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

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.