@inproceedings{1dcdb5823db4420bac8b54c7fe4e1b02,
title = "Towards Real-Time Vehicle Detection on Edge Devices with Nvidia Jetson TX2",
abstract = "With the development of deep convolutional networks, significant advances in object detection task have been achieved. However, for applications in autonomous vehicles, it is necessary to have an efficient object detector that can process rapidly while maintaining high accuracy. This study presents our implementation and performance evaluation of two object detectors EfficientDet-Lite and Yolov3-tiny on Nvidia Jetson TX2 mobile embedded platform. Our experimental results on the KITTI dataset demonstrate that it is possible to achieve real-time and highly accurate object detection on edge devices with constrained resources.",
keywords = "edge device, object detection, real-time, TensorRT",
author = "Nguyen, \{Huy Hung\} and Tran, \{Duong Nguyen Ngoc\} and Jeon, \{Jae Wook\}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020 ; Conference date: 01-11-2020 Through 03-11-2020",
year = "2020",
month = nov,
day = "1",
doi = "10.1109/ICCE-Asia49877.2020.9277463",
language = "English",
series = "2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "2020 IEEE International Conference on Consumer Electronics - Asia, ICCE-Asia 2020",
}