Real-Time Hyperbola Recognition and Fitting in GPR Data

The problem of automatically recognising and fitting hyperbolae from Ground Penetrating Radar (GPR) images is addressed, and a novel technique computationally suitable for real time on-site application is proposed. After pre-processing of the input GPR images, a novel thresholding method is applied to separate the regions of interest from background. A novel column-connection clustering (C3) algorithm is then applied to separate the regions of interest from each other. Subsequently, a machine learnt model is applied to identify hyperbolic signatures from outputs of the C3 algorithm and a hyperbola is fitted to each such signature with an orthogonal distance hyperbola fitting algorithm. The novel clustering algorithm C3 is a central component of the proposed system, which enables the identification of hyperbolic signatures and hyperbola fitting. Only two features are used in the machine learning algorithm, which is easy to train using a small set of training data. An orthogonal distance hyperbola fitting algorithm for ‘south-opening’ hyperbolae is introduced in this work, which is more robust and accurate than algebraic hyperbola fitting algorithms. The proposed method can successfully recognise and fit hyperbolic signatures with intersections with others, hyperbolic signatures with distortions and incomplete hyperbolic signatures with one leg fully or largely missed. As an additional novel contribution, formulae to compute an initial ‘south-opening’ hyperbola directly from a set of given points are derived, which make the system more efficient. The parameters obtained by fitting hyperbolae to hyperbolic signatures are very important features, they can be used to estimate the location, size of the related target objects, and the average propagation velocity of the electromagnetic wave in the medium. The effectiveness of the proposed system is tested on both synthetic and real GPR data

Autonomous Robotic System using Non-Destructive Evaluation methods for Bridge Deck Inspection

Bridge condition assessment is important to maintain the quality of highway roads for public transport. Bridge deterioration with time is inevitable due to aging material, environmental wear and in some cases, inadequate maintenance. Non-destructive evaluation (NDE) methods are preferred for condition assessment for bridges, concrete buildings, and other civil structures. Some examples of NDE methods are ground penetrating radar (GPR), acoustic emission, and electrical resistivity (ER). NDE methods provide the ability to inspect a structure without causing any damage to the structure in the process. In addition, NDE methods typically cost less than other methods, since they do not require inspection sites to be evacuated prior to inspection, which greatly reduces the cost of safety related issues during the inspection process. In this paper, an autonomous robotic system equipped with three different NDE sensors is presented. The system employs GPR, ER, and a camera for data collection. The system is capable of performing real-time, cost-effective bridge deck inspection, and is comprised of a mechanical robot design and machine learning and pattern recognition methods for automated steel rebar picking to provide realtime condition maps of the corrosive deck environments

On the introduction of canny operator in an advanced imaging algorithm for real-time detection of hyperbolas in ground-penetrating radar data

This paper focuses on the use of the Canny edge detector as the first step of an advanced imaging algorithm for automated detection of hyperbolic reflections in ground-penetrating radar (GPR) data. Since the imaging algorithm aims to work in real time; particular attention is paid to its computational efficiency. Various alternative criteria are designed and examined, to fasten the procedure by eliminating unnecessary edge pixels from Canny-processed data, before such data go through the subsequent steps of the detection algorithm. The effectiveness and reliability of the proposed methodology are tested on a wide set of synthetic and experimental radargrams with promising results. The finite-difference time-domain simulator gprMax is used to generate synthetic radargrams for the tests, while the real radargrams come from GPR surveys carried out by the authors in urban areas. The imaging algorithm is implemented in MATLAB

Site Interiography and Geophysical Scanning: Interpreting the Texture and Form of Archaeological Deposits with Ground-Penetrating Radar

The remarkable potential of geophysical scanning—to assess the internal variability of sites in new ways, to highlight important phenomena in the field, to exercise co-creation of interpretation and commitment to minimal destruction of community partners’ resources, and to aid in the practice of due diligence in avoiding desecration of the sacred—continues to be underutilized in archaeology. While archaeological artifacts, features, and strata remain primary foci of archaeological geophysics, these phenomena are perceived quite differently in scans than in visual or tactile exposures. In turn, new registers of site exploration afforded by geophysical prospection may be constrained by the language of site excavation and visual observation, requiring adjustments in the ways of thinking about and describing what the instruments are measuring. The texture and form of site deposits as rendered in ground-penetrating radar scans can be examined in detail prior to making interpretations of cultural features or stratigraphy. Far more than simple “anomalies” demanding our attention for excavation, patterns in geophysical data can be the focus of extensive archaeological analysis prior to, in conjunction with, or independent from excavation

