@inproceedings{7ea35f9e82314d7187c62806ec9e44c8,
title = "Detection and Classification of Moving Vehicle Based on YOLOv3 Model",
abstract = "The development of computer vision and artificial intelligence has advanced in recent years. Object detection system based on deep learning have been extensively investigeted. In this paper, we proposed deep learning based method that will be used to detect and classify moving vehicles in complex traffic flows. The YOLO (You Only Look Once)-v3 network will be implemented to handle this case. This paper consist of saveral stages. In the first stage we collect vehicles dataset on one-way street in the form of a video. The second stage is converting video into frames and labelling frames. The third stage is to feed the labeled frames as input in training this model. The last stage is training and testing the model to detect and classify moving vehicles. The experimental was completed and the proposed method can classify moving vehicles using recorded data video appropriately. The results showed that 0.9648 average accuracies of moderate traffic condition and 0.9256 average accuracies of crowded traffic condition.",
author = "Ridho Sholehurrohman and Budi Setiyono",
note = "Publisher Copyright: {\textcopyright} 2023 American Institute of Physics Inc.. All rights reserved.; 3rd International Conference on Science, Mathematics, Environment, and Education: Flexibility in Research and Innovation on Science, Mathematics, Environment, and Education for Sustainable Development, ICoSMEE 2021 ; Conference date: 27-07-2021 Through 28-07-2021",
year = "2023",
month = jan,
day = "27",
doi = "10.1063/5.0105877",
language = "English",
series = "AIP Conference Proceedings",
publisher = "American Institute of Physics Inc.",
editor = "Indriyanti, {Nurma Yunita} and Sari, {Meida Wulan}",
booktitle = "3rd International Conference on Science, Mathematics, Environment, and Education",
}