CN115512098A - Electronic bridge inspection system and inspection method - Google Patents
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Abstract
The application relates to the technical field of inspection management, in particular to a management method for bridge inspection, and specifically relates to an electronic inspection system and an inspection method for bridges; the system comprises a handheld terminal, a data processing terminal, a data storage terminal and a plurality of data marking points; the handheld terminal comprises a display module, a communication establishing module and a data acquisition module, wherein the communication establishing module receives marking signals of the data marking points in a preset range and establishes communication, and the data acquisition module acquires real-time data in the preset range corresponding to the data marking points based on the establishment of communication; the data processing terminal processes the real-time data acquired by the data acquisition module to obtain a processing result, and the data storage terminal is used for storing the processing result and the corresponding data processing terminal label and the data marking point label; and a data detection model is configured in the data processing terminal and is used for detecting the real-time data to obtain a processing result.
Description
Technical Field
The application relates to the technical field of inspection management, in particular to a management method for bridge inspection, and specifically relates to a bridge electronic inspection system and an inspection method.
Background
The number of bridges entering into service period in China is continuously increased, and the technical condition of the bridges is a main factor influencing the operation safety of the bridges. In order to ensure the safe operation of the bridge structure, the bridge maintenance management department standardizes the highway bridge maintenance management work by establishing a bridge inspection system. The inspection work of the bridge usually takes visual inspection as a main part, and is assisted by a measuring tool if necessary, and professional inspectors inspect the bridge and fill in an inspection record table, so that bridge management and maintenance units can timely and accurately master the technical condition and the disease development condition of each active bridge. The bridge inspection is key and basic work for guaranteeing the safe operation of the bridge, and has important significance for bridge maintenance and management decisions. The bridge has the characteristics of large structure quantity and complex bridge position environment, the traditional bridge inspection is mainly based on visual inspection, inspection personnel are required to fill in forms and collect bridge disease photos on site, the quality of inspection data is influenced by various factors such as personnel quality and environmental conditions, and the accuracy is difficult to guarantee. If the acquired data cannot timely and effectively reflect the structural condition, the correct evaluation of the structural operation condition of the bridge is influenced.
The traditional bridge inspection method can not meet the management requirements of refining, high efficiency and scientification of the current bridge inspection, and meanwhile, a bridge management unit has supervision requirements on bridge inspection services. In order to really master the on-site inspection condition, the inspection progress, the distribution and the severity of bridge diseases and the like of inspectors, the bridge management and maintenance capacity needs to be further enhanced by means of informatization. Therefore, technologies such as the internet of things, the mobile internet and an electronic map need to be comprehensively applied, a set of bridge inspection system capable of meeting higher management requirements is built, and the bridge management and maintenance level is comprehensively improved.
In the existing intelligent bridge inspection system, functions of collecting inspection information, storing inspection data, issuing inspection instructions and the like are mainly realized through an inspection terminal. However, the inspection main object is an inspection worker, and the inspection cost of the inspection worker is reduced, so that the inspection efficiency is improved, and bridge abnormity identification in the inspection process can not be realized in the prior art. Therefore, an electronic system capable of assisting inspection personnel in inspecting is needed, bridge inspection work is standardized, and inspection efficiency and data reliability are improved.
Disclosure of Invention
In order to solve the technical problems, the application provides an electronic bridge inspection system and an inspection method, which can accurately obtain inspection positions and inspection targets in the inspection process through a computer technology, improve the inspection cost of inspection personnel on inspection target points, recognize potential abnormalities in bridge inspection points through an abnormal data recognition method, reduce the neglect of the potential abnormalities caused by the subjective reasons of the inspection personnel, and realize the comprehensiveness of overall maintenance.
In order to achieve the above purpose, the embodiments of the present application adopt the following technical solutions:
in a first aspect, the electronic bridge inspection system comprises a handheld terminal, a data processing terminal, a data storage terminal and a plurality of data marking points; the handheld terminal comprises a display module, a communication establishing module and a data acquisition module, wherein the communication establishing module receives marking signals of the data marking points in a preset range and establishes communication, and the data acquisition module acquires real-time data in the preset range corresponding to the data marking points based on the establishment of communication; the data processing terminal processes the real-time data acquired by the data acquisition module to obtain a processing result, and the data storage end is used for storing the processing result, the corresponding data processing terminal label and the data marking point label; a data detection model is configured in the data processing terminal, and the data detection model is used for detecting the real-time data to obtain a processing result; the data storage terminal comprises a plurality of storage spaces corresponding to the data mark points, the storage spaces are used for storing data corresponding to the data mark points, each storage space comprises at least two storage subspaces, and the storage subspaces are used for correspondingly storing the processing result types.
In a first possible implementation manner of the first aspect, the data storage system further includes a terminal platform, where the terminal platform is connected to the data storage terminal, and collects storage data in the data storage terminal based on a collection policy.