High-resolution coherency functionals for improving the velocity analysis of ground-penetrating radar data

We aim at verifying whether the use of high-resolution coherency functionals could improve the signal-to-noise ratio (S/N) of Ground-Penetrating Radar data by introducing a variable and precisely picked velocity field in the migration process. After carrying out tests on synthetic data to schematically simulate the problem, assessing the types of functionals most suitable for GPR data analysis, we estimated a varying velocity field relative to a real dataset. This dataset was acquired in an archaeological area where an excavation after a GPR survey made it possible to define the position, type, and composition of the detected targets. Two functionals, the Complex Matched Coherency Measure and the Complex Matched Analysis, turned out to be effective in computing coherency maps characterized by high-resolution and strong noise rejection, where velocity picking can be done with high precision. By using the 2D velocity field thus obtained, migration algorithms performed better than in the case of constant or 1D velocity field, with satisfactory collapsing of the diffracted events and moving of the reflected energy in the correct position. The varying velocity field was estimated on different lines and used to migrate all the GPR profiles composing the survey covering the entire archaeological area. The time slices built with the migrated profiles resulted in a higher S/N than those obtained from non-migrated or migrated at constant velocity GPR profiles. The improvements are inherent to the resolution, continuity, and energy content of linear reflective areas. On the basis of our experience, we can state that the use of high-resolution coherency functionals leads to migrated GPR profiles with a high-grade of hyperbolas focusing. These profiles favor better imaging of the targets of interest, thereby allowing for a more reliable interpretation

A GPR-GPS-GIS-integrated, information-rich and error-aware system for detecting, locating and characterizing underground utilities

Underground utilities have proliferated throughout the years. The location and dimension of many underground utilities have not always been properly collected and documented, leading to utility conflicts and utility strikes, and thus resulting in property damages, project delays, cost overruns, environment pollutions, injuries and deaths. The underlying reasons are twofold. First, the reliable data regarding the location and dimension of underground utility are missing or incomplete. Existing methods to collect data are not efficient and effective. Second, positional uncertainties are inherent in the measured utility locations. An effective means is not yet available to visualize and communicate the inherent positional uncertainties associated with utility location data to end-users (e.g., excavator operator). To address the aforementioned problems, this research integrate ground penetrating radar (GPR), global positioning system (GPS) and geographic information system (GIS) to form a total 3G system to collect, inventory and visualize underground utility data. Furthermore, a 3D probabilistic error band is created to model and visualize the inherent positional uncertainties in utility data. ^ Three main challenges are addressed in this research. The first challenge is the interpretation of GPR and GPS raw data. A novel method is created in this research to simultaneously estimate the radius and buried depth of underground utilities using GPR scans and auxiliary GPS data. The proposed method was validated using GPR field scans obtained under various settings. It was found that this newly created method increases the accuracy of estimating the buried depth and radius of the buried utility under a general scanning condition. The second challenge is the geo-registration of detected utility locations. This challenge is addressed by integration of GPR, GPS and GIS. The newly created system takes advantages of GPR and GPS to detect and locate underground utilities in 3D and uses GIS for storing, updating, modeling, and visualizing collected utility data in a real world coordinate system. The third challenge is positional error/uncertainty assessment and modeling. The locational errors of GPR system are evaluated in different depth and soil conditions. Quantitative linkages between error magnitudes and its influencing factors (i.e., buried depths and soil conditions) are established. In order to handle the positional error of underground utilities, a prototype of 3D probabilistic error band is created and implemented in GIS environment. This makes the system error-aware and also paves the way to a more intelligent error-aware GIS. ^ To sum up, the newly created system is able to detect, locate and characterize underground utilities in an information-rich and error-aware manner

A practical guide on using SPOT-GPR, a freeware tool implementing a SAP-DoA technique

This is a software paper, which main objective is to provide practical information on how to use SPOT-GPR release 1.0, a MATLAB®-based software for the analysis of ground penetrating radar (GPR) profiles. The software allows detecting targets and estimating their position in a two-dimensional scenario, it has a graphical user interface and implements an innovative sub-array processing method. SPOT-GPR was developed in the framework of the COST Action TU1208 “Civil Engineering Applications of Ground Penetrating Radar” and is available for free download on the website of the Action (www.GPRadar.eu)