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A novel method for detecting UAVs using parallel neural networks with re-inference

Fulltext:


Authors:

Hubert Stepien , Martin Bilger , Håkan Forsberg, Billy Lindgren , Johan Hjorth

Publication Type:

Conference/Workshop Paper

Venue:

33rd Congress of the International Council of the Aeronautical Sciences


Abstract

In this paper, we present a novel method for detecting UAVs using diverse parallel neural networks with re-inference. The parallel networks are of type Convolutional Neural Networks (CNNs). We first set up a low threshold (2 respectively 20%) for each of the individual networks to detect a flying object. If all networks detect a flying object in the same area of a video frame with some overlap, we zoom into that area and redo the object detection and classification (re-inference step). To ensure correctness and reliability of the results from several parallel CNNs, we introduce total confidence Tc as a measurement. We also introduce the intersection over union for multiple parallel networks, IoUAll, and use that as threshold for calculating a reliable Tc. The results show great improvements regarding accurate detection of flying drones, reduced mispredictions of other objects as drones, and fast response time when drones disappear from the scene.

Bibtex

@inproceedings{Stepien6490,
author = {Hubert Stepien and Martin Bilger and H{\aa}kan Forsberg and Billy Lindgren and Johan Hjorth},
title = {A novel method for detecting UAVs using parallel neural networks with re-inference},
month = {September},
year = {2022},
booktitle = {33rd Congress of the International Council of the Aeronautical Sciences},
url = {http://www.es.mdh.se/publications/6490-}
}