structure.
  • a three-dimensional dynamic model of the tower structure is established based on the multi-frame images, and the defects of the tower structure are identified based on machine learning, which solves the problem of large-scale structural mechanics defect identification such as the collapse or partial fracture of the tower with a higher tower, and improves the structure of the transmission line tower.
  • the efficiency and accuracy of defect detection can be widely used in power systems.
  • each frame image in the multi-frame images of the tower structure to be inspected collected in step 101, includes multiple sets of differential images with different shooting angles. .
  • step 101 multiple frames of images of the structure of the iron tower to be inspected can be collected by a quadruple camera.
  • the four-eye camera uses four cameras to be arranged parallel to the optical axis to form a matrix array.
  • the multi-frame synchronous acquisition of 4 pictures of the tower structure to be tested can obtain 6 sets of differential images at different angles and at different times. It has fast matching and simple calculations. Features, easy to realize the advantages of chip.
  • step 102 may include:
  • the depth data determine the three-dimensional dynamic model of the tower structure to be inspected.
  • a four-eye camera can be used to perform three-dimensional dynamic modeling of the tower structure of the transmission line, that is, to determine the three-dimensional dynamic model of the tower structure to be inspected.
  • the quadruple camera has efficient anti-noise ability in two mutually perpendicular directions, so that effective detection in all directions can be realized in the entire linear space, even for single-directional objects (such as backgroundless transmission lines, transmission towers, etc.) ) Can also obtain better detection performance.
  • the application scenario of the tower structure to be inspected is also considered (whether the tower structure to be inspected is applied in the horizontal structure scene or the vertical result scene), and the selection is determined according to the application scenario of the tower structure to be inspected Which of the four-eye cameras calculates the depth data based on the data, so that the final three-dimensional dynamic model of the tower structure to be inspected is the most accurate, which further improves the efficiency and accuracy of subsequent tower structure defect detection.
  • Binocular vision is a common basic machine vision technology.
  • Four-eye cameras can be broken down into 6 pairs (upper left-upper right, lower left-lower right, upper left-lower left, upper right-lower right, upper right-lower (Left, upper left-lower right) binocular camera system, this measurement method is most concerned about the difference image data in two mutually perpendicular directions, the following measurement method is mainly for upper left-lower left, lower left-lower
  • the two pairs of cameras on the right illustrate the two mutually perpendicular directions represented by the camera machine.
  • the three camera positions are referred to as top, left, and right.
  • the four cameras are the expansion of these two typical camera pairs, and the projections of the three cameras are obtained.
  • the matrix is:
  • f is the common focal length of all cameras
  • b h and b v represent the baseline (camera distance) of the vertical pair and the horizontal pair respectively; assuming that the homogeneous coordinates of the 3D point [x y z] T projected in different cameras are x , the calculation of x
  • x the calculation of x
  • [ x y z ] T is the coordinate of x projected in different cameras.
  • u and v are the horizontal and vertical coordinates of the two-dimensional coordinate x, respectively.
  • x L and x R are the three-dimensional coordinates of the 3D point X projected to the left and right cameras respectively.
  • x L and x R are the two-dimensional coordinates of the 3D point X projected to the left and right cameras respectively.
  • the depth z of a point can be calculated by both the horizontal parallax of the camera and the vertical parallax of the camera.
  • the above steps perform three-dimensional reconstruction on the same frame of image to obtain a three-dimensional static model of the iron tower structure, and repeat the above steps to perform three-dimensional reconstruction of multiple frames of images to obtain a three-dimensional dynamic model of the iron tower structure.
  • the corresponding epipolar line is located horizontally and vertically in the middle of the other images.
  • the most direct way to find the parallax is to compare the image block of the reference camera image (outlined as a black rectangle in Figure 3(a) and Figure 3(b)) with the potential corresponding position in other images. Compare.
  • the search space can be limited to epipolar positions.
  • Figures 4(a) and 4(b) use the abstract insulator structure to be inspected (the insulator application scene, that is, the horizontal structure scene or the vertical structure scene) to evaluate the impact of camera pairs in different directions.
  • the vertical axis in Figure 4(a) represents the horizontal disparity, and the horizontal axis represents the horizontal baseline; the vertical axis in Figure 4(b) represents the vertical disparity, and the horizontal axis represents the vertical baseline.
  • step 103 may include:
  • the modal parameters of the steel tower structure to be tested are determined.
  • step 103 the modal parameters of the iron tower structure to be tested are determined according to the following steps.
  • Step 1 Calculate the displacement vector of the tower structure to be tested.