With reference to the first possible implementation manner of the first aspect, in a second possible implementation manner, the acquisition policy includes a first acquisition policy and a second acquisition policy, and the first acquisition policy and the second acquisition policy are configured in the two storage subspaces respectively.
With reference to the second possible implementation manner of the first aspect, in a third possible implementation manner, the storage subspace includes a first storage subspace and a second storage subspace, and is configured with a first acquisition policy and a second acquisition policy, respectively; the first acquisition strategy is synchronous acquisition, and the second acquisition strategy is asynchronous acquisition.
In a fourth possible implementation manner of the first aspect, the handheld terminal further includes a map marking module, where the map marking module is configured with a plurality of layer maps, and any one of the layer maps is provided with a code corresponding to a data marking point; and the data mark points are configured with NFC digital labels, the NFC digital labels establish communication with the communication establishment module in a preset range, the layer map is determined based on the unique codes of the NFC digital labels, and the layer map and the corresponding mark point coordinates are displayed on the display module.
In a second aspect, a bridge electronic inspection method is applied to any one of the bridge electronic inspection systems, and the method includes: the communication establishing module acquires the communication signals of the data marking points in a preset range and establishes communication with the data marking points; the map marking module establishes a unique code of the corresponding data marking point based on communication to determine a corresponding map layer map, and performs the map layer map and the coordinate corresponding to the data marking point on the display module; the data acquisition module acquires real-time data in a preset range of the corresponding data marking points based on the establishment of communication, wherein the real-time data comprises real-time image data meeting the preset range; sending the real-time image data to the data processing terminal, and carrying out anomaly detection on the real-time image data based on a data detection model in the data processing terminal to obtain a detection result; sending the detection result to the data storage terminal based on a storage strategy; and the terminal platform acquires the data in the data storage terminal based on an acquisition strategy.
In a first possible implementation manner of the second aspect, the acquiring real-time data within a preset range of the corresponding data mark point based on the establishment of communication by the data acquiring module includes: the data acquisition module determines a target acquisition frame and a target acquisition standard image based on the unique codes of the data marking points; based on the target collection frame and the target collection standard image, the target collection standard image is used for carrying out image collection on the object to be inspected corresponding to the data marking point, so as to obtain the real-time image data, and the method specifically comprises the following steps: identifying the real-time image data based on the target acquisition frame to obtain an identification result; judging whether the real-time image data meets preset conditions or not based on the recognition result, and specifically comprising the following steps of: carrying out binarization processing on the real-time image data to obtain preprocessed image data; segmenting the preprocessed image data to obtain a target processed image; and extracting edge coordinates of the target processing image, comparing the edge coordinates with preset standard edge coordinates, and determining whether the real-time image data meets preset conditions based on a comparison threshold.
In a second possible implementation manner of the second aspect, the method includes performing anomaly detection on the real-time image data based on a data detection model in the data processing terminal to obtain a detection result, where the data detection model includes a feature extraction network, a regional recommendation network, and a classification network, and the method includes: extracting features in the real-time image data based on a feature extraction network to obtain a target feature map; positioning a plurality of candidate areas containing detection targets in the target feature map based on an area recommendation network; and acquiring the spatial characteristics of the candidate regions, modeling, judging whether the candidate regions contain the probability distribution of the target features or not based on the model, and determining whether the candidate regions contain the target features or not based on the probability distribution.
With reference to the second possible implementation manner of the second aspect, in a third possible implementation manner, the feature extraction network includes five sets of convolutional neural networks, and any of the convolutional neural networks includes at least one convolutional layer, an activation function layer, and a pooling layer; the activation function in the activation function layer is a rule function.
With reference to the second possible implementation manner of the second aspect, in a fourth possible implementation manner, the area recommendation network includes a first convolution layer, and a second convolution layer and a third convolution layer connected to an output end of the first convolution layer, an output end of the second convolution layer is connected to a normalized index function layer, and an output end of the normalized index function layer and an output end of the third convolution layer are connected to a bonding layer.
In a third aspect, an electronic device is provided, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, the processor implementing the method of any one of the preceding claims when executing the computer program.
In a fourth aspect, there is provided a computer readable storage medium storing a computer program which, when executed by a processor, implements the method of any of the above claims.
According to the technical scheme, the accurate acquisition of the inspection point in the inspection process and the identification of whether the inspection point contains abnormity or not are achieved by setting the functional modules in the system. The inspection efficiency is improved, and the identification accuracy of abnormity in inspection is improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
The methods, systems, and/or processes of the figures are further described in accordance with the exemplary embodiments. These exemplary embodiments will be described in detail with reference to the drawings. These exemplary embodiments are non-limiting exemplary embodiments in which example numbers represent similar mechanisms throughout the various views of the drawings.
Fig. 1 is a schematic structural diagram of an electronic bridge inspection system provided in an embodiment of the present application.
Fig. 2 is a flowchart of a bridge electronic inspection method according to some embodiments of the present disclosure.
