Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이은주 | - |
dc.date.accessioned | 2019-03-06T06:11:25Z | - |
dc.date.available | 2019-03-06T06:11:25Z | - |
dc.date.issued | 2016-09 | - |
dc.identifier.citation | INFRARED PHYSICS & TECHNOLOGY, v. 78, Page. 223-232 | en_US |
dc.identifier.issn | 1350-4495 | - |
dc.identifier.issn | 1879-0275 | - |
dc.identifier.uri | https://www.sciencedirect.com/science/article/pii/S1350449516303966 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/100529 | - |
dc.description.abstract | Accurate advance detection of the sinkholes that are occurring more frequently now is an important way of preventing human fatalities and property damage. Unlike naturally occurring sinkholes, human induced ones in urban areas are typically due to groundwater disturbances and leaks of water and sewage caused by large-scale construction. Although many sinkhole detection methods have been developed, it is still difficult to predict sinkholes that occur in depth areas. In addition, conventional methods are inappropriate for scanning a large area because of their high cost. Therefore, this paper uses a drone combined with a thermal far-infrared (FIR) camera to detect potential sinkholes over a large area based on computer vision and pattern classification techniques. To make a standard dataset, we dug eight holes of depths 0.5-2 m in increments of 0.5 m and with a maximum width of 1 m. We filmed these using the drone-based FIR camera at a height of 50 m. We first detect candidate regions by analysing cold spots in the thermal images based on the fact that a sinkhole typically has a lower thermal energy than its background. Then, these regions are classified into sinkhole and non-sinkhole classes using a pattern classifier. In this study, we ensemble the classification results based on a light convolutional neural network (CNN) and those based on a Boosted Random Forest (BRF) with handcrafted features. We apply the proposed ensemble method successfully to sinkhole data for various sizes and depths in different environments, and prove that the CNN ensemble and the BRF one with handcrafted features are better at detecting sinkholes than other classifiers or standalone CNN. (C) 2016 Elsevier B.V. All rights reserved. | en_US |
dc.description.sponsorship | This work was supported by academic support joint technology development of the Small and Medium Business Administration (South Korea) No. B20140108. | en_US |
dc.language.iso | en_US | en_US |
dc.publisher | ELSEVIER SCIENCE BV | en_US |
dc.subject | Sinkhole detection | en_US |
dc.subject | Drone | en_US |
dc.subject | Far-infrared camera | en_US |
dc.subject | Convolution neural network | en_US |
dc.subject | Boosted random forest | en_US |
dc.title | Early Sinkhole Detection using a Drone-based Thermal Camera and Image Processing | en_US |
dc.type | Article | en_US |
dc.identifier.doi | 10.1016/j.infrared.2016.08.009 | - |
dc.relation.journal | INFRARED PHYSICS & TECHNOLOGY | - |
dc.contributor.googleauthor | Lee, Eun Ju | - |
dc.contributor.googleauthor | Shin, Sang Young | - |
dc.contributor.googleauthor | Ko, Byoung Chul | - |
dc.contributor.googleauthor | Chang, Chunho | - |
dc.relation.code | 2016001240 | - |
dc.sector.campus | E | - |
dc.sector.daehak | COLLEGE OF ENGINEERING SCIENCES[E] | - |
dc.sector.department | DIVISION OF SMART CONVERGENCE ENGINEERING | - |
dc.identifier.pid | eunju19 | - |
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