基于深度学习和各种数据增强技术的邻国军用飞机分类研究

Chanwoo Lee, Hajun Hwang, Hyeok-Jun Kwon, Seung-Kweon Baik, Wooju Kim
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引用次数: 0

摘要

对突然出现在防空识别区的外国飞机进行分析需要大量的成本和时间。本研究旨在开发一个预先训练的模型,该模型可以根据网络上可用的飞机照片识别邻近的军用飞机,并提出一个模型,可以根据盟国拍摄的航空照片确定对应的飞机。该模型的优点是通过提出预训练模型来减少模型分类所需的成本和时间,并通过边缘检测图像的数据增强、裁剪、翻转等来提高分类器的性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Study on the Classification of Military Airplanes in Neighboring Countries Using Deep Learning and Various Data Augmentation Techniques
The analysis of foreign aircraft appearing suddenly in air defense identification zones requires a lot of cost and time. This study aims to develop a pre-trained model that can identify neighboring military aircraft based on aircraft photographs available on the web and present a model that can determine which aircraft corresponds to based on aerial photographs taken by allies. The advantages of this model are to reduce the cost and time required for model classification by proposing a pre-trained model and to improve the performance of the classifier by data augmentation of edge-detected images, cropping, flipping and so on.
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