Monitoring
the crew of a ship can be performed by combining sensors
and artificial intelligence methods to process sensing data. In this
study, we developed a deep learning (DL)-assisted minimalist structure
triboelectric smart mat system for obtaining abundant crew information
without the privacy concerns of taking video. The smart mat system
is fabricated using a conductive sponge with different filling rates
and a fluorinated ethylene propylene membrane. The proposed dual-channel
measurement method improves the stability of the generated signal.
Comprehensive crew and cargo monitoring, including personnel and status
identification, as well as positioning and counting functions are
realized by the DL-assisted triboelectric smart mat system according
to the analysis of instant sensory data. Real-time monitoring of crews
through fixed and mobile devices improves the ability and efficiency
of handling emergencies. The smart mat system provides privacy concerns
and an effective way to build ship Internet of Things and ensure personnel
safety.
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