Article Per Year (5 Year)
p-Index From 2019 - 2024
0.23
P-Index
This Author published in this journals
All Journal ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika
DWIPUTRA, REZA FADILAH
Unknown Affiliation
Electrical & Electronics Engineering Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Deep Learning RetinaNet based Car Detection for Smart Transportation Network DEWI, IRMA AMELIA; KRISTIANA, LISA; DARLIS, ARSYAD RAMADHAN; DWIPUTRA, REZA FADILAH
ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Vol 7, No 3 (2019): ELKOMIKA
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v7i3.570

Abstract

ABSTRAKDeteksi objek yang merupakan salah satu bagian utama dari sistem Smart Transportasion Network (STN) diajukan pada penelitian ini. Penelitian ini menggunakan salah satu model STN yaitu Infrastructure-to-Vehicle (I2V), dimana sistem ini bekerja dengan mendeteksi kendaraan mobil menggunakan model arsitektur RetinaNet dengan backbone Resnet101 dan FPN (Feature Pyramid Network), kemudian hasil deteksi mentrigger VLC transmitter yang terpasang di lampu penerangan jalan mengirimkan sinyal informasi menuju VLC receiver yang dipasang di mobil. Pada tahap proses training, jumlah dataset mobil yang digunakan adalah sekitar 1600 image dan 400 validation image serta pengulangan proses sebanyak 100 epoch. Berdasarkan 50 kali pengujian pada image test, diperoleh nilai precision mencapai 86%, nilai recall mencapai 85% dan f1-score mencapai 84%.Kata kunci: Object detection, RetinaNet, Resnet101, STN, VLC, I2V ABSTRACTObject detection is one of the main part in Smart Transportation Network (STN) system proposed in this research. This research used one of the STN models, namely Infrastructure-to-Vehicle (I2V), a system works by detecting car using RetinaNet architecture model with ResNet 101 and FPN (Feature Pyramid Network) as backbone, then the detection result triggers VLC transmitter set up on the street lighting to transmit information signal to the VLC receiver which set up in the car. At the training process stage, the number of car datasets is approximately 1600 images, 400 validation images and repetition of processes about 100 epochs. Based on the 50 times testing process on a image test, it is obtained 86% of a precision value, by reaching 85% of recall value, and 84% of f1-score. Keywords: Object detection, RetinaNet, Resnet101, STN, VLC, I2V
Co-Authors ARSYAD RAMADHAN DARLIS DEWI, IRMA AMELIA KRISTIANA, LISA