@inproceedings{9423cb5e9a334f12981f67f3b064d2ca,
title = "Detecting of foreign object debris on airfield pavement using convolution neural network",
abstract = "It is of great practical significance to detect foreign object debris (FOD) timely and accurately on the airfield pavement, because the FOD is a fatal threaten for runway safety in airport. In this paper, a new FOD detection framework based on Single Shot MultiBox Detector (SSD) is proposed. Two strategies include making the detection network lighter and using dilated convolution, which are proposed to better solve the FOD detection problem. The advantages mainly include: (i) the network structure becomes lighter to speed up detection task and enhance detection accuracy; (ii) dilated convolution is applied in network structure to handle smaller FOD. Thus, we get a faster and more accurate detection system.",
keywords = "Convolutional Neural Network, Dilated Convolution, Foreign Object Debris",
author = "Xiaoguang Cao and Yufeng Gu and Xiangzhi Bai",
note = "Publisher Copyright: {\textcopyright} 2017 SPIE.; LIDAR Imaging Detection and Target Recognition 2017 ; Conference date: 23-07-2017 Through 25-07-2017",
year = "2017",
doi = "10.1117/12.2295282",
language = "英语",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Weimin Bao and Yueguang Lv and Daren Lv",
booktitle = "LIDAR Imaging Detection and Target Recognition 2017",
address = "美国",
}