Fig. 3 is a schematic structural diagram of an electronic device provided in an embodiment of the present application.
Detailed Description
In order to better understand the technical solutions, the technical solutions of the present application are described in detail below with reference to the accompanying drawings and specific embodiments, and it should be understood that the specific features in the embodiments and examples of the present application are detailed descriptions of the technical solutions of the present application, and are not limitations of the technical solutions of the present application, and the technical features in the embodiments and examples of the present application may be combined with each other without conflict.
In the following detailed description, numerous specific details are set forth by way of examples in order to provide a thorough understanding of the relevant guidance. It will be apparent, however, to one skilled in the art that the present application may be practiced without these specific details. In other instances, well-known methods, procedures, systems, components, and/or circuits have been described at a relatively high-level, without detail, in order to avoid unnecessarily obscuring aspects of the present application.
Flowcharts are used herein to illustrate the implementations performed by systems according to embodiments of the present application. It should be expressly understood that the processes performed by the flowcharts may be performed out of order. Rather, these implementations may be performed in the reverse order or simultaneously. In addition, at least one other implementation may be added to the flowchart. One or more implementations may be deleted from the flowchart.
Before further detailed description of the embodiments of the present invention, terms and expressions mentioned in the embodiments of the present invention are explained, and the terms and expressions mentioned in the embodiments of the present invention are applied to the following explanations.
(1) In response to the condition or state on which the performed operation depends, one or more of the performed operations may be in real-time or may have a set delay when the dependent condition or state is satisfied; there is no restriction on the order of execution of the operations performed unless otherwise specified.
(2) Based on the condition or state on which the operation to be performed depends, the operation or operations to be performed may be in real time or may have a set delay when the condition or state on which the operation depends is satisfied; there is no restriction on the order of execution of the operations performed unless otherwise specified.
(3) The convolutional neural network is a feedforward neural network which comprises convolution calculation and has a deep structure. Convolutional neural networks are proposed by the mechanism of the biological Receptive Field (received Field). Convolutional neural networks are specialized neural networks for processing data having a grid-like structure. For example, time series data (which may be regarded as a one-dimensional grid formed by regularly sampling on a time axis) and image data (which may be regarded as a two-dimensional pixel grid), the convolutional neural network employed in the present embodiment processes for the image data.
According to the technical scheme, the main application scene is the inspection of the bridge, and the inspection under the actual condition is mainly based on the manual inspection of the inspection target and the judgment of the abnormal condition. The abnormal conditions aiming at the bridge comprise structural abnormality of the bridge, which is mainly embodied by cracks or structural defects on a key main body structure of the bridge, and daily road surface abnormality comprises road surface throws or other pollutants, wherein detection of structural defects of the key main body structure of the bridge, such as a segmented road surface connecting position or a beam body, is a key necessary detection, and daily road surface abnormality patrol belongs to random patrol. The routing inspection system in the embodiment is mainly applied based on routing inspection for abnormal conditions of bridges, and routing inspection for daily road surface abnormality can be additionally configured in the scheme in the embodiment. The main part of patrolling and examining is mainly patrolling and examining personnel, and patrolling and examining personnel mainly go on based on subjective mode at current in-process of patrolling and examining, and to this type of mode of patrolling and examining have the following problem: firstly, the management aiming at the inspection personnel is mainly based on the subjectivity of the inspection personnel, the inspection omission is possibly caused for the key inspection points because of insufficient experience of the inspection personnel, and the condition of the inspection omission is more easily caused for the inspection personnel who is on duty newly under the inexperienced operation condition. Moreover, the subjective performance of the inspection personnel cannot provide a specific judgment conclusion for the inspection result, and particularly the judgment on the key main body structure of the bridge is insufficient, so that the accuracy of the inspection result is not high easily.
Therefore, in summary, there is a need to provide a targeted inspection system, which can improve the efficiency of the inspection process and the accuracy of the inspection.
The solution to the above problems is mainly based on the configuration of an inspection system, the marks of key target points are configured in the inspection system, the accurate positioning of the key mark points is realized through the marks, inspection coordinates are generated based on the extraction of an inspection map corresponding to the key mark points, and inspection personnel can perform inspection operation on the key mark points through the inspection coordinates. And a corresponding inspection detection module is configured in the system to realize abnormal identification of the inspection target, so that the accuracy of the inspection target is improved. And the abnormal identification of the inspection target is processed by the remote data processing module separated from the inspection terminal, so that the problem of higher operation cost of the inspection terminal caused by processing is solved. In addition, in the embodiment, the problems of storage redundancy and high data interaction cost caused by large data volume are solved by configuring the acquisition strategy for the correspondence between the identification result and the data in the inspection process.