  • I 1 ( ⁇ , ⁇ , t), I 2 ( ⁇ , ⁇ , t), I 3 ( ⁇ , ⁇ , t), I can be obtained by 4 different cameras in a frame synchronization manner 4 ( ⁇ , ⁇ , t), where ( ⁇ , ⁇ ) represents the coordinate of the pixel in the two-dimensional picture, and t represents the time coordinate.
  • the visual matching method is used to match the pixels of 4 different images in multiple frames to obtain a three-dimensional dynamic model. Based on the pixel displacement of N matching points in the three-dimensional dynamic model, the distribution of the structure shape of the iron tower to be inspected is calculated:
  • R 3 is a three-dimensional dynamic model.
  • Step 2 Calculate the observation vector of the tower structure to be tested.
  • the third step Calculate the modal parameters of the tower structure to be tested.
  • Input the observation vector of the tower structure to be inspected through the output-only modal parameter estimation method (Output-Only Modal Analysis)
  • the method further includes: training to obtain the structural defect detection model for the iron tower through the following steps:
  • the sample data includes positive samples and negative samples.
  • the positive samples are the modal parameters of the position where the iron tower structure is defective, and the negative samples are the modal parameters of the normal position of the iron tower structure;
  • the above-mentioned iron tower structural defect detection model may be generated through machine learning algorithms such as SVM and neural network.
  • an embodiment of the present invention also provides a device for detecting defects in the structure of a transmission line tower, as shown in the following embodiments. Since the principle and method of the device to solve the problem are similar, the implementation of the device can refer to the implementation of the method, and the repetition will not be repeated. As used below, the term “unit” or “module” can be a combination of software and/or hardware that implements a predetermined function. Although the devices described in the following embodiments are preferably implemented by software, implementation by hardware or a combination of software and hardware is also possible and conceived.
  • an embodiment of the present invention provides a device for detecting structural defects of a transmission line iron tower, which includes:
  • Image acquisition module 01 used to acquire multi-frame images of the structure of the iron tower to be inspected
  • the three-dimensional dynamic model generation module 02 is used to determine the three-dimensional dynamic model of the steel tower structure to be tested according to the multi-frame images of the steel tower structure to be tested;
  • the modal parameter determination module 03 is used to determine the modal parameters of the iron tower structure to be inspected according to the three-dimensional dynamic model of the iron tower structure to be inspected;
  • the defect detection module 04 is used to input the modal parameters of the steel tower structure to be tested into the pre-trained steel tower structure defect detection model, and output the defect type of the steel tower structure to be tested; the steel tower structure defect detection model is preliminarily based on the modal parameter sample of the steel tower structure Training generation.
  • each frame of image includes multiple sets of differential images with different shooting angles.
  • the modal parameter determination module 03 is specifically used for:
  • the modal parameters of the steel tower structure to be tested are determined.
  • the defect detection module 04 is also used to:
  • the sample data includes positive samples and negative samples.
  • the positive samples are the modal parameters of the position where the iron tower structure is defective, and the negative samples are the modal parameters of the normal position of the iron tower structure;
  • the technical solution provided by the embodiments of the present invention adopts: collecting multi-frame images of the structure of the tower to be inspected, and determining the three-dimensional dynamic model of the structure of the tower to be inspected according to the multi-frame images of the structure of the tower to be inspected; according to the structure of the tower to be inspected 3D dynamic model to determine the modal parameters of the tower structure to be inspected; input the modal parameters of the tower structure to be inspected into the tower structure defect detection model generated by pre-training, and output the defect type of the tower structure to be inspected, which realizes the structure based on the tower structure.
  • Multi-frame images establish a three-dimensional dynamic model of the tower structure, and identify the defects of the tower structure based on machine learning, which solves the problem of large-scale structural mechanics defect identification such as the collapse or partial fracture of the tower with a higher tower, and improves the structural defects of the transmission line tower
  • the efficiency and accuracy of detection can be widely used in power systems.
  • the embodiments of the present invention can be provided as a method, a system, or a computer program product. Therefore, the present invention may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.
  • computer-usable storage media including but not limited to disk storage, CD-ROM, optical storage, etc.
  • These computer program instructions can also be stored in a computer-readable memory that can guide a computer or other programmable data processing equipment to work in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture including the instruction device.
  • the device implements the functions specified in one process or multiple processes in the flowchart and/or one block or multiple blocks in the block diagram.
  • These computer program instructions can also be loaded on a computer or other programmable data processing equipment, so that a series of operation steps are executed on the computer or other programmable equipment to produce computer-implemented processing, so as to execute on the computer or other programmable equipment.
