How to Implement Drones and Machine Learning to Reduce Time, Costs, and Dangers Associated with Landmine Detection

Two rapidly emerging technologies revolutionizing scientific problem solving are unpiloted aerial systems (UAS), commonly referred to as drones, and deep learning algorithms.1 Our study combines these two technologies to provide a powerful auxiliary tool for scatterable landmine detection. These munitions are traditionally challenging for clearance operations due to their wide area of impact upon deployment, small size, and random minefield orientation. Our past work focused on developing a reliable UAS capable of detecting and identifying individual elements of PFM-1 minefields to rapidly assess wide areas for landmine contamination, minefield orientation, and possible minefield overlap. In our most recent proof-of-concept study we designed and deployed a machine learning workflow involving a region-based convolutional neural network (R-CNN) to automate the detection and classification process, achieving a 71.5% rate of successful detection.2 In subsequent trials, we expanded our dataset and improved the accuracy of the CNN to detect PFM-1 anti-personnel mines from visual (RGB) UAS-based imagery to 91.8%. In this paper, we intend to familiarize the demining community with the strengths and limitations of UAS and machine learning and suggest a fit of this technology as a key auxiliary first look area reduction technique in humanitarian demining operations. As part of this effort, we seek to provide detailed guidance on how to implement this technique for non-technical survey (NTS) support and area reduction of confirmed and suspected hazardous areas with minimal resources and funding

Drones and Butterflies : A Low-Cost UAV System for Rapid Detection and Identification of Unconventional Minefields

Aerially-deployed plastic landmines in post-conflict nations present unique detection and disposal challenges. Their small size, randomized distribution during deployment, and low-metal content make these mines more difficult to identify using traditional methods of electromagnetic mine detection. Perhaps the most notorious of these mines is the Sovietera PFM-1 “butterfly mine,” widely used during the decade-long Soviet-Afghan conflict between 1979 and 1989. Predominantly used by the Soviet forces to block otherwise inaccessible mountain passages, many PFM-1 minefields remain in place due to the high associated costs of access and demining. While the total number of deployed PFM-1 mines in Afghanistan is poorly documented, PFM-1 landmines make up a considerable percentage of the estimated 10 million landmines remaining in place across Afghanistan. Their detection and disposal presents a unique logistical challenge for largely the same reasons that their deployment was rationalized in inaccessible and sparsely populated areas of the country

Automated UAS Aeromagnetic Surveys to Detect MBRL Unexploded Ordnance

Unguided Multiple Barrel Rocket Launcher (MBRL) systems are limited-accuracy, high-impact artillery systems meant to deliver barrages of explosive warheads across a wide area of attack. High rates of failure of MBRL rockets on impact and their wide area of ballistic dispersion result in a long-term unexploded ordnance (UXO) concern across large areas where these systems have been deployed. We field tested a newly-developed UAV (unmanned aerial vehicle)-based aeromagnetic platform to remotely detect and identify unexploded 122 mm rockets of the widely-used BM-21 MBRL. We developed an algorithm that allows near real-time analysis, mapping, and interpretations of magnetic datasets in the field and, as a result, rapid identification of anomalies associated with both surfaced and buried MBRL items of UXO. We tested a number of sensor configurations and calibrated the system for optimal signal-to-noise data acquisition over varying site types and in varying environmental conditions. The use of automated surveying allowed us to significantly constrain the search area for UXO removal or in-place destruction. The results of our field trials conclusively demonstrated that implementation of this geophysical system significantly reduces labor and time costs associated with technical assessment of UXO-contaminated sites in post-conflict regions

A Cost-Efficient Method for Detecting Unexploded 122mm 9M22U Rockets Using Remote Sensing

Unexploded ordnances (UXOs) are any subsurface weapon that pose the threat of detonation. UXOs pose one of the greatest humanitarian concerns of today, as they contaminate land in countries across the globe and lead to thousands of deaths each year. Our research focuses specifically on the BM-21 Grad, a Soviet multiple rocket launcher that fires 122mm rockets with a failure rate of over 4%. This means that the rockets often do not detonate immediately as intended, but become UXOs lodged underground. We studied the use of magnetometry, specifically the UMT MFAM MagPike remote sensor to detect these rockets. We processed data collected from Chernihiv, Ukraine to conclude that BM-21 Grad 122mm rockets do give off magnetic fields that are detectable using magnetometry, and distance above ground level plays a key role in data clarity

