Ground penetrating radar (GPR) is one of the most popular and successful sensing modalities that has been investigated for landmine and subsurface threat detection. Many of the detection algorithms applied to this task are supervised and therefore require labeled examples of target and non-target data for training. Training data most often consists of 2-dimensional images (or patches) of GPR data, from which features are extracted, and provided to the classifier during training and testing. Identifying desirable training and testing locations to extract patches, which we term "keypoints", is well established in the literature. In contrast however, a large variety of strategies have been proposed regarding keypoint utilization (e.g., how many of the identified keypoints should be used at targets, or non-target, locations). Given the variety keypoint utilization strategies that are available, it is very unclear (i) which strategies are best, or (ii) whether the choice of strategy has a large impact on classifier performance. We address these questions by presenting a taxonomy of existing utilization strategies, and then evaluating their effectiveness on a large dataset using many different classifiers and features. We analyze the results and propose a new strategy, called PatchSelect, which outperforms other strategies across all experiments.
In this paper, we adapt the Faster-RCNN framework for the detection of underground buried objects (i.e. hyperbola reflections) in B-scan ground penetrating radar (GPR) images. Due to the lack of real data for training, we propose to incorporate more simulated radargrams generated from different configurations using the gprMax toolbox. Our designed CNN is first pre-trained on the grayscale Cifar-10 database. Then, the Faster-RCNN framework based on the pre-trained CNN is trained and fine-tuned on both real and simulated GPR data. Preliminary detection results show that the proposed technique ca…
Substantial research has been devoted to the development of algorithms that automate buried threat detection (BTD) with ground penetrating radar (GPR) data, resulting in a large number of proposed algorithms. One popular algorithm GPR-based BTD, originally applied by Torrione et al., 2012, is the Histogram of Oriented Gradients (HOG) feature. In a recent large-scale comparison among five veteran institutions, a modified version of HOG referred to here as "gprHOG", performed poorly compared to other modern algorithms. In this paper, we provide experimental evidence demonstrating that the modifi…
Forward-looking ground-penetrating radar (FLGPR) has recently been investigated as a remote sensing modality for buried target detection (e.g., landmines). In this context, raw FLGPR data is beamformed into images and then computerized algorithms are applied to automatically detect subsurface buried targets. Most existing algorithms are supervised, meaning they are trained to discriminate between labeled target and non-target imagery, usually based on features extracted from the imagery. A large number of features have been proposed for this purpose, however thus far it is unclear which are th…
Duke University（杜克大学）；University of Louisville（路易斯维尔大学）；University of Missouri（密苏里大学）；Chemring Sensors and Electronic Systems (CSES)（Chemring传感器与电子系统公司（CSES））
In this paper we consider the development of algorithms for the automatic detection of buried threats using ground penetrating radar (GPR) measurements. GPR is one of the most studied and successful modalities for automatic buried threat detection (BTD), and a large variety of BTD algorithms have been proposed for it. Despite this, large-scale comparisons of GPR-based BTD algorithms are rare in the literature. In this work we report the results of a multi-institutional effort to develop advanced buried threat detection algorithms for a real-world GPR BTD system. The effort involved five instit…
GPR-OdomNet: Difference and Similarity-Driven Odometry Estimation Network for Ground Penetrating Radar-Based Localization
the Department of Computer Science（计算机科学系）；Civil Aviation University of China（民航大学）；Chengdu Textile College（成都纺织学院）；the Department of Robotics（机器人系）；Mohamed Bin Zayed University of Artificial Intelligence (MBZUAI)（马尔代夫比赞人工智能大学）
Karlsruhe Institute of Technology (KIT)（卡尔斯鲁厄理工学院）
In this contribution, we investigate the potential of hyperspectral data combined with either simulated ground penetrating radar (GPR) or simulated (sensor-like) soil-moisture data to estimate soil moisture. We propose two simulation approaches to extend a given multi-sensor dataset which contains sparse GPR data. In the first approach, simulated GPR data is generated either by an interpolation along the time axis or by a machine learning model. The second approach includes the simulation of soil-moisture along the GPR profile. The soil-moisture estimation is improved significantly by the fusi…
Shandong University（山东大学）；University of Leeds（利兹大学）
This research proposes a Ground Penetrating Radar (GPR) data processing method for non-destructive detection of tunnel lining internal defects, called defect segmentation. To perform this critical step of automatic tunnel lining detection, the method uses a CNN called Segnet combined with the Lovász softmax loss function to map the internal defect structure with GPR synthetic data, which improves the accuracy, automation and efficiency of defects detection. The novel method we present overcomes several difficulties of traditional GPR data interpretation as demonstrated by an evaluation on both…
Automatic Road Subsurface Distress Recognition from Ground Penetrating Radar Images using Deep Learning-based Cross-verification
Department of Engineering Mechanics, School of Civil Engineering and Transportation, South China University of Technology（华南理工大学土木工程与交通学院力学系）；Guangdong Provincial Academy of Building Research Group Co. Ltd.（广东省建筑科学研究集团有限公司）；School of Civil and Transportation Engineering, Guangdong University of Technology（广东工业大学土木与交通学院）
The City College of New York（纽约城市学院）；Hostos Community College（霍斯托斯社区学院）
