Detektovanje podvodnih metalnih objekata pomoću veštačke inteligencije i zaštita od korozije predmeta od mekog čelika korišćenih u podvodnoj studiji - studija slučaja - Deo A - detektovanje podvodnih metalnih objekata pomoću veštačke inteligencije

Autori

  • Thankappan Sasilatha AMET University, Department of EEE, Chennai, India Autor
  • Doss Hebciba Mary Suvakeen Amala St. Antony's College of Arts and Sciences for Women Thamaraipady, Department of Chemistry, Tamil Nadu, India Autor
  • Rajendran Susai Santhammal St. Antony's College of Arts and Sciences for Women Thamaraipady, Department of Chemistry, Tamil Nadu, India + Pondicherry University, Puducherry, India Autor
  • Časlav Lačnjevac University of Belgrade, Faculty of Agriculture, Serbia Autor
  • Gurmeet Singh Pondicherry University, Puducherry, India Autor

DOI:

https://doi.org/10.5937/zasmat2201005R

Ključne reči:

ulazni podaci o slici, Konvoluciona neuronska mreža (C ), klasterisanje sa srednjim vrednostima, Studija polarizacije TURBID skupa podataka, Spektar impedanse naizmenične struje, morska voda

Apstrakt

Zbog važnosti odvodnog istraživanja u razvoju i korišćenju dubokomorskih resursa, odvodni autonomni rad je sve važniji kako bi se izbeglo opasno dubokomorsko okruženje od visokim pritiskom. Za podvodni autonomni rad, inteligentni računarski vid je najvažnija tehnologija. U podvodnom okruženju, slabo osvetljenje i nekvalitetno poboljšanje slike, kao postupak prethodne obrade, neophodni su za podvodni vid. U ovom radu predstavljeno je otkrivanje podvodnih metalnih objekata zasnovanog na veštačkoj inteligenciji pomoću ulaznih podataka o slici koristeći nekoliko koraka a poboljšanje performansi modela. U ovom eksperimentu koristi se TURBID skup podataka od 100 slika za proveru performansi. Takođe, upoređuje se rezultat performansi prema datim ulaznim slikama na različitim nivoima validacije. U prvom slučaju, ulazna slika se prethodno obrađuje i te slike se daju u KFCM-segmentaciji. Segmentirane slike se daju DVT ekstrakciji da izdvoje karakteristike iz tih slika. I na kraju, Convolution Neural Netvork (CNN) se koristi za klasifikaciiju slika radi otkrivanja objekata. Takođe, ovaj predloženi model dostigao je tačnost klasifikacije od 98,83%. Ova metoda je veoma pogodna za robotsko otkrivanje objekata u morskim dubinama. Metalni delovi mašina brodova ili aviona mogu potonuti u morsku vodu.Mogu doći do korozije u kontaktu sa morskom vodom koja sadrži 3,5% natrijum hlorida. Ovo je najčešće odgovorno za korozivnu prirodu morske vode. Roboti napravljeni od materijala kao što je meki čelik, takođe, mogu pretrpeti koroziju kada dođu u kontakt sa morskom vodom, dok je u toku pretraga. Ako se nanese premaz boje, on će kontrolisati koroziju ovih predloženih materijala. Zbog toga se ovaj posao preduzima. Meki čelik premazan je azijskom zaštitnom crvenom bojom. Otpornost na koroziju blagog 3,5% rastvora natrijum hlorida meri se pre nanošenja i nakon nanošenja elektrohemijskim studijama, kao što su polarizacione studije i spektri impedanse naizmenične struje. Efikasnost sprečavanja korozije koju crvena boja nudi mekom čeliku u 3,5% natrijum hloridu je 99,98%.

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2022-03-15

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