Referring to fig. 1, based on the above technical background, the embodiment of the present application provides an electronic bridge inspection system 100, which includes a handheld terminal 110, a data processing terminal 120, a data storage terminal 130, and a plurality of data mark points 140; the handheld terminal comprises a display module 111, a communication establishing module 112 and a data acquisition module 113, wherein the communication establishing module receives marking signals of the data marking points in a preset range and establishes communication, and the data acquisition module acquires real-time data in the preset range corresponding to the data marking points based on the establishment of communication; the data processing terminal processes the real-time data acquired by the data acquisition module to obtain a processing result, and the data storage end is used for storing the processing result, the corresponding data processing terminal label and the data marking point label; a data detection model is configured in the data processing terminal and used for detecting the real-time data to obtain a processing result; the data storage terminal comprises a plurality of storage spaces corresponding to the data mark points, the storage spaces are used for storing data corresponding to the data mark points, each storage space comprises at least two storage subspaces, and the storage subspaces are used for correspondingly storing the processing result types.
In this embodiment, the electronic bridge inspection system further includes a terminal platform 150, the terminal platform is connected to the data storage terminal, and collects the storage data in the data storage terminal based on a collection policy.
Specifically, in this embodiment, the acquisition policy includes a first acquisition policy and a second acquisition policy, and the first acquisition policy and the second acquisition policy are respectively configured in the two storage subspaces. The storage subspace includes a first storage subspace 131 and a second storage subspace 132, which are respectively configured with a first acquisition policy and a second acquisition policy; the first acquisition strategy is synchronous acquisition, and the second acquisition strategy is asynchronous acquisition. In this embodiment, the storage subspace is divided into a first storage subspace and a second storage subspace, and a corresponding acquisition policy is configured to implement acquisition of different data in different ways, specifically, abnormal data is stored in the first storage subspace, and normal data is stored in the second storage subspace. Because the importance of the abnormal data on the subsequent analysis of the data is higher than that of the normal data, the abnormal data needs to be collected in real time, the decision error caused by data transmission delay is reduced, the normal data can be collected to a terminal platform based on the preset collection frequency, and in the embodiment, the application scene is the inspection of the bridge, the time period for the abnormal generation of the bridge is longer, so the frequency for collecting the normal data can be set longer, and the collection can be carried out in a monthly or quarterly mode. The above acquisition strategy is an automatic acquisition strategy, and a third acquisition strategy can be set as an active acquisition strategy, so that the data of the second storage subspace is actively acquired in real time at any time in a self-defined manner based on a terminal platform user.
In this embodiment, the handheld terminal further includes a map marking module 114, where the map marking module 114 is configured with a plurality of layer maps, and any one of the layer maps is provided with a code corresponding to a data mark point; and the data mark points are configured with NFC digital labels, the NFC digital labels establish communication with the communication establishment module in a preset range, the layer map is determined based on the unique codes of the NFC digital labels, and the layer map and the corresponding mark point coordinates are displayed on the display module. The NFC digital label can receive a communication port or a communication protocol of a communication establishing module in a handheld terminal matched with the NFC digital label in advance based on a preset signal receiving range to communicate, so that a communication link is established, a corresponding command is sent through the port, the command is displayed on a corresponding display module, the display mode is displayed based on a mode of marking a mark point in a map layer map corresponding to the NFC digital label, and the display mode is used for acquiring a patrol area and acquiring key patrol points in the patrol area. The above settings are mainly set based on missed inspection of key inspection points, and the inspection aiming at daily inspection including the inspection of road surface sprinklers or pollutants is generated based on random events, and are not described in the embodiment.
Referring to fig. 2, the present embodiment provides an inspection method based on an inspection system, and the inspection method is applied to any one of the above bridge electronic inspection systems, and the method includes:
s210, the communication establishing module acquires the communication signals of the data mark points in a preset range and establishes communication with the data mark points.
In this embodiment, the data mark point is configured with an NFC digital tag, and the NFC digital tag establishes communication with the communication establishing module within a preset range. The NFC digital tag has a communication function, and a communication protocol or a communication port corresponding to the NFC digital tag is configured in the communication establishing module. The NFC digital tag has a certain communication range, real-time communication connection request sending is carried out through the NFC digital tag, a communication protocol matched with the handheld terminal is arranged in the communication connection request, and communication connection is achieved through matching based on the same communication protocol. In this embodiment, the establishment of the communication connection includes an active connection and a passive connection, where the active connection is a request for performing communication connection for the NFC digital tag, and is performed based on the polling personnel; the passive connection requests the NFC digital label to perform communication connection, and the communication establishing module directly performs connection according to the communication connection request. The determination of the active connection and the passive connection mode can be set based on inspection personnel, and the default is active connection in the method.
Step S220, the map marking module establishes the unique codes of the corresponding data marking points based on communication to determine the corresponding layer map, and the layer map and the coordinates corresponding to the data marking points are displayed on the display module.
