Deep Learning RetinaNet based Car Detection for Smart Transportation Network

• Main Authors: IRMA AMELIA DEWI, LISA KRISTIANA, ARSYAD RAMADHAN DARLIS, REZA FADILAH DWIPUTRA
• Format: Article
• Language: Indonesian
• Published: Teknik Elektro Institut Teknologi Nasional Bandung 2019-09-01
• Series: Jurnal Elkomika
• Subjects: object detection; retinanet; resnet101; stn; vlc; i2v
• Online Access: https://ejurnal.itenas.ac.id/index.php/elkomika/article/view/3014
• ISSN: 2338-8323; 2459-9638

ABSTRAK Deteksi 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   ABSTRACT Object 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