Refereed article in conference proceedings (A4)

Deep Convolutional Neural Network-based Fusion of RGB and IR Images in Marine Environment




List of Authors: Fahimeh Farahnakian, Jussi Poikonen, Markus Laurinen, Jukka Heikkonen

Conference name: Intelligent Transportation Systems Conference

Publication year: 2019

Book title *: 2019 IEEE Intelligent Transportation Systems Conference (ITSC)

Number of pages: 6

ISBN: 978-1-5386-7025-5

eISBN: 978-1-5386-7024-8

ISSN: 2153-0009

DOI: http://dx.doi.org/10.1109/ITSC.2019.8917332

Self-archived copy’s web address: https://research.utu.fi/converis/portal/detail/Publication/44437898


Abstract

Abstract— Designing accurate and automatic multi-target
detection is a challenging problem for autonomous vehicles.
To address this problem, we propose a late multi-modal fusion
framework in this paper. The framework provides complimentary information from RGB and thermal infrared cameras in
order to improve the detection performance. For this purpose,
it first employs RetinaNet as a dense simple deep model for each
input image separately to extract possible candidate proposals
which likely contain the targets of interest. Then, all proposals
are generated by concatenating the obtained proposals from
two modalities. Finally, redundant proposals are removed by
Non-Maximum Suppression (NMS). We evaluate the proposed
framework on a real marine dataset which is collected by a
sensor system onboard a vessel in the Finnish archipelago.
This system is used for developing autonomous vessels, and
records data in a range of operation and climatic conditions.
The experimental results show that our late fusion framework
can get more detection accuracy compared with middle fusion
and uni-modal frameworks.


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Last updated on 2022-07-04 at 17:37