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http://dx.doi.org/10.20910/JASE.2022.16.5.70

Database Generation and Management System for Small-pixelized Airborne Target Recognition  

Lee, Hoseop (Department of mechanical and Aerospace Engineering of Cheongju University)
Shin, Heemin (School of Electrical Engineering, KAIST)
Shim, David Hyunchul (Unmanned Aircraft System Research Division, Korea Aerospace Research Institute)
Cho, Sungwook (Department of Aeromechnical Engineering of Cheongju University)
Publication Information
Journal of Aerospace System Engineering / v.16, no.5, 2022 , pp. 70-77 More about this Journal
Abstract
This paper proposes database generation and management system for small-pixelized airborne target recognition. The proposed system has five main features: 1) image extraction from in-flight test video frames, 2) automatic image archiving, 3) image data labeling and Meta data annotation, 4) virtual image data generation based on color channel convert conversion and seamless cloning and 5) HOG/LBP-based tiny-pixelized target augmented image data. The proposed framework is Python-based PyQt5 and has an interface that includes OpenCV. Using video files collected from flight tests, an image dataset for airborne target recognition on generates by using the proposed system and system input.
Keywords
Counter Drone; Image Processing; Deep Learning; Dataset; Database; Image Augmentation;
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