CN111160270A - Bridge monitoring method based on intelligent video identification - Google Patents
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Abstract
A bridge monitoring method based on intelligent video identification relates to the field of bridge monitoring and comprises the following steps: the method comprises the steps of intercepting a vehicle passing picture in a vehicle passing video, carrying out image recognition on the vehicle passing picture according to a target detection model so as to extract a real-time image and related information of a preset type of vehicle, carrying out image recognition on the real-time image according to a weight classification model so as to obtain weight classification information of the preset type of vehicle, and sending alarm information containing the weight classification information to a preset bridge monitoring system and carrying out danger analysis by combining monitoring data when the weight classification information is greater than a preset threshold value. The invention has the beneficial effects that: the vehicle dynamic weighing system does not need to be additionally installed, and the recognition, recording and analysis of the overweight vehicle can be realized based on video monitoring by utilizing the preset weighing system.
Description
Technical Field
The invention relates to the technical field of bridge monitoring, in particular to a bridge monitoring method based on intelligent video identification.
Background
The bridge is a key component of traffic transportation, and along with the gradual promotion of 'strong traffic countries', a large number of bridges are created. However, some bridge engineering projects are damaged before reaching the design service life, and the overload transportation is the main reason, and the formation of ruts and network cracks on bridge roads, the sudden increase of dangerous bridges and the like are rarely caused by the fatigue strength of vehicle load, and most of the bridge engineering projects are caused by overload. Therefore, identifying the overweight vehicle of the bridge and analyzing the dangerousness of the overweight vehicle are very important for the long-term healthy and stable operation of the bridge.
In the prior art, a vehicle dynamic weighing system (WIM) is generally used for acquiring information such as passing time of a running vehicle, passing images, license plate numbers, axle weight, total weight, vehicle speed, axle distance and the like, a sensor is used for acquiring structural response of a bridge under the action of external load, and the two data are combined to monitor an overweight vehicle passing through the bridge. Moreover, for a bridge without a vehicle dynamic weighing system, the overweight vehicle cannot be monitored in real time only through installed video monitoring.
Disclosure of Invention
Aiming at the defects in the prior art, the invention provides a bridge monitoring method based on intelligent video identification, which utilizes the existing system to realize real-time identification, recording and analysis of overweight vehicles and improve the long-term operation safety of bridges.
In order to achieve the above purposes, the technical scheme is as follows:
a bridge monitoring method based on intelligent video identification comprises the following steps:
acquiring a plurality of first acquisition information and a plurality of second acquisition information by using a preset weighing system, wherein the first acquisition information comprises a first image and type marking information associated with a plurality of preset types of vehicles contained in the first image, the second acquisition information comprises a second image and weight information associated with a unique preset type of vehicle contained in the second image, processing according to the plurality of first acquisition information to obtain a target detection model, and processing according to the plurality of second acquisition information to obtain a weight classification model;
intercepting a vehicle passing image in a vehicle passing video, and carrying out image recognition on the vehicle passing image according to the target detection model so as to extract a real-time image of a preset type of vehicle from the vehicle passing image;
carrying out image recognition on the real-time image according to the weight classification model to obtain weight classification information of a preset type of vehicle;
judging whether the weight classification information is larger than a preset threshold value:
and if so, sending alarm information containing the weight classification information to a preset bridge monitoring system, and carrying out risk analysis by the preset bridge monitoring system according to the alarm information and monitoring data which is stored by the preset bridge monitoring system and is associated with the alarm information to obtain a bridge passing risk coefficient of a corresponding preset type vehicle.
Preferably, the preset type of vehicle includes a predefined large vehicle.
Preferably, the first image includes a plurality of vehicles, and at least part of the plurality of vehicles is a preset type vehicle;
the second image comprises a vehicle, and the vehicle is a preset type vehicle.
Preferably, a plurality of pieces of the first acquisition information form a first data set, and the target detection model is obtained by training according to the first data set;
the first data set comprises the first acquisition information with the total number more than 10000.
Preferably, the specific steps of the preset weighing system in acquiring the first acquisition information are as follows:
acquiring the first image;
manually labeling the region position coordinates of preset type vehicles in all vehicles of the first image through rectangular frames respectively to obtain a plurality of real labeling frames and coordinate positions, wherein all the real labeling frames and all the coordinate positions of the first image form the type labeling information;
the first image and the type standard information constitute the first acquisition information.
Preferably, the coordinate position is a position coordinate of a vertex at the top left corner and a position coordinate of a vertex at the bottom right corner of the real labeling frame.
Preferably, a plurality of pieces of the second collected information form a second data set, and the weight classification model is obtained through training according to the second data set;
the second data set comprises the second acquisition information with the total number more than 10000.
Preferably, the real-time weight classification model outputs results of (0,10t), (10t,20t), (20t,30t), (30t,40t), (40t,50t), (50t,60t) or (60t, -).
