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http://dx.doi.org/10.7848/ksgpc.2021.39.6.381

Analysis of Building Object Detection Based on the YOLO Neural Network Using UAV Images  

Kim, June Seok (Department of Spatial Information Engineering, Namseoul University)
Hong, Il Young (Department of Spatial Information Engineering, Namseoul University)
Publication Information
Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography / v.39, no.6, 2021 , pp. 381-392 More about this Journal
Abstract
In this study, we perform deep learning-based object detection analysis on eight types of buildings defined by the digital map topography standard code, leveraging images taken with UAV (Unmanned Aerial Vehicle). Image labeling was done for 509 images taken by UAVs and the YOLO (You Only Look Once) v5 model was applied to proceed with learning and inference. For experiments and analysis, data were analyzed by applying an open source-based analysis platform and algorithm, and as a result of the analysis, building objects were detected with a prediction probability of 88% to 98%. In addition, the learning method and model construction method necessary for the high accuracy of building object detection in the process of constructing and repetitive learning of training data were analyzed, and a method of applying the learned model to other images was sought. Through this study, a model in which high-efficiency deep neural networks and spatial information data are fused will be proposed, and the fusion of spatial information data and deep learning technology will provide a lot of help in improving the efficiency, analysis and prediction of spatial information data construction in the future.
Keywords
Object Detection; Building Object Detection; YOLO; UAV; Spatial Analysis;
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