Automatic detection of pipe-flange reflections in GPR data sections using supervised learning

Bordón, Paula D.; Bonomo, Néstor; Martinélli, P.

doi:10.1016/j.jappgeo.2019.103856

Cited by 6 publications

(1 citation statement)

References 39 publications

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“…The application of automatic detection techniques based on supervised learning to GPR data has been addressed by different authors in the last years (Bordon, Bonomo, & Martinelli, 2019; Bordon, Martinelli, & Bonomo, 2017; Giannakis, Giannopoulos, & Warren, 2019; Smitha & Singh, 2019). The most usual objectives of these studies have been to detect and classify landmines and unexploded ordnance (Núñez‐Nieto, Solla, Gómez‐Pérez, & Lorenzo, 2014; Pinar et al, 2015; Torrione, Morton, Sakaguchi, & Collins, 2012), pipes (Bordon et al, 2017, 2019; Maas & Schmalzl, 2013; Noreen & Khan, 2017; Ristić, Bugarinović, Vrtunski, & Govedarica, 2017) and voids and conduits (Kilic & Eren, 2018; Qin & Huang, 2016; Xie, Li, Qin, Liu, & Nobes, 2013) and to inspect pavements and concrete (Kilic & Unluturk, 2014; Rahman & Zayed, 2018; Shangguan, Al‐Qadi, & Lahouar, 2014). Regarding deep learning techniques, they have been applied for pavement inspection (Tong, Gao, & Zhang, 2017), the detection and classification of landmines and unexploded ordnance (Kafedziski, Pecov, & Tanevski, 2018; Lameri, Lombardi, Bestagini, Lualdi, & Tubaro, 2017) and the detection of hyperbolas from different objects in radargrams (Pham & Lefèvre, 2018), rebars in concrete bridge decks (Dinh, Gucunski, & Duong, 2018) and pipes (Alvarez & Kodagoda, 2018).…”

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confidence: 99%

Automatic detection of mud‐wall signatures in ground‐penetrating radar data

Bordón

Martinélli

Medina

et al. 2020

Archaeological Prospection

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The ground‐penetrating radar (GPR) method with the standard constant‐offset reflection mode allows to detect and map different types of archaeological structures, such as walls, foundations, floors and roads. The interpretation of the GPR data usually involves a detailed and time‐consuming analysis of large amounts of information, which entails nonnegligible chances of errors, especially under nonideal fieldwork conditions. The application of suitable automatic detection algorithms can be useful to more rapidly and successfully complete the interpretation task. In this work, we explore the use of supervised machine learning methodologies to automatically detect mud‐wall signatures in radargrams and to map the structures from these detections. Several algorithms, based on Viola–Jones cascade classifiers and the image feature descriptors Haar, histogram of oriented gradients and local binary patterns, were implemented. These algorithms were applied to datasets previously acquired in pre‐Inca and Inca‐Hispanic sites located in the Andean NW region of Argentina. The best algorithms provided very good detection rates for well‐preserved walls and acceptable rates for deteriorated walls, with a low number of spurious predictions. They also exhibited the ability to detect collapsed walls and fragments detached from them. These are remarkable results because mud walls are usually difficult to be detected by conventional analysis, owing to the complex and variable characteristics of their reflection patterns. The results of the automatic detection techniques were represented in plan views and three‐dimensional (3D) views that delineated in detail most of the structures of the sites. These algorithms are very fast, so applying them significantly reduces the interpretation times. In addition, once they have been trained using the patterns of one or several sites, they are directly applicable to other sites with similar characteristics.

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