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 techni...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-03-01
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| Series: | Remote Sensing |
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| Online Access: | https://www.mdpi.com/2072-4292/12/5/859 |
| _version_ | 1798031572411088896 |
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| author | Jasper Baur Gabriel Steinberg Alex Nikulin Kenneth Chiu Timothy S. de Smet |
| author_facet | Jasper Baur Gabriel Steinberg Alex Nikulin Kenneth Chiu Timothy S. de Smet |
| author_sort | Jasper Baur |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-11T19:59:38Z |
| format | Article |
| id | doaj.art-e0f1c6f29e634cd282f4f782356cc4a3 |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-04-11T19:59:38Z |
| publishDate | 2020-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-e0f1c6f29e634cd282f4f782356cc4a32022-12-22T04:05:44ZengMDPI AGRemote Sensing2072-42922020-03-0112585910.3390/rs12050859rs12050859Applying Deep Learning to Automate UAV-Based Detection of Scatterable LandminesJasper Baur0Gabriel Steinberg1Alex Nikulin2Kenneth Chiu3Timothy S. de Smet4Department of Geological Sciences and Environmental Studies, Binghamton University, 4400 Vestal Pkwy E, Binghamton, NY 13902, USADepartment of Computer Science, Binghamton University, 4400 Vestal Pkwy E, Binghamton, NY 13902, USADepartment of Geological Sciences and Environmental Studies, Binghamton University, 4400 Vestal Pkwy E, Binghamton, NY 13902, USADepartment of Computer Science, Binghamton University, 4400 Vestal Pkwy E, Binghamton, NY 13902, USADepartment of Geological Sciences and Environmental Studies, Binghamton University, 4400 Vestal Pkwy E, Binghamton, NY 13902, USARecent 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.https://www.mdpi.com/2072-4292/12/5/859landminesuxouavcnnneural networks |
| spellingShingle | Jasper Baur Gabriel Steinberg Alex Nikulin Kenneth Chiu Timothy S. de Smet Applying Deep Learning to Automate UAV-Based Detection of Scatterable Landmines Remote Sensing landmines uxo uav cnn neural networks |
| title | Applying Deep Learning to Automate UAV-Based Detection of Scatterable Landmines |
| title_full | Applying Deep Learning to Automate UAV-Based Detection of Scatterable Landmines |
| title_fullStr | Applying Deep Learning to Automate UAV-Based Detection of Scatterable Landmines |
| title_full_unstemmed | Applying Deep Learning to Automate UAV-Based Detection of Scatterable Landmines |
| title_short | Applying Deep Learning to Automate UAV-Based Detection of Scatterable Landmines |
| title_sort | applying deep learning to automate uav based detection of scatterable landmines |
| topic | landmines uxo uav cnn neural networks |
| url | https://www.mdpi.com/2072-4292/12/5/859 |
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