Preferably, the algorithm adopted by the target detection model is SSD, Yolo, R-CNN, Fast R-CNN or FasterR-CNN;
the weight classification model adopts an algorithm of VGG, ResNet, MobileNet or inclusion.
Preferably, the monitoring data includes displacement data, deflection data and strain data associated with the predetermined type of vehicle.
The invention has the beneficial effects that: the method comprises the steps of acquiring data by using an existing preset weighing system without additionally installing a vehicle dynamic weighing system, processing the data to obtain a target detection model and a weight classification model, identifying vehicle passing images acquired by video monitoring in real time through the target detection model and the weight classification model to obtain weight classification information of preset type vehicles in the vehicle passing images, performing danger analysis by using the existing preset bridge monitoring system in combination with the weight classification information and the monitoring data which exceed preset thresholds to obtain bridge passing risk coefficients of corresponding preset type vehicles, wherein the preset type vehicles can comprise overweight vehicles, and accordingly identifying, recording and analyzing all road passing vehicles including the overweight vehicles.
Drawings
Fig. 1 is a flowchart of a bridge monitoring method based on intelligent video identification in the embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific examples described herein are intended to be illustrative only and are not intended to be limiting. Moreover, all other embodiments that can be derived by a person skilled in the art from the embodiments of the present invention without making any creative effort belong to the protection scope of the present invention.
The present invention will be described in further detail with reference to the accompanying drawings and examples.
As shown in fig. 1, a bridge monitoring method based on intelligent video identification includes:
and step S1, acquiring a plurality of first acquisition information and a plurality of second acquisition information by using a preset weighing system. The first collected information includes a first image and type label information associated with a plurality of preset types of vehicles included in the first image, and the second collected information includes a second image and weight information associated with a unique preset type of vehicle included in the second image. The first images comprise a plurality of vehicles, at least part of the vehicles are preset types of vehicles, and the type standard information corresponding to each first image comprises related information of all the preset types of vehicles in the first image. The second collected information includes a second image and weight information associated with a unique preset type of vehicle included in the second image, that is, the second image includes only one preset type of vehicle, and the weight information is the weight information of the unique preset type of vehicle in the second image. The preset type of vehicle is a predefined large vehicle (open type). In a first data set composed of first acquisition information, the total number of the first acquisition information is greater than 10000. And in a second data set formed by a plurality of pieces of second acquisition information, the total number of the pieces of second acquisition information is more than 10000 pieces of second acquisition information.
And step S2, processing according to the plurality of first collected information to obtain a target detection model, and processing according to the plurality of second collected information to obtain a weight classification model. The target detection model adopts an algorithm of SSD, Yolo, R-CNN, Fast R-CNN or Faster R-CNN. And the SSD multi-target detection algorithm based on deep learning is preferred, so that the SSD is high in speed and high in accuracy. Other algorithms can be selected by the target detection model according to actual requirements. The algorithm used by the weight classification model is VGG, ResNet, MobileNet or inclusion, preferably inclusion v 3. Other algorithms can be selected by the weight classification model according to actual requirements.
And step S3, intercepting the vehicle passing image in the vehicle passing video, and carrying out image recognition on the vehicle passing image according to the target detection model.
Step S4, judging whether a large vehicle exists in the intercepted vehicle passing image, and if so, turning to step S5; if the judgment result is no, go to step S6.
Step S5, extracting the real-time image of the preset type of vehicle from the vehicle passing image, and then going to step S7.
Step S6, the intercepted vehicle passage image is discarded, and the process proceeds to step S3.
And step S7, carrying out image recognition on the real-time image according to the weight classification model to obtain weight classification information of the preset type of vehicle.
Step S8, judging whether the weight value corresponding to the weight classification information is larger than a preset threshold value, if so, turning to step S9; if the judgment result is no, go to step S10. The preset threshold value is the traffic limiting weight of the bridge through which the vehicle passes at that time.
And S9, storing and recording the weight classification information, sending alarm information to a preset bridge monitoring system, wherein the alarm information comprises the weight classification information, and then turning to the step S11. The weight classification model for vehicle weight prediction adopts a classification algorithm, vehicle data is an ordered sequence, the vehicle data is artificially classified into 7 types of (0,10t), (10t,20t), (20t,30t), (30t,40t), (40t,50t), (50t,60t), (60t, -), and a large vehicle weight prediction model is trained by using pictures and weight category information, and the weight prediction result is one of 7 categories.
Step S10 ends with the weight classification information neither stored nor recorded.
And step S11, after the preset bridge monitoring system receives the alarm information, performing risk analysis by combining the monitoring data of the preset type of vehicle corresponding to the alarm information stored by the preset bridge monitoring system to obtain the bridge passing risk coefficient of the preset type of vehicle. The monitoring data includes displacement data, deflection data, and strain data associated with a predetermined type of vehicle. And comparing the monitoring data with a set threshold value so as to judge the danger of the passing vehicle.