In this embodiment, the unique code is set in the NFC digital tag, the acquisition of the layer map corresponding to the inspection target can be acquired through the association relationship of the unique code, wherein a coordinate point of the inspection target point is configured in the layer map and is displayed in the layer map, wherein the inspection point for establishing the communication connection is displayed in a visual jumping manner, so that the inspection target point for establishing the communication connection can be displayed to the inspection staff, so that the inspection staff can determine the inspection target point closest to the inspection target point, and the determination of the inspection target point by the inspection staff is reduced by displaying the inspection target points. The incidence relation in the embodiment is realized on the basis of a preset database, and the incidence relation is established among a plurality of routing inspection target points through a hierarchical relation in the database. The method specifically comprises the following steps: and establishing a list, and setting the list in a multi-level mode, wherein the highest level is an inspection target, namely a plurality of inspection targets are stored in the database, and the inspection targets are target bridges. The next level is a target patrol point, and the last level is a unique code and a coordinate point of the NFC digital tag. The method comprises the steps of determining target inspection points through unique codes in NFC digital tags, determining inspection targets based on the target inspection points, and determining all the target inspection points, a plurality of NFC digital tags corresponding to the target inspection points and a plurality of corresponding coordinate points based on the inspection targets.
In the embodiment, the routing inspection target point can be determined through the process, the possibility of missed inspection of the routing inspection personnel is reduced, in other embodiments, an optimal route planning method can be configured based on the coordinate points, and an optimal routing inspection route is obtained through the relationship among the coordinate points and the coordinates of the current routing inspection target point, wherein the route planning method can be realized by adopting a method in the prior art, and is not described in the embodiment.
And S230, the data acquisition module acquires real-time data in a preset range of the corresponding data mark points based on the communication establishment.
In the embodiment, the main application scenario is to identify the abnormality in the bridge, and the identification manner is mainly based on the identification of the abnormality in the key structure of the bridge, and the main embodiment manner of the abnormality in the key structure of the bridge is that a crack or a fissure is generated on the surface of the bridge. Therefore, the anomaly identification for the bridge is mainly based on whether the surface of the bridge has cracks, and the acquisition for the cracks is mainly identified through the image of the bridge, so the real-time data is real-time image data in the embodiment.
The key points of the bridge can be on the road surface of the bridge and the beam columns of the bridge, the areas of the road surface of the bridge and the beam columns of the bridge are large, the acquisition mode of image data needs to be determined so as to enable the image to display crack information as much as possible, and other noise images are prevented from being mixed in the image, wherein the noise images are other environment background images except the related image of the bridge. Since the area of the bridge road surface is large and the possibility of mixing other environmental background images is low, the processing for this step is mainly used for image acquisition at the edge of the beam column or the road surface. For the purpose, the processing logic in this embodiment is to set a target collection frame, where the target collection frame has a preset defined condition such as a length, a width, and the like, and collect the main image and the background image in the target image through the target collection frame, so that the target image is completely in the target collection frame, thereby reducing the problem of high subsequent processing caused by introducing the background environment image.
In this embodiment, the step specifically includes the following steps:
and the data acquisition module determines a target acquisition frame and a target acquisition standard image based on the unique codes of the data marking points. In this embodiment, the areas of the target acquisition boxes adopted are different based on the difference in the areas of the targets, wherein the determination of the target acquisition boxes is realized through the correspondence.
And carrying out image acquisition on the object to be inspected corresponding to the data marking point based on the target acquisition frame and the target acquisition standard image to obtain the real-time image data. The method specifically comprises the following steps:
and identifying the real-time image data based on the target acquisition frame to obtain an identification result.
Judging whether the real-time image data meets a preset condition or not based on the identification result, wherein the step specifically comprises the following steps:
and carrying out binarization processing on the real-time image data to obtain preprocessed image data.
And segmenting the preprocessed image data to obtain a target processed image.
And extracting edge coordinates of the target processing image, comparing the edge coordinates with preset standard edge coordinates, and determining whether the real-time image data meets preset conditions based on a comparison threshold.
In this embodiment, when a preset condition is satisfied, the real-time image data is collected to obtain a real-time image, and the real-time image is a collected target image. The image segmentation and coordinate point extraction method can be obtained based on the existing method in the existing machine vision, and is not described in this embodiment.
Step S340, the real-time image data are sent to the data processing terminal, and anomaly detection is carried out on the real-time image data based on a data detection model in the data processing terminal to obtain a detection result.
In the embodiment, the data detection model comprises a feature extraction network, an area recommendation network and a classification network. The characteristic extraction network comprises five groups of convolutional neural networks, wherein any convolutional neural network comprises at least one convolutional layer, an activation function layer and a pooling layer; the activation function in the activation function layer is a rule function. The area recommendation network comprises a first convolution layer, a second convolution layer and a third convolution layer, wherein the second convolution layer and the third convolution layer are connected with an output end of the first convolution layer, an output end of the second convolution layer is connected with a normalization index function layer, and an output end of the normalization index function layer and an output end of the third convolution layer are connected with a combined layer. The classification network comprises a mapping layer, a first convolutional neural network, a second convolutional neural network and a CRF layer, wherein the mapping layer is used for inputting an image block to be detected and the field of the image block to be detected, which are output by a regional recommendation network, into the mapping layer, mapping is carried out to obtain an image matrix, the image matrix is input into the first convolutional neural network layer to obtain a feature vector, the feature vector is respectively input into the second convolutional neural network layer and the CRF layer, position regression processing is carried out through the second convolutional neural network layer, and whether the probability of target features is available, namely whether the probability of cracks is available is judged based on the CRF layer.
