Ground penetrating radar and deep learning-based underground pipeline detection method and system

A ground penetrating radar (GPR) and deep learning-based underground pipeline detection system, comprising:

• a dataset establishment module, configured to acquire sample data of known underground pipelines by means of a GPR, and establish a GPR B-scan dataset according to the sample data;
• an identification module, configured to perform training according to the GPR B-scan dataset to obtain a target identification model You Only Look Once version 3 (YOLOV3);
• a detection module, configured to identify a hyperbola in a real GPR image of an underground pipeline target with the target identification model YOLOV3 to obtain a target bounding box for the underground pipeline target;
• and
• a localization module, configured to:
• collapse the hyperbola in the real GPR image into concentrated blobs at hyperbolic apexes by diffraction stack migration to obtain a reconstructed GPR image, and an expression of the diffraction stack migration is as follow:
• E
• ⁡
• (
• x
• ,
• z
• )
• =
• ∑
• i
• =
• 1
• m
• S
• i
• (
• t
• i
• (
• x
• ,
• z
• )
• )
• where E is the reconstructed GPR image, m is number of traces in a GPR B-scan, ti is a two way travel-time, Si is amplitude of i-th recorded GPR signal at the two way travel-time ti, which is calculated from relative coordinates of a imaging point (x,z) and antennas on a ground surface, as well as a velocity of a background medium;
• apply a Hilbert transformation to each trace in the reconstructed GPR image to obtain an envelope image, and then binarising the image to distinguish target response from background;
• apply a Gaussian filter to remove possible spikes in the background, thereby obtaining a round-shape blob that replaces the hyperbola in the real GPR image;
• and
• locate position of the underground pipeline target through an apex of the round-shape blob.