CN111551562A - Bridge pavement structure damage identification method and system - Google Patents
Bridge pavement structure damage identification method and system Download PDFInfo
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Abstract
The invention discloses a bridge pavement structure damage identification method and a system, which are used for collecting bridge pavement video data and vehicle position data and realizing the matching of time and space dimensions of the data; receiving data transmitted by a data acquisition system, and realizing the processing of structured data and unstructured data through a GPS data module and a video data processing module; and finally, the structural damage identification subsystem identifies the damage of the road surface through the abnormal GPS identification module, the video correction module and the structural damage identification module and issues a damage prompt. The invention utilizes the video acquisition equipment and the GPS acquisition equipment to acquire the multi-source data set, can accurately identify the structural damage of the bridge pavement and provide reliable information for the bridge operation and maintenance management department, thereby formulating a reasonable and efficient operation and maintenance scheme and management measures and lightening the intensity of workers.
Description
Technical Field
The invention relates to the field of intelligent traffic, in particular to a bridge pavement structure damage identification method and system.
Background
The bridge is a key node in a traffic network, and the health condition of the bridge directly influences the safe operation of vehicles. In China, the overload operation of vehicles is ubiquitous, and a bridge structure, particularly a pavement structure is frequently damaged within the service life, so that the performance degradation speed is accelerated, the bearing capacity of the structure is rapidly reduced, and the driving risk is greatly improved. The health monitoring of the bridge structure can effectively identify various damages of the bridge structure and provide accurate information for the evaluation of the state of the bridge structure.
At present, bridge pavement damage is mainly realized in a manual inspection mode, the subjectivity is high, the workload is high, the cost is high, the quantity of bridges in China is large, the workload is high, and the detection can also influence the road traffic operation to cause traffic jam. In a word, it is very important to improve the automation level of bridge pavement damage detection.
Disclosure of Invention
The invention provides a bridge pavement structure damage identification method and system, which fully utilize multi-source data fusion to improve the precision of pavement damage, improve the detection efficiency and precision and save the detection cost.
In order to solve the technical problems, the technical scheme of the invention is as follows:
a bridge pavement structure damage identification method comprises the following steps:
s1: the method comprises the steps that image data of a bridge pavement are collected through roadside video collecting equipment, the bridge pavement is divided into grids, each grid is matched with a GPS data point corresponding to bridge geographic information through a data matching module, and the GPS data and the image data are stored in a data storage module;
s2: slicing the image data obtained in the step S1 by using a video data processing module to obtain an image of each grid, determining the damage condition of the pavement of each grid by using a manual calibration method, dividing the image with the pavement damage into damage samples, dividing the image without the pavement damage into normal samples, marking the normal samples as 0, and marking the damage samples as 1;
s3: the GPS data processing module utilizes the damage sample and the normal sample to construct a training data set, utilizes the training data set to train a model to obtain a trained model, the model is used for identifying the damage of the pavement structure, the input of the model is all GPS data in the grid, the output is the state of the grid, namely 0 or 1, 0 represents that the pavement is normal, and 1 represents that the pavement is damaged;
s4: the vehicle passes through wireless communication network with real-time GPS data transmission to unusual GPS identification module, and the video school is to the image section that the road side video acquisition equipment gathered, and the damage situation on structural damage identification module output road surface includes two kinds: (ii) impaired and normal; if the output is normal, the output is normal; and if the damage is caused, map matching is carried out, the position of the damage is determined, meanwhile, a manager is informed, the manager watches the video acquisition image to determine whether the damage is caused, if the damage is caused, the type and the position of the damage are reported, if the damage is caused, the normal sample is calibrated, the normal output is carried out, meanwhile, the manager is informed, the manager watches the video acquisition image to determine whether the damage is caused, if the damage is caused, the type and the position of the damage are reported, if the damage is caused, the normal sample is calibrated, and the normal output.
Preferably, the GPS data points in step S1 are collected by an onboard GPS device.
Preferably, the size of the grid after the gridding division of the bridge pavement is 0.1m × 0.1 m.
Preferably, the image sliced in step S2 is in accordance with the mesh size.
Preferably, the model training method is any one of a neural network, a support vector machine and a classification tree or other methods.
Preferably, the model identifies both structural damage and the location of the damage.
Preferably, the structural damage includes cracks, subsidence and rutting.
Preferably, the GPS data includes three variables of time identification, longitude, and latitude.
A bridge pavement structure damage identification system based on the identification method comprises the following steps:
the data storage module is used for storing GPS data acquired by vehicle-mounted GPS equipment and image data acquired by roadside equipment;
the data matching module is used for acquiring data from the data storage module and matching the GPS data and the video slice data into the divided grids;
the video data processing module is used for slicing video data, manually calibrating the type of the video slices, calibrating the state of the divided grids according to the positions of the video slices and transmitting the calibration result to the GPS data processing module;
the GPS data processing module is used for receiving the result of the data matching module, converging the GPS data in each grid as an input variable, training the structural damage recognition model and transmitting the trained recognition model to the abnormal GPS recognition module;
the abnormal GPS identification module takes GPS data collected in real time as input and judges the state of the grid in real time based on an identification model;
the video checking module is used for extracting video slices of a specific grid, further confirming the state of the grid and transmitting the result to the structural damage identification module;
and the structural damage identification module is used for transmitting the final identification result.
Compared with the prior art, the technical scheme of the invention has the beneficial effects that:
1. the invention integrates the roadside acquisition equipment data and the vehicle-mounted acquisition equipment data, and improves the detection accuracy;
2. the invention improves the automation level of detection based on the machine learning method;
3. the method can accurately identify the structural damage of the bridge pavement and provide reliable management basis for traffic planning and operation management departments.
Drawings
FIG. 1 is a schematic flow chart of the method of the present invention.
FIG. 2 is a method for bridge meshing according to the present invention.
Fig. 3 is a schematic diagram of the system structure of the present invention.
Detailed Description
The drawings are for illustrative purposes only and are not to be construed as limiting the patent;
for the purpose of better illustrating the embodiments, certain features of the drawings may be omitted, enlarged or reduced, and do not represent the size of an actual product;
it will be understood by those skilled in the art that certain well-known structures in the drawings and descriptions thereof may be omitted.
The technical solution of the present invention is further described below with reference to the accompanying drawings and examples.
Example 1
The embodiment provides a method for identifying damage to a bridge pavement structure, as shown in fig. 1, which includes the following steps:
s1: the method comprises the steps that image data of a bridge pavement are collected by road side video collecting equipment, the bridge pavement is divided into grids, as shown in figure 2, each grid is matched with a GPS data point corresponding to bridge geographic information by a data matching module, and the GPS data and the image data are stored in a data storage module;
s2: slicing the image data obtained in the step S1 by using a video data processing module to obtain an image of each grid, determining the damage condition of the pavement of each grid by using a manual calibration method, dividing the image with the pavement damage into damage samples, dividing the image without the pavement damage into normal samples, marking the normal samples as 0, and marking the damage samples as 1;
s3: the GPS data processing module utilizes the damage sample and the normal sample to construct a training data set, utilizes the training data set to train a model to obtain a trained model, the model is used for identifying the damage of the pavement structure, the input of the model is all GPS data in the grid, and the output is the state of the grid, namely 0 or 1;
s4: the vehicle passes through wireless communication network with real-time GPS data transmission to unusual GPS identification module, and the video school is to the image section that the road side video acquisition equipment gathered, and the damage situation on structural damage identification module output road surface includes two kinds: (ii) impaired and normal; if the output is normal, the output is normal; and if the damage is caused, map matching is carried out, the damage position is determined, meanwhile, a manager is informed, the manager watches the video acquisition image to determine whether the damage is caused, if the damage is caused, the damage type and the damage position are reported, if the damage is caused, the normal sample is calibrated, and the normal sample is output.
The GPS data points in step S1 are collected by an onboard GPS device.
The size of the grid after the gridding division of the bridge pavement is 0.1m multiplied by 0.1 m.
The image sliced in step S2 corresponds to the mesh size.
The model training method is any one of a neural network, a support vector machine and a classification tree.
The model identifies both structural damage and the location of the damage.
The structural damage includes cracks, subsidence, and rutting.
The GPS data includes three variables of time identification, longitude, and latitude.
Example 2
The present embodiment provides a bridge pavement structure damage identification system based on the identification method described in embodiment 1, as shown in fig. 3, including a data acquisition subsystem, a data processing subsystem, and a structure damage identification subsystem, wherein:
the data acquisition subsystem includes:
the data storage module is used for storing GPS data acquired by vehicle-mounted GPS equipment and image data acquired by roadside equipment;
the data matching module is used for acquiring data from the data storage module and matching the GPS data and the video slice data into the divided grids;
the data processing subsystem includes:
the video data processing module is used for slicing video data, manually calibrating the type of the video slices, calibrating the state of the divided grids according to the positions of the video slices and transmitting the calibration result to the GPS data processing module;
the GPS data processing module is used for receiving the result of the data matching module, converging the GPS data in each grid as an input variable, training the structural damage recognition model and transmitting the trained recognition model to the abnormal GPS recognition module;
the structural damage identification subsystem includes:
the abnormal GPS identification module takes GPS data collected in real time as input and judges the state of the grid in real time based on an identification model;
the video checking module is used for extracting video slices of a specific grid, further confirming the state of the grid and transmitting the result to the structural damage identification module;
and the structural damage identification module is used for transmitting the final identification result.
