Image Processing And Disease Detection Of Cement Concrete Pavement In Agricultural And Pastoral Areas

Cracks,pits and bares are the main diseases of cement concrete pavement in agricultural and pastoral areas,which have great hindrances to the economic circulation of the local highway.The effective detection method of cement concrete pavement diseases is an important condition to ensure the smooth circulation of the highway economy.At present,the detection methods of pavement diseases are gradually transitioning from manual and semi-manual detection to automatic detection.The detection and recognition methods based on image processing are widely popular because of their high accuracy and fast speed.However,there are many problems in the current automatic detection methods.For example,the high repetition rate of single frame detection information reduces the detection efficiency,and obvious traces are produced by road image Mosaic,resulting in identification errors,etc.In this thesis,based on the image processing technology carried out by computer,the detection methods of the main diseases of cement concrete pavement are studied as follows:(1)An algorithm combining MSAC algorithm and bidirectional consistency algorithm with traditional SURF algorithm is proposed.This method extracts feature points based on SURF algorithm,calculates accurate matching points through bidirectional consistency algorithm,and finally selects the optimal model obtained by MSAC algorithm according to the minimum error to retain the optimal matching points.Compared with the traditional SURF algorithm,this method has obvious advantages in the accuracy and time of feature extraction and matching.(2)Aiming at the problem of stitching traces in stitched images,a patchwork elimination method based on the proportion adjustment of image brightness and distance was proposed.The entropy,standard deviation and average gradient of the calculated fusion algorithm are 7.534,12.531 and 6.882 respectively.The image fusion effect of this method is good,and the obvious stitching trace caused by different light and dark degree and image dislocation is well eliminated.(3)Based on the texture features and projection features of the disease,the Li BSVM model was built to identify the pavement images.The results showed that the recall rate of the disease images was 98.51%,the accuracy rate was 97.06%,the accuracy rate was 98.06%,and the F1-score was 97.78%.The method had high accuracy in the recognition of the disease.(4)In order to eliminate the repeated information of continuous disease detection,this thesis uses the method of first stitching and then disease detection to recognize the pavement image disease.The results show that the detection accuracy of the Mosaic image is 93.63%,and the detection time is 588.6s.The method can be used to identify and detect the pavement disease more accurately and quickly.