  • the instructions provide steps for implementing the functions specified in one process or multiple processes in the flowchart and/or one block or multiple blocks in the block diagram.

Abstract

一种输电线路铁塔结构缺陷检测方法及装置，该方法包括：采集待检测铁塔结构的多帧图像（101）；根据待检测铁塔结构的多帧图像，确定待检测铁塔结构的三维动态模型（102）；根据待检测铁塔结构的三维动态模型，确定待检测铁塔结构的模态参数，模态参数包括：共振峰频率和模态形状（103）；将待检测铁塔结构的模态参数输入预先训练生成的铁塔结构缺陷检测模型，输出待检测铁塔结构的缺陷位置和缺陷类型；铁塔结构缺陷检测模型根据铁塔结构的模态参数样本预先训练生成（104）。本方案提升了输电线路铁塔结构缺陷检测的效率和准确性，可广泛地应用在电力系统中。

Description

输电线路铁塔结构缺陷检测方法及装置 技术领域
本发明涉及故障检测技术领域，特别涉及一种输电线路铁塔结构缺陷检测方法及装置。
背景技术
输电铁塔在架空输电线路中起着支撑导线、地线及其他附件的作用，是电力系统中重要的组成部分。输电铁塔结构中的某些轻微的损伤和缺陷会伴随着载荷的长期作用，逐渐发展成为严重的损伤和缺陷，铁塔结构的损伤和缺陷会直接影响到电力系统的运行。对输电铁塔进行结构缺陷检测，可提高电力系统安全性和可靠性，避免重大电力事故的发生。
输电铁塔具有高耸塔体和大跨越结构所共有的塔体高、柔性强等特征，易受地震、风、浮冰等环境载荷的影响，在极端条件及振动疲劳损伤下更易引起输电铁塔结构动态倒塌破坏。在电网中，外力和大自然对架空输电线路的影响最为严重。而输电线路倒塔事件并不少见，除此之外，倒塔事故一旦发生，就会对人们的生命财产造成重大损失。
目前对输电铁塔结构缺陷的检测方式主要包括：人工巡检、可见光设备在线监测方法、搭配无人机等载具的识别装置检测方法。
人工巡检是指巡视员携带望远镜，使用车辆以及步行等方式对铁塔进行目视检查，这种方法已经比较成熟，可以检查出一些已经形成的缺陷甚至较严重缺陷，搭配熟练的巡视人员有时能检查出一些未形成的缺陷，但是这种方法缺点也很明显，人力成本巨大，对于一些山区或者自然灾害等紧急情况下的区域往往无法进行人工巡检，无法提前识别缺陷也不能识别较高塔位的多种盲区的缺陷。
可见光设备在线监测方法不需要巡视人员到现场进行巡检，可以较密集地获取图片数据，但是该方法的每个在线监测装置只能识别一小片区域的缺陷，且安装成本巨大，难以大规模安装。
搭配无人机等载具的识别装置检测方法可以获得更大范围的铁塔巡视数据。现在的无人机铁塔巡视主要使用可见光以及图片识别的方式对铁塔缺陷进行识别，该方法只能识别局部缺陷比如销钉缺失、绝缘子爆裂等非结构性缺陷，对会引起倒塔、局部断裂等铁塔大型结构力学缺陷无法识别。
针对上述问题，目前尚未提出有效的解决方案。
发明内容
本发明实施例提供了一种输电线路铁塔结构缺陷检测方法，用以提高输电线路铁塔结构缺陷检测的效率和准确性，该方法包括：
采集待检测铁塔结构的多帧图像；
根据待检测铁塔结构的多帧图像，确定待检测铁塔结构的三维动态模型；
根据待检测铁塔结构的三维动态模型，确定待检测铁塔结构的模态参数，模态参数包括：共振峰频率和模态形状；
将待检测铁塔结构的模态参数输入预先训练生成的铁塔结构缺陷检测模型，输出待检测铁塔结构的缺陷位置和缺陷类型；铁塔结构缺陷检测模型根据铁塔结构的模态参数样本预先训练生成。
本发明实施例还提供了一种输电线路铁塔结构缺陷检测装置，用以提高输电线路铁塔结构缺陷检测的效率和准确性，该装置包括：
图像采集模块，用于采集待检测铁塔结构的多帧图像；
三维动态模型生成模块，用于根据待检测铁塔结构的多帧图像，确定待检测铁塔结构的三维动态模型；
模态参数确定模块，用于根据待检测铁塔结构的三维动态模型，确定待检测铁塔结构的模态参数；
缺陷检测模块，用于将待检测铁塔结构的模态参数输入预先训练生成的铁塔结构缺陷检测模型，输出待检测铁塔结构的缺陷类型；铁塔结构缺陷检测模型根据铁塔结构的模态参数样本预先训练生成。
