Research And Development Of Bridge Deflection Monitoring System Based On Image Processing

As the economy develops and the pace of urbanization accelerates,the importance of bridges in transportation continues to increase.Overall,the installation of bridges is generally in good condition,but safety accidents on bridges are still uncommon,causing inestimable economic and property losses to the country and the people.In order to ensure the safety of the bridge,monitoring it during the construction and use of the bridge is an important link,and deflection measurement is an important part of bridge safety monitoring and an important indicator for evaluating the safety of the bridge.Based on the above problems,this article aims to study a bridge safety monitoring system based on deep learning and image processing.On the one hand,by installing a target disk on the bridge structure,the deflection of the bridge structure is monitored in real time.On the other hand,by identifying the vehicles traveling on the bridge deck,the load on the bridge deck can be roughly estimated.Integrate and analyze the monitoring results of the two aspects to judge the safety status of the bridge in real time.The main research contents of this article include the following:(1)Based on the current status of bridge structure health monitoring and bridge deflection measurement,this paper has developed a bridge structure health monitoring system,which has been designed from the aspects of basic system composition,equipment installation method and system work flow.The system can realize the actual deflection measurement and vehicle load estimation through images,and then judge the safety status of the bridge.In order to prove the feasibility of the system and the accuracy of the monitoring,the target moving distance measurement experiment and the vehicle distance measurement experiment were carried out.The experimental results prove that the accuracy of the system can meet the requirements.Through the comparison of experimental data,the factors that affect the accuracy are summarized,which has important guiding significance for the actual use of the system.(2)In this paper,the target disk is designed.Compared with the cross-shaped target disk,it is found that the round target disk is easier to fit,so the shape of the target disk is selected as a circle.This paper sets a circle of identification marks on the periphery of the target disk to give each target disk a unique identity,which improves the recognition of the target disk.In the actual application process,the identity information of the target disc is connected with its location,and while the target displacement is monitored through the target disc,the displacement data can be directly corresponded to the target location,which greatly facilitates the monitoring work.(3)This article studies the principles and methods of deep learning technology,makes a sample training set that meets the conditions of this article,selects appropriate training parameters to train the network model,and establishes a YOLO network recognition model that can accurately identify targets such as vehicles and target disks..Through the identification and positioning of the target,it can effectively eliminate the interference of other information in the image,improve the efficiency and accuracy of image processing,and facilitate the further processing and analysis of the image.(4)This paper designs the target disc moving distance measurement algorithm,selects the appropriate image processing method to eliminate the interference information in the image through the comparison of the actual processing effect,and uses the target features for in-depth screening,and finally gets the ideal processing The effect is that only the target information is retained in the image.When calculating the distance,the algorithm corrects the camera tilt angle.Experimental results prove that the algorithm has high accuracy.