Research On The Overall Relative Change Monitoring System Of Cracks Based On Computer Visio

Cracks can reflect the early safety of a building structure.With the passage of time,cracks will deform and further affect the safety of the building structure.Therefore,it is necessary to closely monitor the cracks on the building structure.In recent years,with the development of image processing technology and deep learning technology,non-contact crack detection has become the main research direction.Because the crack images obtained by different image acquisition devices at different times have different angles,the overall change of the crack cannot be known when detecting the crack image.This paper designs a set of flow algorithm that can realize the overall change monitoring of cracks,which can register and fine tune the crack images obtained by different time and different image acquisition equipment,and analyze the overall change area of cracks.The main research work and research results of this paper are as follows:(1)Firstly,the angle problem of crack images obtained at different times and different acquisition equipment is solved.Aiming at the low accuracy of common image registration algorithms in the application scene of crack images,a new registration method is designed in this paper.According to the step flow of common registration methods,the two-dimensional code is used to identify the auxiliary crack image for registration.By identifying and locating the point coordinates of the same two-dimensional code in different crack images,the transformation matrix between two crack images taken at different times is calculated and registered.This registration method effectively solves the problem of insufficient accuracy and false matching of common registration methods.(2)After the crack image registration,the crack area in the two crack images can not completely coincide in the image,and there are several pixel offsets.This paper designs an optical flow algorithm to track the pixel points of the crack and calculate the offset of the crack in the two crack images,so as to make the two images achieve the maximum coincidence in space.(3)The processed crack image is identified and detected.In this paper,the deep learning method is used to identify the crack.The trained mask r-cnn is used to obtain the mask information of the crack,analyze the change of the crack according to the change of the mask information of the crack,judge the change process of the crack according to the intersection ratio of the crack mask,and fuse the two crack images by using the coordinates of the crack mask,The change process and change area of cracks are visualized.(4)In this paper,Python is used to compile and implement the algorithm process in the whole process,and the stability and reliability of the overall change process of crack are proved by the change experiment of concrete test block simulating crack.