The method comprises the following specific processes:
in the feature extraction network, the extraction of features is realized by five times of convolution processing, wherein in the convolution process, the size of each convolution kernel is 3 multiplied by 3, and the above arrangement can achieve the same receptive field as 5 multiplied by 5 and 7 multiplied by 7 large convolutions while reducing the calculation amount by combining a plurality of convolution kernels with small sizes. In this embodiment, the step size of the convolution operation is 1, and the size of the boundary padding is also 1, so that the width and height of the image after each convolution do not change. Each pooling layer adopts maximum pooling, the size of each pooling layer is 2 multiplied by 2, and the step length is also 2, so that the width and the height of each image are reduced to half of the original width and height after each pooling. Therefore, after passing through the feature extraction network, the RGB color image with the original size of (h, w, 3) will become a feature map with the size of (h/16, w/16, 5112).
The characteristic diagram is firstly divided into two paths after convolution with the step length and the boundary filling being 1 through 512 3 multiplied by 3 in the regional recommendation network. The first pass operates through 18 1 × 1 convolutions and softmax. The purpose of the 1 × 1 convolution is to change the dimension of the feature map, i.e. the number of channels of the feature map is changed to 18, which corresponds to the probability (Pfg, pbg) that 9 anchor points preset at each point of the feature map belong to the foreground and the background, respectively. The second path outputs the corrected positions of the anchor points through the second path as follows by performing 36 1 × 1 convolution operations, changing the number of the channels of the characteristic diagram into 36, and setting the coordinates and the width and the height of the original preset anchor points as (px, py, pw, ph) corresponding to 4 position coordinate correction values (tx, ty, tw, th) of 9 anchor points:
wherein, g x ，g y ,g w ,g h The center coordinates, width and height of the anchor point after correction. The combined layer is a set of a series of operations, and the function of the combined layer is to combine the results obtained by the two-way convolution. This layer at first carries out the size with exceeding the anchor point of original drawing size and cuts to guarantee that all anchor points are all in the original drawing, reject some undersize and the too big anchor point of aspect ratio again, utilize the output of first way to sort these anchor points from high to low after, keep K anchor points before, screen through non-maximum suppression algorithm at last, keep M candidate regions.
The classification and network takes each generated candidate region as input, and it is necessary to determine whether the candidate region is a crack or not and perform regression correction of the position coordinates of the candidate region. In the classification process, firstly, each candidate area and surrounding image blocks with the same size as the area are taken, the number of the image blocks is 9, the 9 image blocks are used as input, feature extraction is firstly carried out through the convolutional neural network to obtain 9 feature vectors X, then the 9 feature vectors are used as input and are sent into the CRF, the conditional probability of each image block label Y is calculated, the label Y is a crack, and therefore the type of the candidate area is judged. The second path is similar to the area recommendation network, the position coordinate correction value of each candidate area is directly obtained through a convolutional neural network, the probability containing the Y label is obtained through a variation inference mode, and whether a crack exists is determined based on the probability. The method comprises the following specific steps:
based on the above, a conditional probability distribution model P (Y | X) is set, which is used to represent a conditional probability distribution of one set of output random variables Y given the other set of input random variables X. In this embodiment, the probability distribution is represented by an undirected graph, nodes in the undirected graph represent random variables, and edges in the graph represent the dependency relationships between the random variables. Obtaining a joint probability distribution about the undirected graph based on the undirected graph, wherein the joint probability distribution can be factored on a maximum clique thereof, and the joint probability distribution can be expressed as follows:
wherein, the first and the second end of the pipe are connected with each other,
to normalize the factors, the effect is to ensure that the resulting P (Y | X) is a reasonable probability distribution. X is composed of a series of random variables (X) 1 ,X 2 ,L,X N ) Composition, representing a given image block, in the present embodiment, if the number of image blocks is 9, N =9,x represents a 3 × 3 image block. Y is composed of a series of random variables (Y) 1 ,Y 2 ,L,Y N )，Y i The value range of the label of the ith image block is {0,1}, wherein 0 represents no crack and 1 represents a crack. E (Y, X) is a potential function, which indicates that Y takes a certain set of costs of a specific value under a given X condition, in this embodiment, the potential function is a binary potential function, and in this embodiment, cosine similarity is used to define the binary potential function, which has the following form:
wherein u (Y) i ,Y j ) For the tag compatibility function, when Yi = Yj, u (Y) i ,Y j ) =1, otherwise it is 0.
