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Impact of Rainfall on the Detection Performance of Non-Contact Safety Sensors for UAVs/UGVs.
Sumi, Yasushi; Kim, Bong Keun; Ogure, Takuya; Kodama, Masato; Sakai, Naoki; Kobayashi, Masami.
Afiliação
  • Sumi Y; National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba 305-8568, Japan.
  • Kim BK; National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba 305-8568, Japan.
  • Ogure T; National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba 305-8568, Japan.
  • Kodama M; National Institute of Advanced Industrial Science and Technology (AIST), Tsukuba 305-8568, Japan.
  • Sakai N; Altech Corporation, Yokohama 220-6218, Japan.
  • Kobayashi M; National Research Institute for Earth Science and Disaster Resilience (NIED), Tsukuba 305-0006, Japan.
Sensors (Basel) ; 24(9)2024 Apr 24.
Article em En | MEDLINE | ID: mdl-38732818
ABSTRACT
This study comprehensively investigates how rain and drizzle affect the object-detection performance of non-contact safety sensors, which are essential for the operation of unmanned aerial vehicles and ground vehicles in adverse weather conditions. In contrast to conventional sensor-performance evaluation based on the amount of precipitation, this paper proposes spatial transmittance and particle density as more appropriate metrics for rain environments. Through detailed experiments conducted under a variety of precipitation conditions, it is shown that sensor performance is significantly affected by the density of small raindrops rather than the total amount of precipitation. This finding challenges traditional sensor-evaluation metrics in rainfall environments and suggests a paradigm shift toward the use of spatial transmittance as a universal metric for evaluating sensor performance in rain, drizzle, and potentially other adverse weather scenarios.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Ano de publicação: 2024 Tipo de documento: Article