本发明实施例还提供了一种计算机设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，所述处理器执行所述计算机程序时实现上述所述方法。
本发明实施例还提供了一种计算机可读存储介质，所述计算机可读存储介质存储有执行上述所述方法的计算机程序。
本发明实施例提供的技术方案通过：采集待检测铁塔结构的多帧图像，根据待检测铁塔结构的多帧图像，确定待检测铁塔结构的三维动态模型；根据待检测铁塔结构的三维动态模型，确定待检测铁塔结构的模态参数；将待检测铁塔结构的模态参数输入预先 训练生成的铁塔结构缺陷检测模型，输出待检测铁塔结构的缺陷类型，实现了基于铁塔结构的多帧图像建立铁塔结构的三维动态模型，基于机器学习进行铁塔结构的缺陷识别，解决了塔位较高的铁塔的倒塌或局部断裂等大型结构力学的缺陷识别问题，提升了输电线路铁塔结构缺陷检测的效率和准确性，可广泛地应用在电力系统中。
附图说明
为了更清楚地说明本发明实施例中的技术方案，下面将对实施例描述中所需要使用的附图作简单地介绍，显而易见地，下面描述中的附图仅仅是本发明的一些实施例，对于本领域普通技术人员来讲，在不付出创造性劳动的前提下，还可以根据这些附图获得其他的附图。在附图中：
图1为本发明实施例中输电线路铁塔结构缺陷检测方法的流程示意图；
图2是本发明实施例中采集待检测输电线路铁塔结构图像的示意图；
图3(a)是本发明实施例中抽象输电导线模型一应用场景示意图；
图3(b)是本发明实施例中抽象输电导线模型另一应用场景示意图；
图4(a)是本发明实施例中基于垂直方向相机对的计算结果示意图；
图4(b)是本发明实施例中基于水平方向相机对的计算结果示意图；
图5为本发明实施例中输电线路铁塔结构缺陷检测装置的结构示意图。
具体实施方式
为使本发明实施例的目的、技术方案和优点更加清楚明白，下面结合附图对本发明实施例做进一步详细说明。在此，本发明的示意性实施例及其说明用于解释本发明，但并不作为对本发明的限定。
输电铁塔的结构存在严重的损伤和缺陷时，在环境荷载的影响下很容易引起倒塌事故，对人们的生命财产造成重大损失。为了提升输电线路铁塔结构缺陷检测的效率和准确性，本发明实施例提供一种输电线路铁塔结构缺陷检测方法，如图1所示，该方法包括：
步骤101：采集待检测铁塔结构的多帧图像；
步骤102：根据待检测铁塔结构的多帧图像，确定待检测铁塔结构的三维动态模型；
步骤103：根据待检测铁塔结构的三维动态模型，确定待检测铁塔结构的模态参数，模态参数包括：共振峰频率和模态形状；
步骤104：将待检测铁塔结构的模态参数输入预先训练生成的铁塔结构缺陷检测模型，输出待检测铁塔结构的缺陷类型；铁塔结构缺陷检测模型根据铁塔结构的模态参数样本预先训练生成。
如图1所示，本发明实施例提供的技术方案通过：采集待检测铁塔结构的多帧图像，根据待检测铁塔结构的多帧图像，确定待检测铁塔结构的三维动态模型；根据待检测铁塔结构的三维动态模型，确定待检测铁塔结构的模态参数；将待检测铁塔结构的模态参数输入预先训练生成的铁塔结构缺陷检测模型，输出待检测铁塔结构的缺陷类型，实现了基于铁塔结构的多帧图像建立铁塔结构的三维动态模型，基于机器学习进行铁塔结构的缺陷识别，解决了塔位较高的铁塔的倒塌或局部断裂等大型结构力学的缺陷识别问题，提升了输电线路铁塔结构缺陷检测的效率和准确性，可广泛地应用在电力系统中。
具体实施时，为了提高输电线路铁塔结构三维动态模型的准确性，在一个实施例中，步骤101采集的待检测铁塔结构的多帧图像中，每一帧图像包括多组不同拍摄角度的差分图像。
具体实施时，步骤101中，如图2所示，可以通过四目相机采集待检测铁塔结构的多帧图像。四目相机采用四台相机平行光轴布置形成矩阵阵列，对待检测铁塔结构进行4路图片的多帧同步采集，可以得到6组不同角度、不同时间的的差分图像，具有匹配快速、运算简单的特点，易实现芯片化等优点。
具体实施时，为了确定待检测铁塔结构的三维动态模型，在一个实施例中，步骤102可以包括：
根据采集的待检测铁塔结构的多帧图像，确定待检测铁塔结构的深度数据；
根据深度数据，确定待检测铁塔结构的三维动态模型。
