Automatic hyperbola detection and fitting in GPR B-scan image

Release time:2021-06-28

Impact Factor:6.121

DOI number:10.1016/j.autcon.2019.102839

Journal:AUTOMATION IN CONSTRUCTION

Key Words:Automatic hyperbola detection; Double cluster seeking estimate (MSS) algorithm; Ground penetrating radar (GPR); Faster R-CNN

Abstract:Detecting buried objects from ground penetrating radar (GPR) profiles often requires manual interaction and plenty of time. This paper presents an automatic scheme for buried objects detection and localization. First, a trained deep learning framework - Faster R-CNN with data augmentation strategy is applied to identify hyperbolic signatures from a gray GPR B-scan image, which is capable of not only recognizing whether a B-scan profile contains traces of buried object, but also detecting candidate hyperbola region. Then, the detected rectangle region is extracted and transformed to a binary image, a novel double cluster seeking estimate (DCSE) algorithm is proposed to separate object point duster from each other and enable the identification of hyperbolic signatures. Subsequently, a column-based transverse filter points (CTFP) method is utilized to extract hyperbola fitting points automatically from the validated point duster. Downward opening hyperbola fitting is carried out and their respective peaks are obtained finally. The proposed scheme is able to extract information from GPR B-scan images automatically and efficiently; it is validated significant performance in the analysis of synthetic and on-site GPR data sets.

First Author:雷文太

Indexed by:Journal paper

Correspondence Author:侯斐斐(博士生)

Discipline:Engineering

First-Level Discipline:Information and Communication Engineering

Document Type:J

Translation or Not:no

Date of Publication:2019-10-01

Included Journals:SCI、EI