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All Journal Indonesian Journal of Geography Jurnal Penelitian Fisika dan Aplikasinya (JPFA) Bulletin of Electrical Engineering and Informatics Journal of Mathematical and Fundamental Sciences PINUS: Jurnal Penelitian Inovasi Pembelajaran Darmabakti Cendekia: Journal of Community Service and Engagements Journal of Electronics, Electromedical Engineering, and Medical Informatics JPPIPA (Jurnal Penelitian Pendidikan IPA) Jurnal Abdi: Media Pengabdian Kepada Masyarakat Kirana: Jurnal Komunikasi dan Penyuluhan Pertanian Kubis
Moh Yasin
Department Of Physic, Faculty Of Science And Technology, Universitas Airlangga
Agriculture, Biological Sciences & Forestry Humanities Astronomy Chemistry Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mathematics Physics Public Health Social Sciences Other

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Journal : Bulletin of Electrical Engineering and Informatics

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Real-time military person detection and classification system using deep metric learning with electrostatic loss Suprayitno, Suprayitno; Fauzi, Willy Achmat; Ain, Khusnul; Yasin, Moh.
Bulletin of Electrical Engineering and Informatics Vol 12, No 1: February 2023
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v12i1.4284

Abstract

This study addressed a system design to detect the presence of military personnel (combatants or non-combatants) and civilians in real-time using the convolutional neural network (CNN) and a new loss function called electrostatic loss. The basis of the proposed electrostatic loss is the triplet loss algorithm. Triplet loss’ input is a triplet image consisting of an anchor image (xa), a positive image (xp), and a negative image (xn). In triplet loss, xn will be moved away from xa but not far from both xa and xp. It is possible to create clusters where the intra-class distance becomes large and does not determine the magnitude and direction of xn displacement. As a result, the convergence condition is more difficult to achieve. Meanwhile, in electrostatic loss, some of these problems are solved by approaching the electrostatic force on charged particles as described in Coulomb's law. With the inception ResNet-v2 128-dimensional vectors network within electrostatic loss, the system was able to produce accuracy values of 0.994681, mean average precision (mAP) of 0.994385, R-precision of 0.992908, adjusted mutual information (AMI) of 0.964917, and normalized mutual information (NMI) of 0.965031.
Co-Authors Ari Septianingtyas Purwandhini Ari Setiawan Budiyanto, Moh. Dwi Gustiono Riban Fauzi, Willy Achmat Febriyanti, Tarisa Habibah, Aisyah Nurma Herlan Darmawan Herri Trilaksana Imam Suyanto irwan meilano Irzaman, Irzaman Khusnul Ain Maria Evita Mitra Djamal Moh. Budiyanto Mohammad Budiyanto Mohd Syafik Shaharin Mundzir Abdullah Nina Siti Aminah Noriah Bidin Nur Hidayah pudjiastutik, endang wahyu Pujiyanto Pujiyanto Putra, Heriansyah Retna Apsari Samian Suhariningsih Sulaiman Wadi Harun Supadi Supadi Suprayitno Suprayitno Suryanto, Wiwit Wahyu Srigutomo Wahyudi Wahyudi Yunardi, Riky Tri Yusef Abdul Alwafi