具体实施时，可以使用四目相机对输电线路的铁塔结构进行三维动态建模，即确定待检测铁塔结构的三维动态模型。四目相机在两个互相垂直的方向上具有高效的抗噪声能力，从而在整个线性空间上可以实现所有方向上的有效检测，即便是单方向的对象(如无背景的输电线、输电铁塔等)也能获得较好检测性能。在确定待检测铁塔结构的深度数据时还考虑了待检测铁塔结构的应用场景(待检测铁塔结构是应用在水平结构场景中还是垂直结果场景中)，根据待检测铁塔结构的应用场景，确定选用四目相机的中哪 个相机对数据来计算深度数据，从而使得最后建立的待检测铁塔结构的三维动态模型最为精确，进一步提高了后续铁塔结构缺陷检测的效率和准确性。
下面对使用四目相机对输电线路的铁塔结构进行三维动态建模，确定待检测铁塔结构的三维动态模型的原理进行介绍如下。
双目视觉是一种常见的基础机器视觉技术，四目相机可以分解成6对(上左-上右，下左-下右，上左-下左，上右-下右，上右-下左，上左-下右)双目相机系统，本测量方法最关心的是两个相互垂直的方向上的差分图像数据，下面对该测量方法主要针对上左-下左，下左-下右两对相机机器所代表的两个相互垂直的方向进行阐述，三个相机位置简称顶、左、右，实际使用中4个相机为这两个典型相机对的扩充，得到三个相机的投射矩阵为：
Figure PCTCN2019111373-appb-000001
Figure PCTCN2019111373-appb-000002
Figure PCTCN2019111373-appb-000003
f为所有相机的共同焦距，b h和b v分别代表垂直对和水平对的基线(相机距离)；假设3D点的齐次坐标[x y z] T在不同相机中的投影为 x， x的计算公式如下：
Figure PCTCN2019111373-appb-000004
其中，[ x  y  z] T为 x在不同相机中投影后的坐标。
将3D点投影后的坐标转化为二维坐标x，x的计算公式如下：
Figure PCTCN2019111373-appb-000005
其中，u和v分别为二维坐标x的横纵坐标。
将一个3D点X投影到左、右相机中时使用公式(4)得到：
x L＝P L·X＝[f·x,f·y,z] T       (6)
x R＝P R·X＝[f·x-b h·f,f·y,z] T        (7)
其中， x L和 x R分别为3D点X投影到左、右相机后的三维坐标。
从公式(5)、(7)可以推导出图像点的差分值：
Figure PCTCN2019111373-appb-000006
其中，x L和x R分别为3D点X投影到左、右相机后的二维坐标。
在图像坐标在水平方向上所得到的差被相应地称为水平视差：
Figure PCTCN2019111373-appb-000007
同理可以计算垂直方向上的垂直视差：
Figure PCTCN2019111373-appb-000008
那么一个点的深度z就可以既通过相机的水平视差也可以通过相机的垂直视差进行计算。
上述步骤将同一帧图像进行三维重构，得到铁塔结构的三维静态模型，重复上述步骤，将多帧图像进行三维重构，得到铁塔结构的三维动态模型。
由上面的计算可知，垂直结构和水平结构的相机对都可以对深度进行计算，在公式中并未体现具体差异，然而在一些实际应用中，两个方向的视差所带来的计算却存在较大不同，下面分析本发明中待检测铁塔结构的不同应用场景，所得到深度计算结果的不同。
如图3(a)和图3(b)所示，比较纯粹的单向结构(抽象待检测铁塔结构模型)。
假设我们感兴趣的是左图像中心中可以找到的点的深度，则相应的极线水平地和垂直地位于其他图像的中间。找到视差的最直接的方法是将参考相机影像的图像块(在图3(a)和图3(b)中概述为黑色矩形，即长度较长的矩形)与其他图像内的潜在对应位置进行比较。这里，搜索空间可以限于极线位置。
图4(a)和图4(b)使用抽象待检测绝缘子结构(绝缘子应用场景，即水平结构场景还是垂直结构场景)评估不同方向相机对的影响。图4(a)中纵轴代表水平视差，横轴表示水平基线；图4(b)中纵轴代表垂直视差，横轴表示垂直基线。
使用归一化互相关(normalized cross-correlation)作为相似性的度量产生图4(a)和图4(b)中所示的结果。在垂直结构的情况下，左右相机对可以非常好地确定视差d h＝20px，并且没有任何组合的模糊性，如水平对齐的立体装置(参见图4(a)中的实线)。但是，垂直相机对的测试结果显示视差计算效果较差(参见图4(b)中的实线)，因此，根据不同应用场景来选择用哪个相机对的数据进行深度计算尤为重要。
