Use of landmines as a weapon of unconventional warfare rapidly increased in armed conflicts of the last century and some estimates suggest that least 100 million remain in place across post-conflict nations. Among munitions and explosives of concern (MECs), aerially deployed plastic anti-personnel mines are particularly challenging in terms of their detection and subsequent disposal. Detection and identification of MECs largely relies on the geophysical principles of magnetometry and electromagnetic-induction (EMI), which makes non-magnetic plastic MECs particularly difficult to detect and extremely dangerous to clear. In a recent study we demonstrated the potential of time-lapse thermal-imaging technology to detect unique thermal signatures associated with plastic MECs. Here, we present the results of a series of field trials demonstrating the viability of low-cost unmanned aerial vehicles (UAVs) equipped with infrared cameras to detect and identify the most notorious plastic landmines—the Soviet-era PFM-1 aerially deployed antipersonnel mine. We present results of an experiment simulating analysis of a full-scale ballistic PFM-1 minefield and demonstrate our ability to accurately detect and identify all elements associated with this type of deployment. We report significantly reduced time and equipment costs associated with the use of a UAV-mounted infrared system and anticipate its utility to both the scientific and non-governmental organization (NGO) community.
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.
Recent advances in autonomous unmanned aerial vehicle (UAV) technology, along with successful efforts to miniaturize total field magnetometers, offer a unique opportunity to test low-cost UAV-mounted systems for wide-area high-resolution magnetic surveys. Modern UAV platforms capable of flying at low altitudes and collecting dense aerial surveys, coupled with sensitive and compact instruments, allow identification of anthropogenic targets previously identifiable only in ground magnetometer surveys. We present results of a proof-of-concept study focused on developing and field testing a UAV-based magnetometer system to detect and identify abandoned and unmarked oil and gas wells in an area of historical hydrocarbon exploration and development in New York state. Our results indicate that magnetic anomalies associated with metal casing of vertical wells are pronounced considerably above background levels both at the surface and up to 50 m above-ground elevation. We determine that a detection altitude of 40 m is optimal to avoid any canopy interference while recording magnetic data at the highest signal-to-noise ratio. This methodology makes rapid detection and identification of unmarked wells possible and, in turn, allows for future sustainable development of these areas.
It is estimated that there are at least 100 million military munitions and explosives of concern (MEC) devices in the world of various size, shape, and composition. Millions of these are surface plastic land mines with low-pressure detonation regimes, such as the mass-produced Soviet PFM-1. These aerially deployed land mines are concentrated primarily in postconflict developing countries such as Afghanistan and represent a continued humanitarian threat, while also thwarting economic and social development in impacted regions. Identification of this particular type of MEC category poses a significant geophysical challenge, as these mines contain almost no metal (nonferrous aluminum). As a result, standard MEC detection and remediation methodologies based on geophysical principles of magnetometry and electromagnetic induction prove largely ineffective and possibly dangerous. Low-cost unmanned aerial vehicles can rapidly collect large remotely sensed data sets with no risk to MEC technicians. We present results of an experiment focused on remotely assessing thermal signatures of plastic land mines relative to background geology to show that this type of analysis permits rapid detection of randomly dispersed plastic MECs with a high degree of accuracy and low associated costs.
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