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http://dx.doi.org/10.5762/KAIS.2019.20.11.305

A Study on the Applicability of Deep Learning Algorithm for Detection and Resolving of Occlusion Area  

Bae, Kyoung-Ho (Research Institute, Shinhan Aerial Survey CO.,LTD)
Park, Hong-Gi (Department of Civil & Environmental Engineering, Gachon University)
Publication Information
Journal of the Korea Academia-Industrial cooperation Society / v.20, no.11, 2019 , pp. 305-313 More about this Journal
Abstract
Recently, spatial information is being constructed actively based on the images obtained by drones. Because occlusion areas occur due to buildings as well as many obstacles, such as trees, pedestrians, and banners in the urban areas, an efficient way to resolve the problem is necessary. Instead of the traditional way, which replaces the occlusion area with other images obtained at different positions, various models based on deep learning were examined and compared. A comparison of a type of feature descriptor, HOG, to the machine learning-based SVM, deep learning-based DNN, CNN, and RNN showed that the CNN is used broadly to detect and classify objects. Until now, many studies have focused on the development and application of models so that it is impossible to select an optimal model. On the other hand, the upgrade of a deep learning-based detection and classification technique is expected because many researchers have attempted to upgrade the accuracy of the model as well as reduce the computation time. In that case, the procedures for generating spatial information will be changed to detect the occlusion area and replace it with simulated images automatically, and the efficiency of time, cost, and workforce will also be improved.
Keywords
Occlusion Area; Deep Learning; Drone; Image; Detection;
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