@inproceedings{e90431cb3be3460da1429ca654353163,
title = "Pvalite CLN: Lightweight Object Detection with Classfication and Localization Network",
abstract = "We developed a 2D convolution object detection network called CLN layer to let a network with a small amount of parameters and have a better detection result as well. Compared to YOLOv2 detector and SSD with lightweight Pvalite network, the proposed Classification and Localization Network (CLN) layer has better accuracy and higher recall on the object which is located on the border of image. Additionally, we revise the original open source to make this architecture more platform portable than other architectures. The proposed system is implemented on the embedded platforms with the performance of 640x480@10 fps under nVidia Jetson TX-2 and 480x320@5fps under Renesas R-car H3.",
keywords = "Classification, Localization, Object Detection",
author = "Guo Jiun-In and Tsai Chi-Chi and Tseng Ching-Kan",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 32nd IEEE International System on Chip Conference, SOCC 2019 ; Conference date: 03-09-2019 Through 06-09-2019",
year = "2019",
month = sep,
doi = "10.1109/SOCC46988.2019.1570561207",
language = "English",
series = "International System on Chip Conference",
publisher = "IEEE Computer Society",
pages = "118--121",
editor = "Danella Zhao and Arindam Basu and Magdy Bayoumi and Hwee, {Gwee Bah} and Ge Tong and Ramalingam Sridhar",
booktitle = "Proceedings - 32nd IEEE International System on Chip Conference, SOCC 2019",
address = "United States",
}