Inspiring the Next Generation of Humanitarian Mine Action Researchers

Humanitarian mine action (HMA) is a critically under-researched field when compared to other hazards fields of similar societal impact. A potential solution to this problem is early exposure to and engagement in the HMA field in undergraduate education. Early undergraduate education emphasizing technical and social aspects of HMA can help protect lives by building a robust pipeline of passionate researchers who will find new solutions to the global explosive ordnance (EO) crisis. Early engagement of the next generation of HMA researchers and policy makers can occur through various classroom experiences, undergraduate research projects, and public outreach events. These include but are not limited to course-based undergraduate research experiences (CUREs); presenting research results at local, national, and international conferences; dissemination in edited and peer-reviewed publications; local community events; and through social media outreach. Early engagement, active guidance, and mentorship of such students by mid-career and experienced HMA scholars and practitioners could dramatically reduce the learning curve associated with entry into the HMA sector and allow for more fruitful long-term collaboration between academic institutions, private industry, and leading nongovernmental organizations (NGOs) operating across different facets of HMA

Enhanced production of multi-strange hadrons in high-multiplicity proton-proton collisions

At sufficiently high temperature and energy density, nuclear matter undergoes a transition to a phase in which quarks and gluons are not confined: the quark-gluon plasma (QGP)(1). Such an exotic state of strongly interacting quantum chromodynamics matter is produced in the laboratory in heavy nuclei high-energy collisions, where an enhanced production of strange hadrons is observed(2-6). Strangeness enhancement, originally proposed as a signature of QGP formation in nuclear collisions(7), is more pronounced for multi-strange baryons. Several effects typical of heavy-ion phenomenology have been observed in high-multiplicity proton-proton (pp) collisions(8,9), but the enhanced production of multi-strange particles has not been reported so far. Here we present the first observation of strangeness enhancement in high-multiplicity proton-proton collisions. We find that the integrated yields of strange and multi-strange particles, relative to pions, increases significantly with the event charged-particle multiplicity. The measurements are in remarkable agreement with the p-Pb collision results(10,11), indicating that the phenomenon is related to the final system created in the collision. In high-multiplicity events strangeness production reaches values similar to those observed in Pb-Pb collisions, where a QGP is formed.Peer reviewe

Production of He-4 and (4) in Pb-Pb collisions at root(NN)-N-S=2.76 TeV at the LHC

Results on the production of He-4 and (4) nuclei in Pb-Pb collisions at root(NN)-N-S = 2.76 TeV in the rapidity range vertical bar y vertical bar <1, using the ALICE detector, are presented in this paper. The rapidity densities corresponding to 0-10% central events are found to be dN/dy4(He) = (0.8 +/- 0.4 (stat) +/- 0.3 (syst)) x 10(-6) and dN/dy4 = (1.1 +/- 0.4 (stat) +/- 0.2 (syst)) x 10(-6), respectively. This is in agreement with the statistical thermal model expectation assuming the same chemical freeze-out temperature (T-chem = 156 MeV) as for light hadrons. The measured ratio of (4)/He-4 is 1.4 +/- 0.8 (stat) +/- 0.5 (syst). (C) 2018 Published by Elsevier B.V.Peer reviewe

Production of muons from heavy-flavour hadron decays in p-Pb collisions at root s(NN)=5.02 TeV

The production of muons from heavy-flavour hadron decays in p-Pb collisions at root s(NN) = 5.02 TeV was studied for 2 <p(T) <16 GeV/c with the ALICE detector at the CERN LHC. The measurement was performed at forward (p-going direction) and backward (Pb-going direction) rapidity, in the ranges of rapidity in the centre-of-mass system (cms) 2.03 <y(cms) <3.53 and -4.46 <y(cms) <-2.96, respectively. The production cross sections and nuclear modification factors are presented as a function of transverse momentum (P-T). At forward rapidity, the nuclear modification factor is compatible with unity while at backward rapidity, in the interval 2.5 <p(T) <3.5 GeV/c, it is above unity by more than 2 sigma. The ratio of the forward -to -backward production cross sections is also measured in the overlapping interval 2.96 <|y(cms)| <3.53 and is smaller than unity by 3.7 sigma in 2.5 <p(T) <3.5 GeV/c. The data are described by model calculations including cold nuclear matter effects. (C) 2017 The Author(s). Published by Elsevier B.V.Peer reviewe

Centrality dependence of high-p(T) D-meson suppression in Pb-Pb collisions at root s(NN) = 2.7 6 TeV (vol 2015, 2015)

This is an addendum to the article JHEP 11 (2015) 205 [1]. The figures 3 (right), 4 (right) and 5 are updated with published results on non-prompt J/psi-meson production from the CMS collaboration [2]