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Open AccessArticle

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

1
Department of Geological Sciences and Environmental Studies, Binghamton University, 4400 Vestal Pkwy E, Binghamton, NY 13902, USA
2
Department of Computer Science, Binghamton University, 4400 Vestal Pkwy E, Binghamton, NY 13902, USA
*
Author to whom correspondence should be addressed.
Remote Sens. 2020, 12(5), 859; https://doi.org/10.3390/rs12050859
Received: 29 January 2020 / Revised: 22 February 2020 / Accepted: 1 March 2020 / Published: 6 March 2020
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. View Full-Text
Keywords: landmines; UXO; UAV; CNN; neural networks landmines; UXO; UAV; CNN; neural networks
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MDPI and ACS Style

Baur, J.; Steinberg, G.; Nikulin, A.; Chiu, K.; de Smet, T.S. Applying Deep Learning to Automate UAV-Based Detection of Scatterable Landmines. Remote Sens. 2020, 12, 859.

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