Landmine detection from GPR data using convolutional neural networks

Lameri, Silvia; Lombardi, Federico; Bestagini, Paolo; Lualdi, Maurizio; Tubaro, Stefano

doi:10.23919/eusipco.2017.8081259

Cited by 105 publications

(67 citation statements)

References 18 publications

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“…Since the start of 2020, articles have been published using convolutional neural networks (CNNs) to detect patterns in LiDAR (Light Detection and Ranging) data, images in the Google Street View database, video data, UAV data, and NASA's Earth Observation (EO) data for a variety of purposes from detecting pedestrians at night to mapping landslides [19][20][21][22][23]. There have been successful efforts using CNN's to detect buried landmines in ground-penetrating radar data, yet there is a lack of research on using CNN to identify surface mines such as the PFM-1 [24,25]. This study focuses on UAV based multispectral and thermal infrared sensing to train a robust CNN to automate detection of the PFM-1 landmines to dramatically decrease the time, cost, and increase accuracy associated with current methods.…”

Section: Convolutional Neural Network (Cnn) Overviewmentioning

confidence: 99%

Applying Deep Learning to Automate UAV-Based Detection of Scatterable Landmines

et al. 2020

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Recent advances in unmanned-aerial-vehicle-(UAV-) based remote sensing utilizing lightweight multispectral and thermal infrared sensors allow for rapid wide-area landmine contamination detection and mapping surveys. We present results of a study focused on developing and testing an automated technique of remote landmine detection and identification of scatterable antipersonnel landmines in wide-area surveys. Our methodology is calibrated for the detection of scatterable plastic landmines which utilize a liquid explosive encapsulated in a polyethylene or plastic body in their design. We base our findings on analysis of multispectral and thermal datasets collected by an automated UAV-survey system featuring scattered PFM-1-type landmines as test objects and present results of an effort to automate landmine detection, relying on supervised learning algorithms using a Faster Regional-Convolutional Neural Network (Faster R-CNN). The RGB visible light Faster R-CNN demo yielded a 99.3% testing accuracy for a partially withheld testing set and 71.5% testing accuracy for a completely withheld testing set. Across multiple test environments, using centimeter scale accurate georeferenced datasets paired with Faster R-CNN, allowed for accurate automated detection of test PFM-1 landmines. This method can be calibrated to other types of scatterable antipersonnel mines in future trials to aid humanitarian demining initiatives. With millions of remnant PFM-1 and similar scatterable plastic mines across post-conflict regions and considerable stockpiles of these landmines posing long-term humanitarian and economic threats to impacted communities, our methodology could considerably aid in efforts to demine impacted regions.

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Section: Convolutional Neural Network (Cnn) Overviewmentioning

confidence: 99%

Applying Deep Learning to Automate UAV-Based Detection of Scatterable Landmines

et al. 2020

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“…Oberweger [11] applied CNN to gesture recognition; Lameri [12] used CNN for GPR image recognition and got better results compared with other traditional methods. Faster R-CNN is an end-to-end target detection algorithm based on candidate regions.…”

Section: Introductionmentioning

confidence: 99%

Research on GPR image recognition based on deep learning

Zhang

2020

MATEC Web Conf.

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It is difficult for traditional image recognition methods to accurately identify ground penetrating radar (GPR) images. This paper proposes a deep-learning based Faster R-CNN algorithm for the automatic classification and recognition of GPR images. Firstly, GPR images with different features were obtained by using gprMax, a professional GPR simulation software. Then, the feature of the target in the image was taken as the recognition object and the data set was made. Finally, Faster R-CNN's recognition ability of GPR images was analyzed from various accuracy, average accuracy and other indicators. The results showed that Faster R-CNN could successfully identify GPR images and accurately classify them, with an average accuracy rate of 93.9%.

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“…The data are then processed through a learning technique, known as Convolutional Neural Networks (CNNs), to detect the presence of buried objects not coherent with the surrounding soil texture [36][37][38]. Compared to other supervised learning alternatives, CNNs, a deep learning technique particularly suited for computer vision and image classification tasks, determine the classification features in an automated way directly from the raw data, i.e., without the need for extensive manual data labelling.…”

Section: Introductionmentioning

confidence: 99%

Ballistic Ground Penetrating Radar Equipment for Blast-Exposed Security Applications

et al. 2020

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Among all the forensic applications in which it has become an important exploration tool, ground penetrating radar (GPR) methodology is being increasingly adopted for buried landmine localisation, a framework in which it is expected to improve the operations efficiency, given the high resolution imaging capability and the possibility of detecting both metallic and non-metallic landmines. In this context, this study presents landmine detection equipment based on multi-polarisation: a ground coupled GPR platform, which ensures suitable penetration/resolution performance without affecting the safety of surveys, thanks to the inclusion of a flexible ballistic shielding for supporting eventual blasts. The experimental results have shown that not only can the blanket absorb blast-induced flying fragments impacts, but that it also allows for the acquisition of data with the accuracy required to generate a correct 3D reconstruction of the subsurface. The produced GPR volume is then processed through an automated learning scheme based on a Convolutional Neural Network (CNN) capable of detecting buried objects with a high degree of accuracy.

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Landmine detection from GPR data using convolutional neural networks

Cited by 105 publications

References 18 publications

Applying Deep Learning to Automate UAV-Based Detection of Scatterable Landmines

Applying Deep Learning to Automate UAV-Based Detection of Scatterable Landmines

Research on GPR image recognition based on deep learning

Ballistic Ground Penetrating Radar Equipment for Blast-Exposed Security Applications

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