Ground Penetrating Radar (GPR) is a very useful non-destructive evaluation (NDE) device for locating and mapping underground assets prior to digging and trenching efforts in construction. This paper presents a novel robotic system to automate the GPR data collection process, localize the underground utilities, interpret and reconstruct the underground objects for better visualization allowing regular non-professional users to understand the survey results. This system is composed of three modules: 1) an Omni-directional robotic data collection platform, that carries an RGB-D camera with an Ine…
mmSpyVR: Exploiting mmWave Radar for Penetrating Obstacles to Uncover Privacy Vulnerability of Virtual Reality
Southeast University（东南大学）；City University of Hong Kong（香港城市大学）；Michigan State University（密歇根州立大学）；Singapore University of Technology and Design（新加坡科技设计大学）；University of Minnesota（明尼苏达大学）
Shandong University（山东大学）；University of Leeds（利兹大学）
A DNN architecture referred to as GPRInvNet was proposed to tackle the challenges of mapping the ground-penetrating radar (GPR) B-Scan data to complex permittivity maps of subsurface structures. The GPRInvNet consisted of a trace-to-trace encoder and a decoder. It was specially designed to take into account the characteristics of GPR inversion when faced with complex GPR B-Scan data, as well as addressing the spatial alignment issues between time-series B-Scan data and spatial permittivity maps. It displayed the ability to fuse features from several adjacent traces on the B-Scan data to enhanc…
Indiana University（印第安纳大学）；University of Kansas（堪萨斯大学）
Ground-penetrating radar on planes and satellites now makes it practical to collect 3D observations of the subsurface structure of the polar ice sheets, providing crucial data for understanding and tracking global climate change. But converting these noisy readings into useful observations is generally done by hand, which is impractical at a continental scale. In this paper, we propose a computer vision-based technique for extracting 3D ice-bottom surfaces by viewing the task as an inference problem on a probabilistic graphical model. We first generate a seed surface subject to a set of constr…
Indiana University（印第安纳大学）；University of Kansas（堪萨斯大学）；University of Colorado（科罗拉多大学）
Understanding the structure of Earth's polar ice sheets is important for modeling how global warming will impact polar ice and, in turn, the Earth's climate. Ground-penetrating radar is able to collect observations of the internal structure of snow and ice, but the process of manually labeling these observations is slow and laborious. Recent work has developed automatic techniques for finding the boundaries between the ice and the bedrock, but finding internal layers - the subtle boundaries that indicate where one year's ice accumulation ended and the next began - is much more challenging beca…
Mapping Global Floods with 10 Years of Satellite Radar Data
Microsoft AI for Good Research Lab（微软人工智能公益研究院）；Cooperative Institute for Meteorological Satellite Studies, University of Wisconsin-Madison（威斯康星大学麦迪逊分校合作气象卫星研究所）
RADE-Net: Robust Attention Network for Radar-Only Object Detection in Adverse Weather
Infineon Technologies AG（英飞凌科技有限公司）；Institute of Visual Computing（视觉计算研究所）；Graz University of Technology（格拉茨工业大学）；Virtual Vehicle Research GmbH（虚拟车辆研究有限公司）
The Pennsylvania State University（宾夕法尼亚州立大学）；U.S. Army Research Laboratory（美国陆军研究实验室）
Classifying buried and obscured targets of interest from other natural and manmade clutter objects in the scene is an important problem for the U.S. Army. Targets of interest are often represented by signals captured using low-frequency (UHF to L-band) ultra-wideband (UWB) synthetic aperture radar (SAR) technology. This technology has been used in various applications, including ground penetration and sensing-through-the-wall. However, the technology still faces a significant issues regarding low-resolution SAR imagery in this particular frequency band, low radar cross sections (RCS), small ob…
DeepTopoNet: A Framework for Subglacial Topography Estimation on the Greenland Ice Sheets
iHARP, University of Maryland, Baltimore County (UMBC)（iHARP，美国马里兰大学巴尔的摩县分校）；Department of Earth Sciences, Dartmouth College（地球科学系，达特茅斯学院）；University of Maryland, Baltimore County (UMBC)（美国马里兰大学巴尔的摩县分校）
Fraunhofer Institute for High Frequency Physics and Radar Techniques（弗劳恩霍夫高频物理与雷达技术研究所）；The University of Iowa（爱荷华大学）；Weizmann Institute of Science（魏茨曼科学研究所）；University of Siegen（锡根大学）
Ground penetrating radar (GPR) target detection and classification is a challenging task. Here, we consider online dictionary learning (DL) methods to obtain sparse representations (SR) of the GPR data to enhance feature extraction for target classification via support vector machines. Online methods are preferred because traditional batch DL like K-SVD is not scalable to high-dimensional training sets and infeasible for real-time operation. We also develop Drop-Off MINi-batch Online Dictionary Learning (DOMINODL) which exploits the fact that a lot of the training data may be correlated. The D…
University of Science and Technology of China（中国科学技术大学）
Ground Penetrating Radar (GPR) has been widely used to estimate the healthy operation of some urban roads and underground facilities. When identifying subsurface anomalies by GPR in an area, the obtained data could be unbalanced, and the numbers and types of possible underground anomalies could not be acknowledged in advance. In this paper, a novel method is proposed to improve the subsurface anomaly detection from GPR B-scan images. A normal (i.e. without subsurface objects) GPR image section is firstly collected in the detected area. Concerning that the GPR image is essentially the represent…
Reservoir-enhanced Segment Anything Model for Subsurface Diagnosis
School of Computer Science and Technology, University of Science and Technology of China（中国科学技术大学计算机科学与技术学院）；Research Institute of Mine Artificial Intelligence, Chinese Institute of Coal Science（中国煤炭科学研究院矿山人工智能研究院）
