UAVs to detect dangerous ''butterfly'' landmines

Low-cost unmanned aerial vehicles can collect large remotely sensed data sets to detect dangerous "butterfly" landmines in remote regions of post-conflict countries.

Director of the Geophysics and Remote Sensing Laboratory Timothy S. de Smet and Assistant Professor of Energy Geophysics Alex Nikulin from Binghamton University, have developed a method that allows highly accurate detection of "butterfly" landmines from commercial drones. The researchers used mounted infrared cameras to remotely map the dynamic thermal conditions of the surface and recorded unique thermal signatures associated with the plastic casings of the mines. A thermal infrared camera measures the amount of infrared radiation emitted by an object. Physical properties of materials such as specific heat capacity, thermal conductivity, composition, size, shape, density, and porosity affect the thermal behavior of objects so that varied materials heat and cool at different rates. During an early-morning experiment, they found that the mines heated up at a much-greater rate than surrounding rocks, and they were able to identify the mines by their shape and apparent thermal signature. Results indicate that this methodology holds considerable potential to rapidly identify the presence of surface plastic MECs (Military munitions and Explosives of Concern) during early-morning hours, when these devices become thermal anomalies relative to surrounding geology.

The results demonstrate that the proposed methodology can be successfully applied in relatively low-energy temperature environments with gradual temperature fluctuations. ''We show that the PFM-1 (butterfly mine) heats more rapidly than the background and maintains a significant separation in temperature for an interval of several hours in the early morning. We expect that the temporal detection window would significantly expand in environments with large daily temperature swings, specifically high-altitude areas with limited vegetation and soil cover'', the researchers state.

The authors express their opinion about future projects. ''Future research on the detection of the PFM-1 and other similar surficial plastic mines, such as the American BLU-43, will consider a greater suite of environmental variables (diurnal temperature variation, altitude, host geology, UAV flight altitude, and mine orientation) at larger scale controlled test sites. Because the PFM-1 mines and KSF-1 dispensers have distinct thermal signatures and characteristic shapes, supervised machine learning algorithms can be trained to quickly automate detection and classification over large areas''.

Journal Reference: Timothy S. de Smet, Alex Nikulin. Catching "butterflies" in the morning: A new methodology for rapid detection of aerially deployed plastic land mines from UAVs. The Leading Edge, 2018; 37 (5): 367 DOI: 10.1190/tle37050367.1