Research And Application Of Bridge Crack Detection Algorithm Under Complex Background

With the great development of bridge industry in China,more attention has been paid to the maintenance and management of bridges.As the most common bridge disease and an important part of bridge health monitoring,bridge crack is paid more attention by the maintenance department.At present,the detection of bridge cracks mainly relies on manual inspection.This method has the problems of high risk,high subjectivity,poor reliability and low detection efficiency.As digital image processing and deep learning developed,using such technology and method to achieve the bridge cracks’ intelligent detection and improve the accuracy and efficiency of crack detection is of great implications.Because the surface environment of the bridge is easily disturbed by illumination,stains,patches,pitting surface,surface peeling and other factors,the background of crack image is more complex,and it is difficult to identify and extract crack features with high precision.Therefore,the research on the detection of bridge cracks under the complex background has certain theoretical and application value.This paper mainly studies the detection methods of bridge cracks in complex background images,and the main research contents are as follows:1.Preprocessing the image of bridge crack.Common image enhancement algorithms and threshold-based image binary segmentation methods are analyzed and compared.In order to reduce the noise and retain more crack edge features,the optimal guided filter was used to denoising.In order to enhance crack feature information and ensure crack feature integrity,gamma correction is used to improve image contrast.Finally,the crack images are segmented by the maximum inter-class variance method.Experimental results show the effectiveness of the proposed preprocessing method.2.To solve the problem that digital image processing method has low recall rate in crack detection under complex image background,a bridge crack detection model combined Inception structure and deformable residual blocks is proposed based on U-net model.The model optimized the network structure by improving the convolution method and the loss function,and realized the rapid and complete extraction of crack features.Experimental results show that the proposed model has higher accuracy in crack extraction under complex image background.3.A new method Zhang’s fast parallel algorithm is proposed after analyzing K3 M,Hilditch,Zhang’s fast parallel three kinds of algorithm,which are commonly used in image thinning.The algorithm is mainly to refine the image of orthogonal and burr elimination was improved,the improved algorithm can guarantee the single pixel width of skeleton image,eliminate the burr and keep the skeleton smooth effectively.Experimental comparison shows that the improved algorithm is effective.4.In order to calculate the maximum width of crack more accurately,a linear crack maximum width measurement algorithm based on piecewise fitting of pixel points was proposed.The method calculate the maximum width of crack according to the extension direction of each crack point by piecewise linear fitting of pixel points of crack skeleton image.In order to accurately calculate the area of reticular crack,a method of measuring the area of reticular crack based on convex polygon is proposed.The method is that the boundary points and endpoints of cracks in the image are taken as the apexs of the convex polygon.The polygon’s area is the reticula crack’s area.5.On the basis of the research in this paper,combined with the practical application requirements,the prototype system of bridge crack detection is designed and implemented,which realizes the functions of image import,image preprocessing,crack characteristic parameter calculation and so on.