And acquiring whether the result has the crack or not based on the joint probability distribution.
S350, sending the detection result to the data storage terminal based on a storage strategy; and the terminal platform acquires the data in the data storage terminal based on an acquisition strategy.
According to the technical scheme, the inspection point is accurately acquired in the inspection process and whether abnormal identification exists in the inspection point is achieved by setting the functional modules in the system. The efficiency of patrolling and examining is improved to the discernment precision to the anomaly in patrolling and examining has been improved.
Referring to fig. 3, the above methods may be integrated into an electronic device 300 comprising a memory 310, a processor 320 and a computer program stored in the memory and executable on the processor, wherein the processor performs the data sharing method.
In this embodiment, the memory, the processor and the communication unit are directly or indirectly electrically connected to each other to realize data transmission or interaction. For example, the components may be electrically connected to each other via one or more communication buses or signal lines. The memory is used for storing specific information and programs, and the communication unit is used for sending the processed information to the corresponding user side.
In the embodiment, the storage module is divided into two storage areas, wherein one storage area is a program storage unit, and the other storage area is a data storage unit. The program storage unit is equivalent to a firmware area, the read-write authority of the area is set to be a read-only mode, and data stored in the area cannot be erased and changed. And the data in the data storage unit can be erased or read and written, and when the capacity of the data storage area is full, the newly written data can overwrite the earliest historical data.
The Memory may be, but is not limited to, a Random Access Memory (RAM), a Read Only Memory (ROM), a Programmable Read-Only Memory (PROM), an Erasable Read-Only Memory (EPROM), an electrically Erasable Read-Only Memory (EEPROM), and the like.
The processor may be an integrated circuit chip having signal processing capabilities. The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but may also be a Digital Signal Processor (DSP)), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware components. The various methods, steps and logic blocks disclosed in the embodiments of the present invention may be implemented or performed. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
It should be understood that the technical terms which are not noun-nounced in the above-mentioned contents are not limited to the meanings which can be clearly determined by those skilled in the art from the above-mentioned disclosures.
The skilled person can determine some preset, reference, predetermined, set and preference labels of technical features/technical terms, such as threshold, threshold interval, threshold range, etc., without any doubt according to the above disclosure. For some technical characteristic terms which are not explained, the technical solution can be clearly and completely implemented by those skilled in the art by reasonably and unambiguously deriving the technical solution based on the logical relations in the previous and following paragraphs. The prefixes of unexplained technical feature terms, such as "first," "second," "example," "target," and the like, may be unambiguously derived and determined from the context. Suffixes of technical feature terms not explained, such as "set", "list", etc., can also be derived and determined unambiguously from the preceding and following text.
The above disclosure of the embodiments of the present application will be apparent to those skilled in the art from the above description. It should be understood that the process of deriving and analyzing technical terms, which are not explained, by those skilled in the art based on the above disclosure is based on the contents described in the present application, and thus the above contents are not an inventive judgment of the overall scheme.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered merely illustrative and not restrictive of the broad application. Various modifications, adaptations, and alternatives may occur to one skilled in the art, though not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present application and thus fall within the spirit and scope of the exemplary embodiments of the present application.
Also, this application uses specific terminology to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various portions of this specification are not necessarily all referring to the same embodiment. Furthermore, certain features, structures, or characteristics may be combined as suitable in at least one embodiment of the application.
In addition, those skilled in the art will recognize that the various aspects of the present application may be illustrated and described in terms of any number of patentable categories or situations, including any new and useful combination of procedures, machines, products, or materials, or any new and useful modifications thereof. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as a "unit", "component", or "system". Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, embodied in at least one computer readable medium.
A computer readable signal medium may comprise a propagated data signal with computer program code embodied therein, for example, on a baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, and the like, or any suitable combination. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code on a computer readable signal medium may be propagated over any suitable medium, including radio, electrical cable, fiber optic cable, RF, or the like, or any combination of the preceding.
Computer program code required for the execution of aspects of the present application may be written in any combination of one or more programming languages, including object oriented programming such as Java, scala, smalltalk, eiffel, JADE, emerald, C + +, C #, VB.NET, python, and the like, or similar conventional programming languages, such as the "C" programming language, visual Basic, fortran 2003, perl, COBOL 2002, PHP, ABAP, dynamic programming languages such as Python, ruby, and Groovy, or other programming languages. The programming code may execute entirely on the user's computer, as a stand-alone software package, partly on the user's computer, partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).
