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Proceedings Paper

Detecting of foreign object debris on airfield pavement using convolution neural network
Author(s): Xiaoguang Cao; Yufeng Gu; Xiangzhi Bai
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Paper 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.

Paper Details

Date Published: 15 November 2017
PDF: 7 pages
Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 1060536 (15 November 2017); doi: 10.1117/12.2295282
Show Author Affiliations
Xiaoguang Cao, Beihang Univ. (China)
Yufeng Gu, Beihang Univ. (China)
Xiangzhi Bai, Beihang Univ. (China)


Published in SPIE Proceedings Vol. 10605:
LIDAR Imaging Detection and Target Recognition 2017
Yueguang Lv; Weimin Bao; Weibiao Chen; Zelin Shi; Jianzhong Su; Jindong Fei; Wei Gong; Shensheng Han; Weiqi Jin; Jian Yang, Editor(s)

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