Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
MM4Drone: A Multi-spectral Image and mmWave Radar Approach for Identifying Mosquito Breeding Grounds via Aerial Drones
University of Colombo, Sri Lanka.
University of Colombo, Sri Lanka.
University of Colombo, Sri Lanka.
University of Colombo, Sri Lanka.
Show others and affiliations
2023 (English)In: Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST, Springer Science and Business Media Deutschland GmbH , 2023, Vol. 488, p. 412-426Conference paper, Published paper (Refereed)
Abstract [en]

Mosquitoes spread disases such as Dengue and Zika that affect a significant portion of the world population. One approach to hamper the spread of the disases is to identify the mosquitoes’ breeding places. Recent studies use drones to detect breeding sites, due to their low cost and flexibility. In this paper, we investigate the applicability of drone-based multi-spectral imagery and mmWave radios to discover breeding habitats. Our approach is based on the detection of water bodies. We introduce our Faster R-CNN-MSWD, an extended version of the Faster R-CNN object detection network, which can be used to identify water retention areas in both urban and rural settings using multi-spectral images. We also show promising results for estimating extreme shallow water depth using drone-based multi-spectral images. Further, we present an approach to detect water with mmWave radios from drones. Finally, we emphasize the importance of fusing the data of the two sensors and outline future research directions. 

Place, publisher, year, edition, pages
Springer Science and Business Media Deutschland GmbH , 2023. Vol. 488, p. 412-426
Keywords [en]
Aerial Drones, mmWave Radar, Multispectral Imagery, Object Detection, Aircraft detection, Antennas, Drones, Millimeter waves, Object recognition, Radar imaging, Tracking radar, Aerial drone, Breeding grounds, Low-costs, Mm waves, Mosquito breeding, Multispectral images, Objects detection, World population
National Category
Computer and Information Sciences
Identifiers
URN: urn:nbn:se:ri:diva-65700DOI: 10.1007/978-3-031-34586-9_27Scopus ID: 2-s2.0-85164166242ISBN: 9783031345852 (electronic)OAI: oai:DiVA.org:ri-65700DiVA, id: diva2:1787173
Conference
16th EAI International Conference on Pervasive Computing Technologies for Healthcare, PH 2022. Thessaloniki, Greece. 12 December 2022 through 14 December 2022
Note

This work has been partly funded by Digital Futures and the Swedish Research Council (Grant 2018-05024).

Available from: 2023-08-11 Created: 2023-08-11 Last updated: 2023-08-11Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textScopus

Authority records

Mottola, LucaVoigt, Thiemo

Search in DiVA

By author/editor
Mottola, LucaVoigt, Thiemo
By organisation
Data Science
Computer and Information Sciences

Search outside of DiVA

GoogleGoogle Scholar

doi
isbn
urn-nbn

Altmetric score

doi
isbn
urn-nbn
Total: 13 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf