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

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.