The subsurface of urban cities is becoming increasingly congested.In-time records of subsur-face structures are of vital importance for the maintenance and management of urban infrastructure beneath or above the groun...The subsurface of urban cities is becoming increasingly congested.In-time records of subsur-face structures are of vital importance for the maintenance and management of urban infrastructure beneath or above the ground.Ground-penetrating radar(GPR)is a nondestructive testing method that can survey and image the subsurface without excava-tion.However,the interpretation of GPR relies on the operator’s experience.An automatic workflow was proposed for recognizing and classifying subsurface structures with GPR using computer vision and machine learning techniques.The workflow comprises three stages:first,full-cover GPR measurements are processed to form the C-scans;second,the abnormal areas are extracted from the full-cover C-scans with coefficient of variation-active contour model(CV-ACM);finally,the extracted segments are recognized and classified from the corresponding B-scans with aggregate channel feature(ACF)to produce a semantic map.The selected computer vision methods were validated by a controlled test in the laboratory,and the entire workflow was evaluated with a real,on-site case study.The results of the controlled and on-site case were both promising.This study establishes the necessity of a full-cover 3D GPR survey,illustrating the feasibility of integrating advanced computer vision techniques to analyze a large amount of 3D GPR survey data,and paves the way for automating subsurface modeling with GPR.展开更多
基金supported by the Shenzhen University[860-000002111308].
文摘The subsurface of urban cities is becoming increasingly congested.In-time records of subsur-face structures are of vital importance for the maintenance and management of urban infrastructure beneath or above the ground.Ground-penetrating radar(GPR)is a nondestructive testing method that can survey and image the subsurface without excava-tion.However,the interpretation of GPR relies on the operator’s experience.An automatic workflow was proposed for recognizing and classifying subsurface structures with GPR using computer vision and machine learning techniques.The workflow comprises three stages:first,full-cover GPR measurements are processed to form the C-scans;second,the abnormal areas are extracted from the full-cover C-scans with coefficient of variation-active contour model(CV-ACM);finally,the extracted segments are recognized and classified from the corresponding B-scans with aggregate channel feature(ACF)to produce a semantic map.The selected computer vision methods were validated by a controlled test in the laboratory,and the entire workflow was evaluated with a real,on-site case study.The results of the controlled and on-site case were both promising.This study establishes the necessity of a full-cover 3D GPR survey,illustrating the feasibility of integrating advanced computer vision techniques to analyze a large amount of 3D GPR survey data,and paves the way for automating subsurface modeling with GPR.
