Open AccessCombining of Transfer Learning with Faster-RCNN For Aedes Aegyti Larvae Detection
Author(s) -
Muhammad Fuad,
Fauzal Naim Zohedi,
M Ghani,
Rozaimi Ghazali,
Tarmizi Ahmad Izzuddin
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.b1145.0782s619
Subject(s) - dengue fever , aedes aegypti , statistic , transfer of learning , alarm , artificial intelligence , constant false alarm rate , computer science , machine learning , larva , aedes , environmental science , environmental health , statistics , biology , engineering , medicine , mathematics , ecology , virology , aerospace engineering
The dengue epidemiology episode has become one of the global phenomena especially the rain forest countries including Malaysia. Environmental management, the used of chemical and biological environment are control strategies that has been proposed and practiced by World Health Organization. However, based on statistic al of dengue cases, there is still no concrete solution in curbing this problem especially at non-accessible places. This paper proposed a study on detection Aedes Aegypti larvae in water storage tank by combining transfer learning with Faster-RCNN. The purpose of the study is to acquire train and validation losses along with detection performance metrics. The experimental results disclose that the probability detection has scored 97.01% while false alarm has scored 5.97%. Those significant value has depicted that the trained model has high detection accuracies.