Article Per Year (5 Year)
p-Index From 2021 - 2026
1.383
P-Index
This Author published in this journals
All Journal Jurnal Teknik Elektro IPTEK The Journal of Engineering Jurnal Teknologi Informasi dan Ilmu Komputer Program Kreativitas Mahasiswa - Penelitian EMITTER International Journal of Engineering Technology INOVTEK POLBENG INOVTEK Polbeng - Seri Informatika Scientific Journal of Mechanical Engineering Kinematika Indonesian Journal of Electrical Engineering and Computer Science Journal of Computer, Electronic, and Telecommunication (COMPLETE) Jurnal Rekayasa elektrika The Indonesian Journal of Computer Science
Indra Adji Sulistijono
Politeknik Elektronika Negeri Surabaya
Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering Industrial & Manufacturing Engineering Materials Science & Nanotechnology Mechanical Engineering

Published : 21 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 21 Documents
Search

 1   2   3 
Automatic Detection of Wrecked Airplanes from UAV Images Risnumawan, Anhar; Perdana, Muhammad Ilham; Alif Habib Hidayatulloh; A. Khoirul Rizal; Indra Adji Sulistijono; Achmad Basuki; Rokhmat Febrianto
EMITTER International Journal of Engineering Technology Vol 7 No 2 (2019)
Publisher : Politeknik Elektronika Negeri Surabaya (PENS)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.24003/emitter.v7i2.424

Abstract

Searching the accident site of a missing airplane is the primary step taken by the search and rescue team before rescuing the victims. However, due to the vast exploration area, lack of technology, no access road, and rough terrain make the search process nontrivial and thus causing much delay in handling the victims. Therefore, this paper aims to develop an automatic wrecked airplane detection system using visual information taken from aerial images such as from a camera. A new deep network is proposed to distinguish robustly the wrecked airplane that has high pose, scale, color variation, and high deformable object. The network leverages the last layers to capture more abstract and semantics information for robust wrecked airplane detection. The network is intertwined by adding more extra layers connected at the end of the layers. To reduce missing detection which is crucial for wrecked airplane detection, an image is then composed into five patches going feed-forwarded to the net in a convolutional manner. Experiments show very well that the proposed method successfully reaches AP=91.87%, and we believe it could bring many benefits for the search and rescue team for accelerating the searching of wrecked airplanes and thus reducing the number of victims.
Co-Authors A. Khoirul Rizal A.A. Ketut Agung Cahyawan W Achmad Basuki Achmad Basuki Achmad Basuki Achmad Jazidie Agus Indra Gunawan Ahmad Fauzi Firmansyah Ali Husein Alasiry Ali Ridho Barakbah Alif Habib Hidayatulloh Ananda Verdi S Andy Yuniawan Aris Sandi Aziz, Adam Shidqul Bagus Muliawan, Nobby Dedid Cahya Happyanto Denny Hanurawan Eko Henfri B Endah Suryawati N Endah Suryawati Ningrum Eny Kusumawati Ermita Dwi Rachmawati Febrianto, Rokhmat Handy Wicaksono Mitsuji Sampei Muhammad Rois Natasya GO, Ardelia Nobby Bagus Muliawan Perdana, Muhammad Ilham Prabancoro Adhi Catur Widagdo Prihastono Prihastono Renita Chulafaurrosyda Risnumawan, Anhar Riyanto Sigit Rizana Fauzi, Rizana Rodik Wahyu Indrawan Rokhmat Febrianto Rusdhianto Effendi Safriudin Rifandi Salfikar, Inzar Siswoyo, Agus Siswoyo, Agus Son Kuswadi Son Kuswadi Tamara, Muhamad Nasyir Zainal Arief Zainal Arief