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MM4Drone : A Multi-Spectral Image and mmWave Radar Approach for Identifying Mosquito Breeding Grounds via Aerial Drones

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  • معلومة اضافية
    • بيانات النشر:
      Uppsala universitet, Avdelningen för datorteknik
      Uppsala universitet, Datorteknik
      University of Colombo, Sri Lanka
      KTH, Sweden
      RISE
      Cham
    • الموضوع:
      2022
    • Collection:
      Uppsala University: Publications (DiVA)
    • نبذة مختصرة :
      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.
    • File Description:
      application/pdf
    • Relation:
      Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering (LNICST), 1867-8211; 488; Pervasive Computing Technologies for Healthcare : 16th EAI International Conference, PervasiveHealth 2022, Thessaloniki, Greece, December 12-14, 2022, Proceedings, p. 412-426
    • الرقم المعرف:
      10.1007/978-3-031-34586-9_27
    • الدخول الالكتروني :
      http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-498865
      https://doi.org/10.1007/978-3-031-34586-9_27
    • Rights:
      info:eu-repo/semantics/openAccess
    • الرقم المعرف:
      edsbas.3BE861BA