The same or similar reference numerals correspond to the same or similar parts;
the terms describing positional relationships in the drawings are for illustrative purposes only and are not to be construed as limiting the patent;
it should be understood that the above-described embodiments of the present invention are merely examples for clearly illustrating the present invention, and are not intended to limit the embodiments of the present invention. Other variations and modifications will be apparent to persons skilled in the art in light of the above description. And are neither required nor exhaustive of all embodiments. Any modification, equivalent replacement, and improvement made within the spirit and principle of the present invention should be included in the protection scope of the claims of the present invention.
Claims (9)
1. A bridge pavement structure damage identification method is characterized by comprising the following steps:
s1: the method comprises the steps that image data of a bridge pavement are collected through roadside video collecting equipment, the bridge pavement is divided into grids, each grid is matched with a GPS data point corresponding to bridge geographic information through a data matching module, and the GPS data and the image data are stored in a data storage module;
s2: slicing the image data obtained in the step S1 by using a video data processing module to obtain an image of each grid, determining the damage condition of the pavement of each grid by using a manual calibration method, dividing the image with the pavement damage into damage samples, dividing the image without the pavement damage into normal samples, marking the normal samples as 0, and marking the damage samples as 1;
s3: the GPS data processing module utilizes the damage sample and the normal sample to construct a training data set, utilizes the training data set to train a model to obtain a trained model, the model is used for identifying the damage of the pavement structure, the input of the model is all GPS data in the grid, and the output is the state of the grid, namely 0 or 1;
s4: the vehicle passes through wireless communication network with real-time GPS data transmission to unusual GPS identification module, and the video school is to the image section that the road side video acquisition equipment gathered, and the damage situation on structural damage identification module output road surface includes two kinds: (ii) impaired and normal; if the output is normal, the output is normal; and if the damage occurs, performing map matching to determine the position of the damage.
2. The bridge pavement structure damage identification method according to claim 1, wherein the GPS data points in step S1 are collected by vehicle-mounted GPS equipment.
3. The method for identifying damage to the bridge pavement structure according to claim 2, wherein the size of the grid after gridding division of the bridge pavement is 0.1m x 0.1 m.
4. The bridge pavement structure damage identification method according to claim 3, wherein the image sliced in step S2 is in accordance with the mesh size.
5. The bridge pavement structure damage identification method according to claim 4, wherein the model training method is any one of a neural network, a support vector machine and a classification tree.
6. The bridge pavement structural damage identification method of claim 5, wherein the model identifies both structural damage and the location of damage.
7. The bridge pavement structural damage identification method of claim 6, wherein the structural damage comprises cracks, subsidence, and rutting.
8. The bridge pavement structure damage identification method according to claim 7, wherein the GPS data comprises three variables of time identification, longitude and latitude.
9. A bridge pavement structure damage identification system based on the identification method of claims 1 to 8, comprising:
the data storage module is used for storing GPS data acquired by vehicle-mounted GPS equipment and image data acquired by roadside equipment;
the data matching module is used for acquiring data from the data storage module and matching the GPS data and the video slice data into the divided grids;
the video data processing module is used for slicing video data, manually calibrating the type of the video slices, calibrating the state of the divided grids according to the positions of the video slices and transmitting the calibration result to the GPS data processing module;
the GPS data processing module is used for receiving the result of the data matching module, converging the GPS data in each grid as an input variable, training the structural damage recognition model and transmitting the trained recognition model to the abnormal GPS recognition module;
the abnormal GPS identification module takes GPS data collected in real time as input and judges the state of the grid in real time based on an identification model;
the video checking module is used for extracting video slices of a specific grid, further confirming the state of the grid and transmitting the result to the structural damage identification module;
and the structural damage identification module is used for transmitting the final identification result.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112487925A (en) * | 2020-11-25 | 2021-03-12 | 上海海事大学 | Bridge load damage identification method and system |
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Application publication date: 20200818 |