Additionally, the order of the process elements and sequences described herein, the use of numerical letters, or other designations are not intended to limit the order of the processes and methods unless otherwise indicated in the claims. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it should be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware means, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
It should also be appreciated that in the foregoing description of embodiments of the present application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of at least one embodiment of the invention. This method of disclosure, however, is not intended to imply that more features are required than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
Claims (10)
1. A bridge electronic inspection system is characterized by comprising a handheld terminal, a data processing terminal, a data storage terminal and a plurality of data marking points; the handheld terminal comprises a display module, a communication establishing module and a data acquisition module, wherein the communication establishing module receives marking signals of the data marking points in a preset range and establishes communication, and the data acquisition module acquires real-time data in the preset range corresponding to the data marking points based on the establishment of communication; the data processing terminal processes the real-time data acquired by the data acquisition module to obtain a processing result, and the data storage terminal is used for storing the processing result and the corresponding data processing terminal label and the data marking point label; a data detection model is configured in the data processing terminal and used for detecting the real-time data to obtain a processing result; the data storage terminal comprises a plurality of storage spaces corresponding to the data mark points, the storage spaces are used for storing data corresponding to the data mark points, each storage space comprises at least two storage subspaces, and the storage subspaces are used for correspondingly storing the processing result types.
2. The bridge electronic inspection system according to claim 1, further comprising a terminal platform, wherein the terminal platform is connected with the data storage terminal, and the terminal platform collects the storage data in the data storage terminal based on a collection strategy.
3. The bridge electronic inspection system according to claim 2, wherein the collection policies include a first collection policy and a second collection policy, the first collection policy and the second collection policy being respectively configured within the two storage subspaces.
4. The bridge electronic inspection system according to claim 3, wherein the storage subspace includes a first storage subspace and a second storage subspace, and a first acquisition strategy and a second acquisition strategy are respectively configured; the first acquisition strategy is synchronous acquisition, and the second acquisition strategy is asynchronous acquisition.
5. The bridge electronic inspection system according to claim 1, wherein the handheld terminal further includes a map marking module, the map marking module is configured with a plurality of layer maps, and any one of the layer maps is provided with a code corresponding to a data marking point; and the data mark points are configured with NFC digital tags, the NFC digital tags establish communication with the communication establishment module within a preset range, determine the map layer map based on the unique codes of the NFC digital tags, and display the map layer map and the corresponding mark point coordinates on the display module.
6. A bridge electronic inspection method applied to the bridge electronic inspection system according to any one of claims 1 to 5, the method comprising:
the communication establishing module acquires the communication signals of the data marking points in a preset range and establishes communication with the data marking points;
the map marking module establishes a unique code of the corresponding data marking point based on communication to determine a corresponding map layer map, and displays the map layer map and the coordinate of the corresponding data marking point on the display module;
the data acquisition module acquires real-time data in a preset range of the corresponding data marking points based on the establishment of communication, wherein the real-time data comprises real-time image data meeting the preset range;
sending the real-time image data to the data processing terminal, and performing anomaly detection on the real-time image data based on a data detection model in the data processing terminal to obtain a detection result;
sending the detection result to the data storage terminal based on a storage strategy;
and the terminal platform acquires the data in the data storage terminal based on an acquisition strategy.
7. The bridge electronic inspection method according to claim 6, wherein the data acquisition module acquires real-time data within a preset range of corresponding data marking points based on the establishment of communication, and the method comprises the following steps:
the data acquisition module determines a target acquisition frame and a target acquisition standard image based on the unique code of the data marking point;
based on the target collection frame and the target collection standard image, the target collection standard image is used for carrying out image collection on the object to be inspected corresponding to the data marking point, so as to obtain the real-time image data, and the method specifically comprises the following steps:
identifying the real-time image data based on the target acquisition frame to obtain an identification result;
judging whether the real-time image data meets preset conditions or not based on the recognition result, and specifically comprising the following steps of:
carrying out binarization processing on the real-time image data to obtain preprocessed image data;
segmenting the preprocessed image data to obtain a target processed image;
and extracting edge coordinates of the target processing image, comparing the edge coordinates with preset standard edge coordinates, and determining whether the real-time image data meets preset conditions based on a comparison threshold.
8. The bridge electronic inspection method according to claim 6, wherein the real-time image data is subjected to anomaly detection based on a data detection model in the data processing terminal to obtain a detection result, the data detection model comprises a feature extraction network, a regional recommendation network and a classification network, and the method comprises the following steps:
extracting features in the real-time image data based on a feature extraction network to obtain a target feature map;
positioning a plurality of candidate areas containing detection targets in the target feature map based on an area recommendation network;
and acquiring the spatial characteristics of the candidate regions, modeling, judging whether the candidate regions contain the probability distribution of the target features or not based on the model, and determining whether the candidate regions contain the target features or not based on the probability distribution.
9. The bridge electronic inspection method according to claim 8, wherein the feature extraction network includes five sets of convolutional neural networks, and any convolutional neural network includes at least one convolutional layer, an activation function layer and a pooling layer; the activation function in the activation function layer is a rule function.
10. The bridge electronic inspection method according to claim 8, wherein the area recommendation network includes a first convolution layer, and a second convolution layer and a third convolution layer connected to an output end of the first convolution layer, an output end of the second convolution layer is connected to a normalized index function layer, and an output end of the normalized index function layer and an output end of the third convolution layer are connected to a bonding layer.
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