对于如图3(b)所示的水平结构(水平结构应用场景)，可以类似地观察到相同的情况。这里不可能从水平对齐的立体装备中提取真实的视差。但是，它可以用垂直对来测量。显然，对于任何可能的场景使用单个立体装备是不够的。应结合水平和垂直立体设置，以实现更好的鲁棒性。
具体实施时，为了确定待检测铁塔结构的模态参数，在一个实施例中，步骤103可以包括：
根据待检测铁塔结构的三维动态模型，确定待检测铁塔结构的位移向量；
根据待检测铁塔结构的位移向量，确定待检测铁塔结构的观测矢量；
根据待检测铁塔结构的观测矢量，确定待检测铁塔结构的模态参数。
具体实施时，步骤103中，按照如下步骤确定待检测铁塔结构的模态参数。
第一步：计算待检测铁塔结构的位移向量。
可以通过4个不同的相机按照帧同步的方式获得的4组不同矩阵信息I 1(ξ，η，t)、I 2(ξ，η，t)、I 3(ξ，η，t)、I 4(ξ，η，t)，其中(ξ，η)表示二维图片中像素的坐标，t代表时间坐标。通过视觉匹配方法对多帧4幅不同图像的像素点进行匹配，得到三维动态模型，基于三维动态模型中N个匹配点的像素位移，计算待检测铁塔结构形状的分布：
Figure PCTCN2019111373-appb-000009
式中，
Figure PCTCN2019111373-appb-000010
表示匹配点的像素的位置随时间的分布向量，其中，n＝1，...，N，N为匹配点个数，
Figure PCTCN2019111373-appb-000011
R 3为三维动态模型。
第二步：计算待检测铁塔结构的观测矢量。
将匹配点n的位移向量
Figure PCTCN2019111373-appb-000012
在向量
Figure PCTCN2019111373-appb-000013
上投影得到一组标量值
Figure PCTCN2019111373-appb-000014
Figure PCTCN2019111373-appb-000015
为单位向量，q为单位向量的方向，Q为投影方向的个数：
Figure PCTCN2019111373-appb-000016
将所有匹配点的位移向量
Figure PCTCN2019111373-appb-000017
在多个方向上投影得到多维向量
Figure PCTCN2019111373-appb-000018
Figure PCTCN2019111373-appb-000019
多维向量
Figure PCTCN2019111373-appb-000020
可以作为观察矢量用于位移向量
Figure PCTCN2019111373-appb-000021
在
Figure PCTCN2019111373-appb-000022
方向上的模态分析。
第三步：计算待检测铁塔结构的模态参数。
通过仅基于输出的模态参数估计法(Output-Only Modal Analysis)，输入待检测铁塔结构的观测矢量
Figure PCTCN2019111373-appb-000023
输出待检测铁塔结构在
Figure PCTCN2019111373-appb-000024
方向上的模态参数，包括：第m(m＝1,...,M)个共振峰频率
Figure PCTCN2019111373-appb-000025
和第m个模态形状
Figure PCTCN2019111373-appb-000026
其中，M是用于估计的最大模态阶数。
具体实施时，为了生成铁塔结构缺陷检测模型，在一个实施例中，在步骤104之前还包括：通过如下步骤训练得到所述铁塔结构缺陷检测模型：
获取训练样本数据，样本数据包括正样本和负样本，正样本为铁塔结构存在缺陷的位置的模态参数，负样本为铁塔结构正常的位置的模态参数；
将样本数据划分为训练集、测试集和验证集；
利用训练集对铁塔结构缺陷检测模型进行训练；
利用测试集对训练后的铁塔结构缺陷检测模型进行测试；
利用验证集对测试后的铁塔结构缺陷检测模型进行验证，得到铁塔结构缺陷检测模型。
具体实施时，步骤104中，可以通过SVM、神经网络等机器学习算法生成上述铁塔结构缺陷检测模型。
