Classification of Unmanned Aerial Vehicle and Bird Images Using Deep Transfer Learning Methods

Ahmet Özdemir, İlker Ali OZKAN
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Abstract

The increasing accessibility and affordability of unmanned aerial vehicles (UAVs), commonly known as drones, have led to the emergence of malicious users. In precaution to this perceived threat, various anti-UAV systems are being developed, including electro-optical systems utilizing cameras. It is possible to detect UAVs from images using various machine learning methods. However, the similarity between UAVs and birds poses a challenge, as birds can be mistakenly identified as UAVs, leading to false alarms in a security system. In order to avoid this problem, this study provided the classification of birds and unmanned aerial vehicles over images using deep learning methods. In this study, a data set consisting of 400 birds and 428 UAV images was used. The data were divided into 70% for training, 30% for testing and validation purposes. Three different deep learning models, based on DenseNet, VGG16, and VGG19 architectures, were trained using transfer learning techniques, and their performances were compared. Experimental results on the test data showed an accuracy of 94.64% with the DenseNet model, 89.67% with the VGG16 model, and 90.67% with the VGG19 model.
基于深度迁移学习方法的无人机和鸟类图像分类
无人驾驶飞行器(uav)的可及性和可负担性越来越高,这导致了恶意用户的出现。为了预防这种感知到的威胁,各种反无人机系统正在发展,包括利用摄像头的光电系统。使用各种机器学习方法可以从图像中检测无人机。然而,无人机和鸟类之间的相似性带来了挑战,因为鸟类可能被错误地识别为无人机,从而导致安全系统中的错误警报。为了避免这一问题,本研究使用深度学习方法对图像上的鸟类和无人机进行分类。在本研究中,使用了由400只鸟和428架无人机图像组成的数据集。数据分为70%用于培训,30%用于测试和验证。使用迁移学习技术训练了基于DenseNet、VGG16和VGG19架构的三种不同的深度学习模型,并比较了它们的性能。在测试数据上的实验结果表明,DenseNet模型的准确率为94.64%,VGG16模型的准确率为89.67%,VGG19模型的准确率为90.67%。
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