Mosquitoes spread diseases such as Dengue and Zika that affect a significant portion of the world population. One approach to hamper the spread of the diseases 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.

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

Mottola L.;
2023-01-01

Abstract

Mosquitoes spread diseases such as Dengue and Zika that affect a significant portion of the world population. One approach to hamper the spread of the diseases 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.
2023
Proceedings of the EAI International Conference on Pervasive Computing Technologies for Healthcare (PervasiveHealth)
Aerial drones
mmWave radar
Multispectral imagery
Object detection
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11311/1260793
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