基于同一发明构思，本发明实施例中还提供了一种输电线路的铁塔结构缺陷检测装置，如下面的实施例。由于装置解决问题的原理与方法相似，因此装置的实施可以参见方法的实施，重复之处不再赘述。以下所使用的，术语“单元”或者“模块”可以实现预定功能的软件和/或硬件的组合。尽管以下实施例所描述的装置较佳地以软件来实现，但是硬件，或者软件和硬件的组合的实现也是可能并被构想的。
如图5所示，本发明实施例提供一种输电线路的铁塔结构缺陷检测装置，该装置包括：
图像采集模块01，用于采集待检测铁塔结构的多帧图像；
三维动态模型生成模块02，用于根据待检测铁塔结构的多帧图像，确定待检测铁塔结构的三维动态模型；
模态参数确定模块03，用于根据待检测铁塔结构的三维动态模型，确定待检测铁塔结构的模态参数；
缺陷检测模块04，用于将待检测铁塔结构的模态参数输入预先训练生成的铁塔结构缺陷检测模型，输出待检测铁塔结构的缺陷类型；铁塔结构缺陷检测模型根据铁塔结构的模态参数样本预先训练生成。
实施例中，图像采集模块01采集的待检测铁塔结构的多帧图像中，每一帧图像包括多组不同拍摄角度的差分图像。
实施例中，模态参数确定模块03具体用于：
根据待检测铁塔结构的三维动态模型，确定待检测铁塔结构的位移向量；
根据待检测铁塔结构的位移向量，确定待检测铁塔结构的观测矢量；
根据待检测铁塔结构的观测矢量，确定待检测铁塔结构的模态参数。
实施例中，缺陷检测模块04在将待检测铁塔结构的模态参数输入预先训练生成的铁塔结构缺陷检测模型之前，还用于：
获取训练样本数据，样本数据包括正样本和负样本，正样本为铁塔结构存在缺陷的位置的模态参数，负样本为铁塔结构正常的位置的模态参数；
将样本数据划分为训练集、测试集和验证集；
利用训练集对铁塔结构缺陷检测模型进行训练；
利用测试集对训练后的铁塔结构缺陷检测模型进行测试；
利用验证集对测试后的铁塔结构缺陷检测模型进行验证，得到铁塔结构缺陷检测模型。
综上所述，本发明实施例提供的技术方案通过：采集待检测铁塔结构的多帧图像，根据待检测铁塔结构的多帧图像，确定待检测铁塔结构的三维动态模型；根据待检测铁塔结构的三维动态模型，确定待检测铁塔结构的模态参数；将待检测铁塔结构的模态参数输入预先训练生成的铁塔结构缺陷检测模型，输出待检测铁塔结构的缺陷类型，实现了基于铁塔结构的多帧图像建立铁塔结构的三维动态模型，基于机器学习进行铁塔结构的缺陷识别，解决了塔位较高的铁塔的倒塌或局部断裂等大型结构力学的缺陷识别问题，提升了输电线路铁塔结构缺陷检测的效率和准确性，可广泛地应用在电力系统中。
本领域内的技术人员应明白，本发明的实施例可提供为方法、系统、或计算机程序产品。因此，本发明可采用完全硬件实施例、完全软件实施例、或结合软件和硬件方面的实施例的形式。而且，本发明可采用在一个或多个其中包含有计算机可用程序代码的计算机可用存储介质(包括但不限于磁盘存储器、CD-ROM、光学存储器等)上实施的计算机程序产品的形式。
本发明是参照根据本发明实施例的方法、设备(系统)、和计算机程序产品的流程图和/或方框图来描述的。应理解可由计算机程序指令实现流程图和/或方框图中的每一流程和/或方框、以及流程图和/或方框图中的流程和/或方框的结合。可提供这些计算机程序指令到通用计算机、专用计算机、嵌入式处理机或其他可编程数据处理设备的处理器以产生一个机器，使得通过计算机或其他可编程数据处理设备的处理器执行的指令产生用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的装置。
这些计算机程序指令也可存储在能引导计算机或其他可编程数据处理设备以特定方式工作的计算机可读存储器中，使得存储在该计算机可读存储器中的指令产生包括指令装置的制造品，该指令装置实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能。
这些计算机程序指令也可装载到计算机或其他可编程数据处理设备上，使得在计算机或其他可编程设备上执行一系列操作步骤以产生计算机实现的处理，从而在计算机或其他可编程设备上执行的指令提供用于实现在流程图一个流程或多个流程和/或方框图一个方框或多个方框中指定的功能的步骤。
以上所述的具体实施例，对本发明的目的、技术方案和有益效果进行了进一步详细说明，所应理解的是，以上所述仅为本发明的具体实施例而已，并不用于限定本发明的保护范围，凡在本发明的精神和原则之内，所做的任何修改、等同替换、改进等，均应包含在本发明的保护范围之内。