In the embodiment, a vehicle dynamic weighing system is not required to be additionally installed, identification, recording and analysis of the overweight vehicle can be realized based on the existing preset weighing system, identification of the overweight vehicle is realized by using a computer vision technology, and meanwhile, the overweight vehicle is linked with bridge structure monitoring data of the preset bridge monitoring system, so that the danger is judged in real time, and a guarantee is provided for long-term healthy and stable operation of the bridge.
When a data set is collected and a target detection model and a weight classification model are obtained through training according to the data set, position coordinates of an open area of a large vehicle in a vehicle image are manually marked by a rectangular frame to obtain a real marking frame of the position of the area of the large vehicle and 4 coordinate values of the real marking frame in each picture, wherein the 4 coordinate values are respectively the position coordinates of a vertex at the upper left corner and the position coordinates of a vertex at the lower right corner of the vehicle area, information of the real marking frame of each picture is stored as an xml file (namely type marking information), all large vehicle pictures and corresponding xml marking result files form a large vehicle marking data set (namely a first data set), and a target detector for identifying the large vehicle is obtained through training by utilizing the first data set. And training by using the second data set to obtain a weight classification model for identifying the weight classification of the large-sized vehicle.
The present invention is not limited to the embodiments, and it is apparent to those skilled in the art that various modifications and improvements can be made without departing from the principle of the present invention, and these modifications and improvements are also considered to be within the scope of the present invention. Those not described in detail in this specification are within the skill of the art.
Claims (10)
1. A bridge monitoring method based on intelligent video identification is characterized in that: the bridge monitoring method comprises the following steps:
acquiring a plurality of first acquisition information and a plurality of second acquisition information by using a preset weighing system, wherein the first acquisition information comprises a first image and type marking information associated with a plurality of preset types of vehicles contained in the first image, the second acquisition information comprises a second image and weight information associated with a unique preset type of vehicle contained in the second image, processing according to the plurality of first acquisition information to obtain a target detection model, and processing according to the plurality of second acquisition information to obtain a weight classification model;
intercepting a vehicle passing image in a vehicle passing video, and carrying out image recognition on the vehicle passing image according to the target detection model so as to extract a real-time image of a preset type of vehicle from the vehicle passing image;
carrying out image recognition on the real-time image according to the weight classification model to obtain weight classification information of a preset type of vehicle;
judging whether the weight classification information is larger than a preset threshold value:
and if so, sending alarm information containing the weight classification information to a preset bridge monitoring system, and carrying out risk analysis by the preset bridge monitoring system according to the alarm information and monitoring data which is stored by the preset bridge monitoring system and is associated with the alarm information to obtain a bridge passing risk coefficient of a corresponding preset type vehicle.
2. The bridge monitoring method of claim 1, wherein: the preset type of vehicle comprises a predefined large vehicle.
3. The bridge monitoring method of claim 1, wherein: the first image comprises a plurality of vehicles, and at least part of the vehicles are vehicles of a preset type;
the second image comprises a vehicle, and the vehicle is a preset type vehicle.
4. The bridge monitoring method of claim 1, wherein: a plurality of pieces of first acquisition information form a first data set, and the target detection model is obtained through training according to the first data set;
the first data set comprises the first acquisition information with the total number more than 10000.
5. The bridge monitoring method of claim 1, wherein: the specific steps of the preset weighing system in acquiring the first acquisition information are as follows:
acquiring the first image;
manually labeling the region position coordinates of preset type vehicles in all vehicles of the first image through rectangular frames respectively to obtain a plurality of real labeling frames and coordinate positions, wherein all the real labeling frames and all the coordinate positions of the first image form the type labeling information;
the first image and the type standard information constitute the first acquisition information.
6. The bridge monitoring method of claim 5, wherein: and the coordinate positions are the position coordinate of the vertex at the upper left corner and the position coordinate of the vertex at the lower right corner of the real labeling frame.
7. The bridge monitoring method of claim 1, wherein: forming a second data set by a plurality of pieces of second acquired information, and training according to the second data set to obtain the weight classification model;
the second data set comprises the second acquisition information with the total number more than 10000.
8. The bridge monitoring method of claim 1, wherein: the real-time weight classification model outputs the result as (0,10t), (10t,20t), (20t,30t), (30t,40t), (40t,50t), (50t,60t) or (60t, -).
9. The bridge monitoring method of claim 1, wherein: the target detection model adopts an algorithm of SSD, Yolo, R-CNN, Fast R-CNN or FasterR-CNN;
the weight classification model adopts an algorithm of VGG, ResNet, MobileNet or inclusion.
10. The bridge monitoring method of claim 1, wherein: the monitoring data includes displacement data, deflection data, and strain data associated with the predetermined type of vehicle.
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| CN114396877A (en) * | 2021-11-19 | 2022-04-26 | 重庆邮电大学 | Intelligent three-dimensional displacement field and strain field measurement method oriented to material mechanical properties |
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