@inproceedings{17d7d01bc04a4e9083f5777b8e9c21b1,
title = "A Lightweight YOLOv2 Object Detector Using a Dilated Convolution",
abstract = "In recent years, object detection approaches such as you-only-look-once (YOLO) have been getting a special attention owing to the emerging trend of autonomous driving systems. However, memory and computation complexity are usually known as the bottlenecks in implementing a YOLOv2 in hardware design. This study proposes a simple yet effective variant of YOLOv2 by using dilated convolution to reduce its complexity. Experimental results show that the proposed method achieves up to 36\% of memory access reduction and 9\% of computation reduction on a network with a negligible performance loss of 1.75\% on the well-known VOC dataset.",
keywords = "dilated convolution, YOLOv2",
author = "Nguyen, \{Tuan Nghia\} and Nguyen, \{Xuan Truong\} and Hyun Kim and Lee, \{Hyuk Jae\}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 34th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2019 ; Conference date: 23-06-2019 Through 26-06-2019",
year = "2019",
month = jun,
doi = "10.1109/ITC-CSCC.2019.8793337",
language = "English",
series = "34th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
booktitle = "34th International Technical Conference on Circuits/Systems, Computers and Communications, ITC-CSCC 2019",
}