Claims (10)

  1. 一种输电线路的铁塔结构缺陷检测方法，其特征在于，包括：
    采集待检测铁塔结构的多帧图像；
    根据所述待检测铁塔结构的多帧图像，确定待检测铁塔结构的三维动态模型；
    根据所述待检测铁塔结构的三维动态模型，确定待检测铁塔结构的模态参数，所述模态参数包括：共振峰频率和模态形状；
    将所述待检测铁塔结构的模态参数输入预先训练生成的铁塔结构缺陷检测模型，输出待检测铁塔结构的缺陷位置和缺陷类型；所述铁塔结构缺陷检测模型根据铁塔结构的模态参数样本预先训练生成。
  2. 如权利要求1所述的方法，其特征在于，所述待检测铁塔结构的多帧图像中，每一帧图像包括多组不同拍摄角度的差分图像。
  3. 如权利要求1所述的方法，其特征在于，根据所述待检测铁塔结构的三维动态模型，确定待检测铁塔结构的模态参数，包括：
    根据所述待检测铁塔结构的三维动态模型，确定待检测铁塔结构的位移向量；
    根据所述待检测铁塔结构的位移向量，确定待检测铁塔结构的观测矢量；
    根据所述待检测铁塔结构的观测矢量，确定待检测铁塔结构的模态参数。
  4. 如权利要求1至3任一项所述的方法，在将待检测铁塔结构的模态参数输入预先训练生成的铁塔结构缺陷检测模型之前，所述方法还包括：
    通过如下步骤训练得到所述铁塔结构缺陷检测模型：
    获取训练样本数据，所述样本数据包括正样本和负样本，所述正样本为铁塔结构存在缺陷的位置的模态参数，所述负样本为铁塔结构正常的位置的模态参数；
    将所述样本数据划分为训练集、测试集和验证集；
    利用所述训练集对铁塔结构缺陷检测模型进行训练；
    利用所述测试集对训练后的铁塔结构缺陷检测模型进行测试；
    利用所述验证集对测试后的铁塔结构缺陷检测模型进行验证，得到所述铁塔结构缺陷检测模型。
  5. 一种输电线路的铁塔结构缺陷检测装置，其特征在于，包括：
    图像采集模块，用于采集待检测铁塔结构的多帧图像；
    三维动态模型生成模块，用于根据所述待检测铁塔结构的多帧图像，确定待检测铁塔结构的三维动态模型；
    模态参数确定模块，用于根据所述待检测铁塔结构的三维动态模型，确定待检测铁塔结构的模态参数；
    缺陷检测模块，用于将所述待检测铁塔结构的模态参数输入预先训练生成的铁塔结构缺陷检测模型，输出待检测铁塔结构的缺陷位置和缺陷类型；所述铁塔结构缺陷检测模型根据铁塔结构的模态参数样本预先训练生成。
  6. 如权利要求5所述的装置，其特征在于，所述待检测铁塔结构的多帧图像中，每一帧图像包括多组不同拍摄角度的差分图像。
  7. 如权利要求5所述的装置，其特征在于，所述模态参数确定模块具体用于：
    根据所述待检测铁塔结构的三维动态模型，确定待检测铁塔结构的位移向量；
    根据所述待检测铁塔结构的位移向量，确定待检测铁塔结构的观测矢量；
    根据所述待检测铁塔结构的观测矢量，确定待检测铁塔结构的模态参数。
  8. 如权利要求5至7中任一项所述的装置，其特征在于，所述缺陷检测模块在将待检测铁塔结构的模态参数输入预先训练生成的铁塔结构缺陷检测模型之前，还用于通过如下步骤训练得到所述铁塔结构缺陷检测模型：
    获取训练样本数据，所述样本数据包括正样本和负样本，所述正样本为铁塔结构存在缺陷的位置的模态参数，所述负样本为铁塔结构正常的位置的模态参数；
    将所述样本数据划分为训练集、测试集和验证集；
    利用所述训练集对铁塔结构缺陷检测模型进行训练；
    利用所述测试集对训练后的铁塔结构缺陷检测模型进行测试；
    利用所述验证集对测试后的铁塔结构缺陷检测模型进行验证，得到所述铁塔结构缺陷检测模型。
  9. 一种计算机设备，包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序，其特征在于，所述处理器执行所述计算机程序时实现权利要求1至4任一所述输电线路的铁塔结构缺陷检测方法。
  10. 一种计算机可读存储介质，其特征在于，所述计算机可读存储介质存储有执行权利要求1至4任一所述输电线路的铁塔结构缺陷检测方法的计算机程序。
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