Comparison of Drone Classification Accuracy for CNN Models Using UWB Radar

We have previously proposed a method for recognizing various drones from images of ultra-wideband (UWB) radar range profile by using a convolutional neural network (CNN). In general, the classification accuracy for CNN tends to improve as the layer becomes deeper. Therefore, in this paper, we examin...

Full description

Saved in:
Bibliographic Details
Published in2022 Asia-Pacific Microwave Conference (APMC) pp. 907 - 909
Main Authors Kurosaki, Masashi, Kawaguchi, Daiki, Ogawa, Kenshi, Nakamura, Ryohei, Hadama, Hisaya
Format Conference Proceeding
LanguageEnglish
Published The Institute of Electronics Information and Communication Engineers (IEICE) of Japan 29.11.2022
Subjects
Biological system modeling
Birds
convolutional neural network
deep learning
drone
image recognition
Microwave measurement
Neural networks
Particle measurements
Phantoms
radar
Target recognition
ultra-wideband
Online AccessGet full text

Cover

More Information
Summary:We have previously proposed a method for recognizing various drones from images of ultra-wideband (UWB) radar range profile by using a convolutional neural network (CNN). In general, the classification accuracy for CNN tends to improve as the layer becomes deeper. Therefore, in this paper, we examine the classification accuracy of drones for four types of CNN models (AlexNet, GoogleNet, ResNet-50, and ResNet-101) with different depths. We have investigated the classification performances for five types of drones (Matrice 600, 3DR Solo, Phantom 3, Mavic pro, and Bebop drone) and a radio-controlled flapping bird (Bionic bird) by each of the CNN models. As a result, we have confirmed that all the CNN models can recognize each target with an average accuracy of 90% or more. Furthermore, we have also confirmed that ResNet-50 consisting of 50 layers can recognize all the targets with equal or better accuracy than that by AlexNet which consists of 8 layers.
DOI:10.23919/APMC55665.2022.10000022