Abstract
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.
Highlights
Landmine clearance protocols adapted by demining NGOs and various state demining services largely rely on the geophysical principles of electromagnetic induction (EMI), which have demonstrated high effectiveness in the detection of large metallic landmines and buried unexploded ordnance (UXO) [2]
This study focuses on unmanned aerial vehicle (UAV) based multispectral and thermal infrared sensing to train a robust Convolutional Neural Network (CNN) to automate detection of the PFM-1 landmines to dramatically decrease the time, cost, and increase accuracy associated with current methods
To push the accuracy of the Faster R-CNN past 71.5% for fully withheld datasets, and past 99.3% for partially withheld datasets, several actions will be taken in future research efforts
Summary
The expanding rift between landmine placement and clearance is driven by a technological disconnect between modern landmine technology and the traditional demining toolkit. Landmine clearance protocols adapted by demining NGOs and various state demining services largely rely on the geophysical principles of electromagnetic induction (EMI), which have demonstrated high effectiveness in the detection of large metallic landmines and buried unexploded ordnance (UXO) [2]. EMI-based surveys produce high numbers of false flags in the presence of metallic debris against mines with reduced metal content [3]. Many modern landmines are designed to avoid detection by EMI methods; they are smaller, have a reduced metal content, and may contain little or no metal shrapnel elements [4]
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