15 November 2017 Detecting of foreign object debris on airfield pavement using convolution neural network
Author Affiliations +
Proceedings Volume 10605, LIDAR Imaging Detection and Target Recognition 2017; 1060536 (2017) https://doi.org/10.1117/12.2295282
Event: LIDAR Imaging Detection and Target Recognition 2017, 2017, Changchun, China
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.
© (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Xiaoguang Cao, Xiaoguang Cao, Yufeng Gu, Yufeng Gu, Xiangzhi Bai, Xiangzhi Bai, } "Detecting of foreign object debris on airfield pavement using convolution neural network", Proc. SPIE 10605, LIDAR Imaging Detection and Target Recognition 2017, 1060536 (15 November 2017); doi: 10.1117/12.2295282; https://doi.org/10.1117/12.2295282
PROCEEDINGS
7 PAGES


SHARE
Back to Top